@conference {zeng2025ML4SEISMICbde,
	title = {Background-Conditioned Diffusion Inversion for Seismic Velocity Models},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Accurate seismic imaging and velocity estimation are important for subsurface characterization, yet conventional full-waveform inversion remains computationally expensive and highly sensitive to initial velocity models. To address these challenges, we propose a simulation-based inference framework using conditional elucidated diffusion models for posterior velocity-model sampling. Our approach integrates both horizontal and vertical subsurface-offset common-image gathers to capture a wider range of reflector geometries, from gently dipping to steeply inclined layers. We further condition the model on the background-velocity field to improve generalization across diverse geological settings. Evaluations on the SEAM dataset, which includes complex salt geometries, demonstrate that this conditioning substantially improves performance, increasing SSIM from 0.717 to 0.733 and reducing RMSE from 0.381 km/s to 0.274 km/s. Uncertainty analysis further shows enhanced calibration, lowering uncertainty calibration error from 6.68 km/s to 3.91 km/s. These results confirm that our diffusion-based simulation framework enables robust seismic inversion with reliable uncertainty quantification.},
	keywords = {Bayesian inference, CIG, deep learning, diffusion models, FWI, Imaging, Inverse problems, ML4SEISMIC, MVA, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/zeng2025ML4SEISMICbde},
	author = {Yunlin Zeng and Huseyin Tuna Erdinc and Rafael Orozco and Felix J. Herrmann}
}
@conference {deng2025ML4SEISMICbag,
	title = {Bridging the Acoustic{\textendash}Elastic Gap in Seismic Inversion via Robust Summary Statistics},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Simulation-based Bayesian inference (SBI) enables uncertainty quantification in seismic inversion but typically assumes that simulated and observed data follow the same physics. In reality, this assumption does not hold. The Earth is elastic, yet large-scale simulations are often acoustic due to the prohibitive cost of elastic modeling. This acoustic{\textendash}elastic mismatch introduces model misspecification, leading to biased posterior estimates and reduced interpretability. We propose a robust conditional generative framework that mitigates this mismatch by learning a shared representation between acoustic and elastic domains. The approach integrates a robust summary network within a conditional normalizing flow, trained primarily on acoustic simulations and guided by limited elastic supervision. It enables elastic-consistent posterior inference from inexpensive acoustic simulations, balancing computational efficiency with physical realism. This framework offers a scalable path toward uncertainty-aware subsurface characterization under model misspecification, bridging the gap between fast acoustic modeling and physically accurate elastic data.},
	keywords = {Amortized Variational Inference, attention, Bayesian inference, CIG, deep learning, FWI, generative model, Imaging, Inverse problems, ML4SEISMIC, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/deng2025ML4SEISMICbag},
	author = {Zijun Deng and Abhinav Prakash Gahlot and Shiqin Zeng and Felix J. Herrmann}
}
@conference {li2025ML4SEISMICdsc,
	title = {Digital Shadow for CO2-based Enhanced Oil Recovery (CO2-EOR)},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {We present a Digital Shadow for CO2-based Enhanced Oil Recovery (CO2-EOR), a data-driven surrogate model that predicts reservoir dynamics by conditioning on time-lapse seismic observations. Built upon conditional normalizing flows, the Digital Shadow learns the posterior distribution of subsurface states{\textemdash}CO2 and oil saturation{\textemdash}conditioned on seismic data generated through coupled multiphase flow and rock-physics modeling. The framework integrates high-fidelity simulations with JutulDarcy.jl, nonlinear seismic forward modeling and imaging with JUDI.jl, and nonlinear rock-physics mappings to relate saturation changes to changes in the acoustic properties. Trained on 128 stochastic realizations derived from a North Sea permeability distribution, the model achieves accurate posterior samples for the saturation fields with high structural similarity to the ground truth. This work demonstrates how integrating differentiable physics with generative modeling enables seismic-informed monitoring of CO2 plume evolution and oil recovery, laying the foundation for a Digital Twin of CO2-based EOR operations.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, EOR, experimental design, FWI, GCS, Imaging, Inverse problems, ML4SEISMIC, permeability, reservoir simulation, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/li2025ML4SEISMICdsc},
	author = {Haoyun Li and Abhinav Prakash Gahlot and Olav Moyner and Felix J. Herrmann}
}
@conference {park2025ML4SEISMICftn,
	title = {Fisher-Informed Training of Neural Operators for Reliable PDE Inversion},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Neural operators have shown strong performance for PDE-solution learning; however, their effectiveness in PDE-constrained optimization, such as inversion, remains limited, in part because inaccurately learned derivatives can mislead gradient-based updates. Standard training typically optimizes only the forward least-squares fit, so during iterative inversion the optimization variable can drift outside the training distribution, further degrading quality of derivative information and inversion performance. To address this limitation, we propose a training algorithm designed for inversion: the surrogate is trained not only to predict forward outputs but also to learn observation-relevant gradients, which leads to Fisher-information directed parameter updates. We evaluate forward and gradient accuracy, as well as the inversion trajectory, on problems governed by PDEs{\textemdash}Darcy flow and laminar (incompressible) Navier{\textendash}Stokes{\textemdash}which are relevant to porous-media fluid flow and energy-storage applications. Our results suggest that training for gradient fidelity with forward accuracy unlocks a pathway to reliable, efficient, neural operator{\textendash}based inversion.},
	keywords = {deep learning, derivatve-informed, Fisher-information, FNO, GCS, Inverse problems, ML4SEISMIC, permeability, reduced-order modeling, reservoir simulation, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/park2025ML4SEISMICftn},
	author = {Jeongjin Park and Grant Bruer and Huseyin Tuna Erdinc and Richard Rex Arockiasamy and Nisha Chandramoorthy and Felix J. Herrmann}
}
@conference {gahlot2025ML4SEISMICmrm,
	title = {Mitigating Rock-Physics Model Misspecification in Digital Shadows via Amortized Bayesian Inference},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Digital shadows for subsurface monitoring rely on simulation-based inference to map seismic observations to evolving reservoir states. However, when the simulations are generated using simplified or different rock physics models than those governing the observed data, model misspecification leads to biased posterior estimates and degraded uncertainty quantification. We address this challenge through Bayesian frameworks including rock-physics marginalization and a context-aware amortized inference using conditional normalizing flows. The framework learns a mapping from seismic responses to pressure and saturation while accounting for variability across different randomly drawn models for the rock-physics. To enhance sensitivity to fluid and stress effects, we also incorporate pressure-dependent rock physics into the seismic forward model and perform inference in the Radon domain of Common Image Gathers (CIGs) instead of conventional RTM images. Because fluids exhibit distinct angle-dependent signatures in AVA responses, Radon-transformed CIGs are expected to provide improved separation between pressure and saturation effects, leading to more accurate and physically consistent state estimates. Thus, it enables an uncertainty-aware, physics-informed digital shadows under rock-physics model mismatch.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, GCS, Imaging, Inverse problems, ML4SEISMIC, permeability, reservoir simulation, rock physics, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/gahlot2025ML4SEISMICmrm},
	author = {Abhinav Prakash Gahlot and Ipsita Bhar and Felix J. Herrmann}
}
@conference {cirakman2025ML4SEISMICpss,
	title = {Multiscale Wavelet Score-based Posterior Approximations for Seismic Inversion},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {We present a cascaded conditional wavelet score{\textendash}based posterior surrogate (CWSGM) for seismic inversion that generates samples of subsurface velocity fields conditioned on physics-derived summaries (e.g., RTM images). Through multi-scale wavelet-based whitening, we specifically address ill-conditioning of the velocity model{\textquoteright}s covariance (due to its fractal-like 1/f power spectrum), whose long range correlations hamper training neural networks to approximate the score function. This strategy improves the conditioning of the score-learning problem and accelerates both training and sampling. Experiments on the North Sea BG Model demonstrate that CWSGM accurately captures long-range geological correlations while reducing GPU memory usage by approximately 50\% and sampling time by 73\% compared to a single-scale vanilla score model. Overall, our approach enables efficient multi-scale generation of subsurface velocities at lower computational cost and provides a foundation for exploring alternative multiscale transforms that are more optimally aligned with seismic observations.},
	keywords = {Bayesian inference, deep learning, generative model, Imaging, Inverse problems, ML4SEISMIC, power-scaling, RTM, score, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/cirakman2025ML4SEISMICpss},
	author = {Ege Cirakman and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@conference {erdinc2025ML4SEISMICpbi,
	title = {Power-scaled Bayesian inference for velocity-model estimation},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Score-based generative models have emerged as powerful tools for conditional generation in Bayesian inference. While promising, a common criticism of these methods is their reliance on strong, structured priors, which can dominate the inference process and limit the network{\textquoteright}s dependence on observed data. To address this issue, we propose a modification to the generative sampling algorithm that incorporates power scaling of both priors and likelihoods. Our approach enables flexible control over the relative influence of the prior and likelihood without requiring retraining for different scaling configurations. We demonstrate this in the context of synthesizing seismic velocity models conditioned on imaged seismic data. By sampling from intermediate power posteriors, our method naturally supports sensitivity analysis and allows us to assess how varying prior and likelihood weights determines the behavior of the posterior. Through a comprehensive set of experiments, we examine the impact of the power parameters in three scenarios: scaling only the prior, scaling only the likelihood, and scaling both simultaneously. The results show that increasing the likelihood power up to a certain threshold improves the fidelity of posterior samples to the conditioning data, while reducing prior power increases structural diversity in the generated models. Furthermore, moderate likelihood scaling leads to reduced shot data residuals, demonstrating its effectiveness for posterior refinement.},
	keywords = {Bayesian inference, deep learning, generative model, Imaging, Inverse problems, ML4SEISMIC, power-scaling, RTM, score, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/erdinc2025ML4SEISMICpbi},
	author = {Huseyin Tuna Erdinc and Yunlin Zeng and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {henriksson2025ML4SEISMICpss,
	title = {Predicting Subsurface CO2 Saturation using Late Fusion of Seismic and Streaming Well Data},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Determining subsurface flow of CO2 in porous rock formations is a challenging task especially when it involves intergration of multimodal data that are collected at disparate timescales. In this work, we investigate how data fushion can be used to integrate multimodal data consisting on infrequently collected active-source seismic surveys and continuous streaming data of saturation measurements collected at monitoring wells. For the purpose, we propose a data fusion framework based on late fusion, which combines seismic images, computed from seismic surveys, with time-series data collected at the wells. By integrating the spatial information from seismic data with high-resolution but sparce spatial-temporal patterns from wells, we aim to better approximate the complex CO2 flow patterns.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, GCS, Imaging, Inverse problems, late fusion, ML4SEISMIC, permeability, reservoir simulation, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/henriksson2025ML4SEISMICpss},
	author = {Victor Henriksson and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {zeng2025ML4SEISMICrrs,
	title = {Reconstructing reservoir states from multimodal data via score-based generative models},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {8},
	abstract = {This study develops a score-based generative framework for reservoir simulation to reconstruct spatially varying permeability and saturation distributions in saline aquifers from sparse observations at two wells combined with time-lapse seismic data. We learn joint distributions of permeability, saturation, and pressure from high-fidelity multiphase-flow simulations, conditioned on observed imaged time-lapse seismic data. The framework provides a unified, any-subset inference interface that flexibly incorporates well logs and physics-based constraints to enforce mass balance and physically plausible behavior. An ablation without pressure on training yields markedly lower structural similarity within physically meaningful measurement ranges, underscoring the pressure{\textquoteright}s importance for accurate reconstruction of the saturation. Our approach generalizes across varying geological settings and highlights the value of multimodal data fusion for practical reservoir management.},
	keywords = {deep learning, diffusion-pde models, Generative models, history matching, ML4SEISMIC, reservoir simulation, score, SLIM, sparse observations, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/zeng2025ML4SEISMICrrs},
	author = {Shiqin Zeng and Abhinav Prakash Gahlot and Haoyun Li and Felix J. Herrmann}
}
@conference {li2025ML4SEISMICric,
	title = {Risk-Aware Injectivity Control for Energy Storage},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {We introduce a digital-twin{\textendash}driven framework for real-time injectivity control that maximizes injected CO2 while enforcing reservoir safety. The forward model is a multiphase flow simulator evolving reservoir states under injectivity controls q; time-lapse seismic acts as the observation operator to reduce uncertainty of the the state (reservoir{\textquoteright}s saturation/pressure). Risk is handled via two complementary metrics: the Probability of Failure (POF) for exceedance likelihood and the less conservative CVaR (superquantile) for tail severity. For the optimization, we employ smooth surrogates{\textemdash}a logistic exceedance rate for POF and the Rockafellar{\textendash}Uryasev formulation for CVaR{\textemdash}enabling gradient-based control with constraints. Within a finite-horizon Model Predictive Control (MPC) loop, we optimize a trajectory per posterior sample and aggregate the ensemble of optimal rates into an empirical (KDE-kernel) Cumulative Density Function (CDF) with binomial confidence bands; the final controlled injectivity q is chosen as the largest rate whose upper confidence bound satisfies the target risk level. Case studies demonstrate that POF-constrained optimization is more conservative than CVaR-based optimization, In addition, sensitivity of the policy to the CVaR tail level, α, is reduced compared to POF{\textquoteright}s parameter sensitivity, allowing for adaptive, uncertainty-aware control that balances injectivity and fracture risk. The proposed framework also unifies time-lapse seismic-informed inference, risk metrics, and MPC into a practical approach for safe high-throughput underground energy storage.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, EOR, experimental design, FWI, GCS, Imaging, Inverse problems, ML4SEISMIC, permeability, reservoir simulation, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/li2025ML4SEISMICric},
	author = {Haoyun Li and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {erdinc2025ML4SEISMICspm,
	title = {SAGE - Subsurface modeling with AI-driven Geostatistical Extraction and evaluation on North Sea Data},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Traditional machine learning based approaches often depend on large, high-quality and complete datasets of 2D Earth propety models, which can be difficult to obtain in subsurface applications. In this study, we leverage score-based generative models to synthesize high-fidelity geophysical properties (velocity, density, etc.) trained on a combination of sparse but tied well logs and seismic lines without relying on densely sampled velocity models and adress the sampling-density differences by enabling coherent integration of these complementary sources of information. With experiments on multiple synthetic datasets from subsurface models featuring diverse geological structures (e.g., faults, salt bodies), we demonstrate that our approach can accurately capture long-range geological correlations and align well with unseen ground-truth properties. Finally, we highlight the potential of our method to generalize to field data by conducting fine-tuning on a curated dataset derived from real well-log and seismic data currated from the UK Data Repository for the North Sea. This study represents an important step toward the development of foundational models for inverting physical properties and provides inputs for applications such as full-waveform inference (WISE/ASPIRE), supervised learning algorithms, and enhanced seismic-based subsurface modeling.},
	keywords = {Bayesian inference, deep learning, generative model, Imaging, Inverse problems, ML4SEISMIC, North-sea data, power-scaling, RTM, score, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/erdinc2025ML4SEISMICspm},
	author = {Huseyin Tuna Erdinc and Ipsita Bhar and Thales Souza and Rafael Orozco and Felix J. Herrmann}
}
@conference {bhar2025ML4SEISMICsdc,
	title = {Seismic Dataset Curation from UK National Data Repository to Validate SAGE and WISE},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {This work presents a curated seismic dataset pipeline developed for data residing in the UK National Data Repository (UK NDR) in support of advanced generative geophysical modeling and machine learning. The workflow integrates checkshots and 3D post-stack seismic data volumes to generate accurate depth-domain seismic sections. Custom interpolation algorithms are used to construct velocity models from checkshot data, which are then used for time-to-depth conversion with [OpendTect]https://dgbes.com/software/opendtect. The converted seismic cubes are subsequently processed in Python using [SegySAK]https://segysak.readthedocs.io/en/latest/, a python package, enabling efficient extraction and visualization of 2D lines. These datasets will be used to further validate the workflow of SAGE (Subsurface foundational model for AI-driven Geostatistical Extraction), which focuses on large-scale representation learning for the geosciences. The trained generative models will be used as input to WISE and ASPIRE that form foundation models for seismic inference designed to characterize the subsurface. This curated dataset pipeline streamlines seismic data preparation, enhances reproducibility, and bridges the gap between conventional geophysical workflows and emerging data-driven inference methods.},
	keywords = {Bayesian inference, generative model, Imaging, ML4SEISMIC, North-sea data, RTM, SLIM, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/bhar2025ML4SEISMICsdc},
	author = {Ipsita Bhar and Huseyin Tuna Erdinc and Thales Souza and Rafael Orozco and Felix J. Herrmann}
}
@presentation {bruer2025GSCSsmc,
	title = {Seismic monitoring of CO2 using ensemble Kalman filtering},
	year = {2025},
	note = {(GSCS, Gatech)},
	month = {02},
	keywords = {CCS, EnKF, fluid flow, GCS, GSCS, Imaging, monitoring, RTM, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Posters/GSCS/2025/bruer2025GSCSsmc/2025GSCS_poster.pdf},
	author = {Grant Bruer and Abhinav Prakash Gahlot and Felix Herrmann and Edmond Chow}
}
@conference {zeng2025ML4SEISMICssn,
	title = {Self-attention summary networks for velocity model building from common-image gathers},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {This study develops a self-attention-based summary network designed to efficiently encode and compress seismic information residing in subsurface-offset Common Image Gathers (CIGs) into low-dimensional latent representations suitable for conditional machine-learning enabled velocity-model building. The proposed network functions as a compact seismic encoder that captures global spatial dependencies through self-attention mechanisms while preserving essential local structural features critical for accurate reconstruction. When integrated within a flow matching generative network, the summary network learns to effectively summarize seismic information from CIGs via a new multi-scale attention mechanisms. This integration enables efficient training and inference, facilitating fast and physically consistent generation of seismic velocity models conditioned on CIGs.},
	keywords = {attention, CIG, deep learning, flow-matching, Generative models, ML4SEISMIC, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/zeng2025ML4SEISMICssn},
	author = {Shiqin Zeng and Abhinav Prakash Gahlot and Yunlin Zeng and Zijun Deng and Felix J. Herrmann}
}
@conference {deng2025ML4SEISMICtfi,
	title = {Time-lapse full-waveform inversion with uncertainty quantification},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Reliable management of underground energy storage systems, such as underground compressed Hydrogene or Air storage, rely on semi-continuous monitoring and rigorous quantification of subsurface uncertainty. Time-lapse seismic imaging provides valuable insights into fluid migration and storage integrity. However, traditional inversion techniques{\textemdash}particularly full-waveform inversion (FWI){\textemdash}are inherently ill-posed and deterministic, limiting their applicability for uncertainty-aware decision-making. The Joint Recovery Method (JRM) improves consistency across multiple surveys by jointly reconstructing shared structures, but does not capture uncertainty. We introduce the Probabilistic Joint Recovery Method (πJRM), which embeds uncertainty estimation within a joint inversion framework. πJRM leverages a shared generative model to infer wave-equation{\textendash}based posterior distributions for each monitoring survey, enabling probabilistic interpretation of time-lapse changes. Synthetic experiments demonstrate that πJRM accurately recovers dynamic reservoir evolution and provides uncertainty-aware reconstructions, offering a principled foundation for safe, efficient management of subsurface energy storage operations.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, GCS, igm, image generative model, Imaging, Inverse problems, jrm pjrm, ML4SEISMIC, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/deng2025ML4SEISMICtfi},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {park2025ML4SEISMICvmb,
	title = {Velocity Model Building with Jacobian-Informed Neural Operators},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	keywords = {deep learning, derivatve-informed, FNO, GCS, Inverse problems, ML4SEISMIC, permeability, reduced-order modeling, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/park2025ML4SEISMICvmb},
	author = {Jeongjin Park and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@presentation {herrmann2024ML4SEISMICdtg,
	title = {A Digital Twin for Geological Carbon Storage with Controlled Injectivity},
	year = {2024},
	month = {11},
	abstract = {We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage (GCS), capable of handling multimodal time-lapse data and controlling CO 2  injectivity to mitigate reservoir fracturing risks. In GCS, DT represents virtual replicas of subsurface systems that incorporate real-time data and advanced generative Artificial Intelligence (genAI) techniques, including neural posterior density estimation via simulation-based inference and sequential Bayesian inference. These methods enable the effective monitoring and control of CO 2  storage projects, addressing challenges such as subsurface complexity, operational optimization, and risk mitigation. By integrating diverse monitoring data, e.g., geophysical well observations and imaged seismic, DT can bridge the gaps between seemingly distinct fields like geophysics and reservoir engineering. In addition, the recent advancements in genAI also facilitate DT with principled uncertainty quantification. Through recursive training and inference, DT utilizes simulated current state samples, e.g., CO 2  saturation, paired with corresponding geophysical field observations to train its neural networks and enable posterior sampling upon receiving new field data. However, it lacks decision-making and control capabilities, which is necessary for full DT functionality. This study aims to demonstrate how DT can inform decision-making processes to prevent risks such as cap rock fracturing during CO 2  storage operations.},
	keywords = {Amortized Variational Inference, augmentation, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, FWI, Imaging, Inverse problems, ML4SEISMIC, rock physics, RTM, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/herrmann2024ML4SEISMICdtg},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@presentation {herrmann2024dt4gcs,
	title = {Digital Twins in the Era of Generative AI: Application to Geological CO2 Storage},
	year = {2024},
	month = {2},
	abstract = {Our industry is experiencing significant changes due to AI and the challenges of the energy transition. While some view these changes as threats, recent advances in AI offer unique opportunities, especially in the context of Digital Twins for subsurface monitoring and control. IBM defines "A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." During this talk, I will explore these concepts and their significance in addressing the challenges of monitoring \& control of geological CO 2  storage projects. This talk also aims to illustrate how Digital Twins can serve as a platform to integrate the seemingly disparate and siloed fields of geophysics and reservoir engineering.},
	keywords = {Bayesian inference, CCS, deep learning, digital twin, Imaging, monitoring, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs.pdf},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {zeng2024ML4SEISMICepu,
	title = {Enhancing Performance with Uncertainty Estimation in Geological Carbon Storage Leakage Detection from Time-Lapse Seismic Data},
	year = {2024},
	month = {11},
	abstract = {Ensuring CO 2  non-leakage is a critical aspect of Geological Carbon Storage (GCS). While previous approaches that develop deep neural networks demonstrate promising automatic leakage detection and potential cost reduction in dataset collection from time-lapse seismic images, they face challenges, such as a limited ability to reduce false alarms in CO 2  leakage instances and a lack of uncertainty analysis in detection results. This paper introduced a framework aimed at enhancing the deep neural network model{\textquoteright}s ability to detect GCS leakage risk through a multi-criteria decision-making (MCDM)-based ensemble algorithm. The proposed method can improve the detection ability of leakage cases while accurately distinguishing them from non-leakage instances. Furthermore, the proposed uncertainty analysis method utilizing Monte Carlo (MC) dropout technique efficiently identifies misclassified non-leakage cases and categorizes them as undetermined for further investigation. This comprehensive approach enhances both the reliability and performance of the model in detecting GCS leakage risks.},
	keywords = {CAM, CCS, classification, explainability, GCS, Imaging, Inverse problems, JRM, ML4SEISMIC, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/zeng2024ML4SEISMICepu},
	author = {Shiqin Zeng and Huseyin Tuna Erdinc and Ziyi Yin and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {rex2024ML4SEISMICfca,
	title = {FNO-charged ASPIRE},
	year = {2024},
	month = {11},
	abstract = {During this talk, we will demonstrate how extended re-migrations{\textemdash}i.e, formation of subsurface-offset Common-Image Gathers (CIGs) for a new velocity model, can be avoided altogether by training Fourier Neural Operators during training of ASPIRE {\textemdash} Amortized posteriors with Summaries that are Physics-based and Iteratively REfined. In this approach, FNOs are trained as surrogates capable of mapping CIGs for one migration-velocity model to the other. The approach is computationally feasible because it uses the same training set as used during ASPIRE. As a result, additional training costs are small and the inference costs are reduced by a factor equal to the number of ASPIRE refinements.},
	keywords = {deep learning, FNO, hierarchical tucker tensor, Inverse problems, kronecker product, ML4SEISMIC, SLIM, two-phase flow},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/rex2024ML4SEISMICfca},
	author = {Richard Rex and Yunlin Zeng and Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@presentation {yin2024GTwise,
	title = {Generative AI for full-waveform variational inference},
	year = {2024},
	month = {1},
	abstract = {We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, FWI, generative AI, GT, inversion, machine learning, Normalizing flows, SLIM, Summary Statistics, Uncertainty quantification, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Lectures/GTseminar/2024/yin2024GTwise},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {zeng2024ML4SEISMICiic,
	title = {Image Impeccable Challenge: An Effective Machine Learning Denoising Method for 3D Seismic Volumes},
	year = {2024},
	month = {11},
	abstract = {Seismic denoising is essential for enhancing the clarity, accuracy, and reliability of seismic data. Traditional seismic denoising methods, while effective for specific types of noise, often rely on well-established mathematical techniques that can be time-consuming, require manual tuning, and struggle with more complex noise patterns. Leveraging the 500 paired synthetic seismic datasets provided by the Think Onward community, we incorporate a 3D U-Net deep learning model with residual blocks and spatial attention to capture both local and global features for the seismic denoising task. During training, we apply the Laplacian operator to preserve edge details, followed by the Structural Similarity Index Measure (SSIM) loss to fine-tune the model, effectively removing concurrent noise and recovering the original seismic information. The resulting individual model achieves an SSIM of 0.99 compared to the ground truth seismic data. Additionally, we implement Langevin dynamics and Equivariant Bootstrapping techniques to estimate uncertainty during the training and inference phases, further improving the robustness of the denoising process.},
	keywords = {CAM, CCS, classification, explainability, GCS, Imaging, Inverse problems, JRM, ML4SEISMIC, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/zeng2024ML4SEISMICiic},
	author = {Shiqin Zeng and Rafael Orozco and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@presentation {orozco2024ML4SEISMICmev,
	title = {Machine-learning enabled velocity-model building with uncertainty quantification},
	year = {2024},
	month = {11},
	abstract = {Accurately characterizing subsurface properties is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional characterization methods such as Full-Waveform Inversion (FWI) represent powerful tools but often struggle with the inherent complexities of the inverse problem, including noise, limited bandwidth and aperture of data, limited azimuth and computational constraints. To address these challenges, we propose a scalable methodology that integrates generative modeling with physics-informed summary statistics, making it suitable for complicated imaging problems potentially including field datasets. Our approach leverages the power of conditional diffusion networks, and methodologically incorporates physics in the form of summary statistics, allowing for the computationally efficient generation of Bayesian posterior samples that offer an useful assessment of uncertainty of the inferred migration-velocity models. To validate our approach, we introduce a battery of tests that measure the quality of the image estimates as well as the quality of the inferred uncertainties. With modern synthetic datasets, we maximally leverage the advantages of using subsurface-offset Common Image Gathers (CIGs) as the conditioning observable. Next, we tackle the challenging SEAM salt model that requires incorporating salt flooding into our approach based on the iterative refinements of ASPIRE {\textemdash} Amortized posteriors with Summaries that are Physics-based and Iteratively REfined.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, diffusion models, FWI, Imaging, Inverse problems, ML4SEISMIC, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/orozco2024ML4SEISMICmev},
	author = {Rafael Orozco and Huseyin Tuna Erdinc and Thales Souza and Yunlin Zeng and Ziyi Yin and Felix J. Herrmann}
}
@presentation {gahlot2024ML4SEISMICosm,
	title = {Optimizing CO2 Storage Monitoring with Enhanced Rock Physics Modeling},
	year = {2024},
	month = {11},
	abstract = {Based on the latest data-assimilation and machine-learning techniques, Digital Twins (DTs) have shown promise for high-fidelity monitoring and control of underground CO2​ storage. While the use of these techniques have important advantages, they do rely on certain assumptions. If these assumptions are not met, the DT{\textquoteright}s neural networks may no longer infer the state of the CO2 plume (pressure/saturation) accurately. By augmenting the forecast ensemble, we address this issue.},
	keywords = {Amortized Variational Inference, augmentation, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, FWI, Imaging, Inverse problems, ML4SEISMIC, rock physics, RTM, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/gahlot2024ML4SEISMICosm},
	author = {Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {park2024ML4SEISMICpbi,
	title = {Physical Bayesian Inference for Two-Phase Flow Problems},
	year = {2024},
	month = {11},
	abstract = {Previous research on surrogate modeling of multiphase flow systems has shown that even models with low generalization error in forward predictions can generate posterior estimates that are out of distribution and physically unrealistic. To address this, we propose a regularization method that leverages the Fisher Information Matrix (FIM) to guide the training process. By integrating the FIM into a differentiable optimization framework, we aim to improve the reliability of surrogate models, such as Fourier Neural Operators (FNO), for both forward predictions and posterior inference. Our experiments on benchmark problems, including the Lorenz-63 system and Navier-Stokes equations, demonstrate that our approach significantly enhances physical consistency throughout time evolution, keeping predictions within the correct spatial distribution. Looking ahead, we plan to extend our framework to more complex applications, such as Geological Carbon Storage, with an emphasis on scaling FIM computations for high-dimensional problems.},
	keywords = {Bayesian inference, deep learning, Fisher Information Matrix, FNO, Inverse problems, ML4SEISMIC, SLIM, two-phase flow, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/park2024ML4SEISMICpbi},
	author = {Jeongjin Park and Huseyin Tuna Erdinc and Haoyun Li and Richard Rex and Nisha Chandramoorthy and Felix J. Herrmann}
}
@presentation {deng2024ML4SEISMICpjr,
	title = {Probabilistic Joint Recovery Method for CO2 plume monitoring},
	year = {2024},
	month = {11},
	abstract = {Accurately predicting fluid flow patterns in Carbon Capture and Storage (CCS) is a challenging task, particularly due to uncertainties in CO 2  plume dynamics and reservoir properties. While previous deterministic methods such as the Joint Recovery Method (JRM) have provided valuable insights, their effectiveness is limited as tools for decision-making since they do not communicate uncertainty. To address this, we propose a Probabilistic Joint Recovery Method (PJRM) that computes the posterior distribution at each monitoring survey while leveraging the shared structure among surveys through a common generative model. By efficiently computing posterior distributions for each monitoring survey, this method aims to provide valuable uncertainty information to decision-makers in CCS projects, augmenting their workflow with principled risk minimization.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, igm, image generative model, Imaging, Inverse problems, JRM, ML4SEISMIC, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/deng2024ML4SEISMICpjr},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {li2024ML4SEISMICrps,
	title = {Reconstructing Permeability and Saturation in Reservoir Simulation Using Diffusion PDE Models},
	year = {2024},
	month = {11},
	abstract = {This study explores the application of a diffusion partial differential equation (PDE) model for reservoir simulation, particularly aimed at reconstructing permeability and saturation fields within a saline aquifer. Focusing on pairs of input permeability and output saturation, the model is trained to capture the underlying dynamics governing fluid flow in porous media. Post-training, the model is capable of inferring or recovering the complete permeability and saturation distributions when provided with limited vertical pixel data of permeability and saturation. This approach offers a novel pathway for enhancing the resolution of subsurface characteristics, contributing to more accurate predictions in reservoir engineering and carbon storage simulations.},
	keywords = {augmentation, Bayesian inference, deep learning, diffusion models, Inverse problems, ML4SEISMIC, SLIM, Summary Statistics, two-phase flow, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/li2024ML4SEISMICrps},
	author = {Haoyun Li and Shiqin Zeng and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {erdinc2024ML4SEISMICsfm,
	title = {SAGE {\textendash} Subsurface foundational model with AI-driven Geostatical Extraction},
	year = {2024},
	month = {11},
	abstract = {In this study, we present a novel approach for synthesizing diverse subsurface velocity models using diffusion-based generative models. Traditional methods often depend on large, high-quality datasets of 2D velocity models, which can be difficult to obtain in subsurface applications. In contrast, our method leverages incomplete well and seismic data to generate high-fidelity velocity samples without requiring fully sampled training datasets.The results demonstrate that the generative model accurately captures long-range geological structures and aligns well with unseen ground-truth velocity models. Furthermore, it is shown that the diversity of generated velocity models can be increased through prior guidance in the training phase, and model uncertainties can be reduced with well conditioning during inference.Experiments conducted with multiple datasets (BG, Synthoseis, and North Sea data) and velocity models featuring various geological structures (e.g., faults, salt bodies) suggest that our approach facilitates realistic subsurface velocity synthesis, providing valuable inputs for full-waveform inversion and enhancing seismic-based subsurface modeling.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, diffusion models, FWI, Imaging, Inverse problems, ML4SEISMIC, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/erdinc2024ML4SEISMICsfm},
	author = {Huseyin Tuna Erdinc and Rafael Orozco and Felix J. Herrmann}
}
@presentation {bruer2024ML4SEISMICsmp,
	title = {Seismic monitoring of CO2 plume dynamics using ensemble Kalman filtering},
	year = {2024},
	month = {11},
	abstract = {Monitoring CO 2  injected and stored in subsurface reservoirs is critical for avoiding failure scenarios and enables real-time optimization of CO 2  injection rates. Sequential Bayesian data assimilation (DA) is a statistical method for combining information over time from multiple sources to estimate a hidden state, such as the spread of the subsurface CO 2  plume. An example of scalable and efficient sequential Bayesian DA is the ensemble Kalman filter (EnKF). We improve upon existing DA literature in the seismic-CO 2  monitoring domain by applying this scalable DA algorithm to a high-dimensional CO 2  reservoir using two-phase flow dynamics and time-lapse full waveform seismic data with a realistic surface-seismic survey design. We show more accurate estimates of the CO 2  saturation field using the EnKF compared to using either the seismic data or the fluid physics alone. Furthermore, we test a range of values for the EnKF hyperparameters and give guidance on their selection for seismic CO 2  reservoir monitoring.},
	keywords = {Bayesian inference, data assimilation, ensemble Kalman filter, FWI, Imaging, Inverse problems, ML4SEISMIC, RTM, SLIM, two-phase flow, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/bruer2024ML4SEISMICsmp},
	author = {Grant Bruer and Abhinav Prakash Gahlot and Edmond Chow and Felix J. Herrmann}
}
@presentation {bhar2024ML4SEISMICshs,
	title = {Sensitivity of Horizontal shear waves on Geological Carbon Storage},
	year = {2024},
	month = {11},
	abstract = {With the growing focus on Carbon Capture and Storage (CCS) activities, the significance of shear wave velocity (Vs) in monitoring CO2 is becoming increasingly important. Shear waves are instrumental in detecting CO2 leakage, evaluating CCS-induced seismicity, and identifying caprock failure. According to Biot-Gassmann rock physics models, changes in seismic velocity are expected during CO2 injection. This work specifically focuses on the horizontal component of shear wave velocity (Vsh). Shear (Sh) waves are more sensitive to density variations than P-wave or Sv wave velocities, leading us to anticipate changes in Sh velocity as CO2 saturation increases. While P-wave velocity tends to decrease with supercritical CO2 injection, the investigation of its effects on Sh waves presents a particularly compelling area of study. Therefore, we simulated the wave equation incorporating both P-wave and Sh wave velocities to better understand their impacts on imaging and CO2 injection processes.},
	keywords = {FWI, Imaging, Inverse problems, ML4SEISMIC, RTM, SH wave, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/bhar2024ML4SEISMICshs},
	author = {Ipsita Bhar and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {rex2024ML4SEISMIChtc,
	title = {Tucker Compression for Scalable Operator Learning in Large-Scale Parametric PDE Models},
	year = {2024},
	month = {11},
	abstract = {Simulating two-phase flow via PDEs is computationally expensive due to the inversion of large, ill-conditioned matrices. To accelerate these computations, we reformulate Hierarchical Tucker Tensor (HTT) decompositions into Kronecker products, enabling scalable Fourier Neural Operators (FNOs) for CO2 saturation predictions in subsurface environments. This reformulation allows efficient scaling across multiple GPUs while maintaining a large number of modes. Building on our existing matrix-free abstraction library, we extend its capabilities to support distributed tensor operators. The extended library is auto-differentiable, with customized AD rules for training complex networks. We demonstrate the performance and scalability of our approach by evaluating FNO simulations against traditional PDE solvers for predicting time-varying CO2 saturations from permeability models in large-scale subsurface environments.},
	keywords = {deep learning, FNO, hierarchical tucker tensor, Inverse problems, kronecker product, ML4SEISMIC, SLIM, two-phase flow},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/rex2024ML4SEISMIChtc},
	author = {Richard Rex and Srikanth Avasarala and Thomas Grady and Felix J. Herrmann}
}
@presentation {herrmann2023SCAIMact,
	title = {Act normal that{\textquoteright}s crazy enough {\textemdash} an overview of seismic inversion with normalizing flows and surrogate modeling},
	year = {2023},
	note = {(SCAIM, Vancover)},
	month = {03},
	abstract = {During this talk, I will give an overview on how techniques from neural (conditional) density estimation and surrogate modeling can be used to solve challenging problems in seismic imaging and monitoring of geological carbon storage. I will start by outlining how (conditional) normalizing flows can be used as priors, to regularize inverse problems, and as low-fidelity amortized posteriors for wave-based inversions. To this end, I will uss techniques from simulation-based inference. When time, permits I will also talk about permeability inversion from time-lapse seismic data using neural surrogates (Fourier Neural Operators) to mimic solution operators of two-phase flow equations.},
	keywords = {CCS, FNO, GCS, monitoring, Normalizing flows, SCAIM, sequential Bayes, SLIM, surrogate, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SCAIM/2023/herrmann2023SCAIMact/index.html},
	author = {Felix J. Herrmann}
}
@presentation {bruer2023ML4SEISMICcrm,
	title = {CO2 reservoir monitoring through Bayesian data assimilation},
	year = {2023},
	month = {11},
	abstract = {Carbon capture and storage can be implemented by injecting supercritical carbon dioxide (CO2) into geological carbon reservoirs for long-term containment. Monitoring the pressure and saturation of the CO2 is necessary to optimize the injection amount without causing CO2 leakage or seismic activity. Directly measuring the CO2 at locations within the reservoir requires expensive drilling procedures that may damage the reservoir, so direct measurements are sparse and usually lie along injection and production wells. Indirect measurements such as seismic data are typically noisy, and inverting for the CO2 state is ill-posed. Bayesian data assimilation techniques allow us to integrate known physics for CO2 flow into this inversion process. The most well-established data assimilation algorithms are the family of Kalman filters. The ensemble Kalman filter is designed to efficiently work with large problem sizes and nonlinearity. In this work, we apply the ensemble Kalman filter to seismic measurements of a CO2 reservoir, yielding an estimate of the saturation and pressure fields with quantified uncertainties. This method models the CO2 plume state as a random field with a known distribution and assimilates information from seismic measurements with information from a physics model describing the CO2 flow. We show that the data assimilation strategy is a valuable contribution to advancing reservoir monitoring technology.},
	keywords = {bayes, CCS, data assimilation, kalman, ML4SEISMIC, monitoring, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/bruer2023ML4SEISMICcrm},
	author = {Grant Bruer and Felix J. Herrmann and Edmond Chow}
}
@article {herrmann2023dte,
	title = {Digital twins in the era of generative AI},
	journal = {The Leading Edge},
	volume = {42},
	year = {2023},
	note = {(The Leading Edge, President{\textquoteright}s Page)},
	month = {11},
	keywords = {CCS, deep learning, GCS, Imaging, inversion, invertible networks, machine learning, monitoring, multiphysics, Normalizing flows, SLIM, software, time-lapse},
	doi = {10.1190/tle42110730.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/herrmann2023dte/PresidentsPage.pdf},
	author = {Felix J. Herrmann}
}
@presentation {yin2023ML4SEISMICe2e,
	title = {End-to-end permeability inversion from prestack time-lapse seismic data: a case study on Compass model},
	year = {2023},
	month = {11},
	abstract = {Effective geological carbon storage hinges on a deep understanding of CO2 plume behavior. The dynamics of these plumes can be modeled using multiphase flow equations, but their accuracy is tied to a precise permeability model. A significant challenge is that we often lack detailed permeability data, limiting our predictive capabilities. To bridge this gap, we{\textquoteright}ve developed a multiphysics inversion method. This technique inverts for the permeability from observed time-lapse seismic data. Through a case study on the Compass model, we{\textquoteright}ve compared this approach with traditional 4D FWI in forecasting CO2 plume movements. Additionally, our research delves into how different initial permeability models, acquisition setups, and survey frequencies affect the results. Across the board, the inversion method not only enhances our current estimations but also provides valuable insights into future plume dynamics, even without continuous monitoring.},
	keywords = {CCS, coupled inversion, end-to-end, fluid-flow, inversion, ML4SEISMIC, monitoring, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/yin2023ML4SEISMICe2e},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner and Felix J. Herrmann}
}
@presentation {erdinc2023ML4SEISMICias,
	title = {Improved automatic seismic CO2 leakage detection via dataset augmentation},
	year = {2023},
	month = {11},
	abstract = {Previous works showed that neural classifiers can be trained to detect CO2 leakage from time-lapse seismic images. While this result is crucial to the global deployment of geological carbon storage (GCS), its success depends on relatively dense non-replicated time-lapse data acquisition. In this study, we present an approach to enhance the detection accuracy and robustness of CO2 leakage detection by augmenting the training dataset with a variety of coarsely sampled receiver data and their corresponding receiver numbers. This augmentation strategy is particularly beneficial for scenarios where low-cost coarse receiver samplings, such as with ocean bottom nodes (OBNs), are utilized. Furthermore, we explore interpretability of the classifier{\textquoteright}s decisions by generating saliency maps for further analysis.},
	keywords = {CCS, classifier, deep learning, leakage detection, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/erdinc2023ML4SEISMICias},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {yin2023CSEslim,
	title = {Introduction to Seismic Laboratory for Imaging and Modeling},
	year = {2023},
	note = {(CSE Student Recruiting Event)},
	month = {11},
	abstract = {We introduce the latest research developments in Seismic Laboratory for Imaging and Modeling (SLIM), including geological carbon storage monitoring, seismic and medical imaging and uncertainty quantification, and advancements in scientific machine learning and high performance computing.},
	keywords = {CCS, deep learning, Fourier neural operators, GCS, Inverse problems, learned constraints, learned surrogates, medical imaging, multiphysics, Normalizing flows, PDE, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSE/2023/SLIM.pdf},
	author = {Ziyi Yin}
}
@presentation {rex2023ML4SEISMIClsp,
	title = {Large-scale parametric PDE approximations with model-parallel Fourier neural operators},
	year = {2023},
	month = {11},
	abstract = {Solving PDEs to simulate two-phase flow is expensive since it involves the inversion of large ill-conditioned matrices. FNOs represent a special type of neural network capable of approximating solutions to two-phase flow equations. In order to speed up this computation, we develop a high-level software abstraction tool to exploit the linearly separable property of Fourier Transforms via Kronecker products. We perform a series of all-to-all operations where we partition the data to apply the operations in a distributed fashion. We apply these FNOs to predict the evolution of CO2-plumes in subsurface environments. Our model takes an input permeability model, and outputs time-varying CO2 saturations in a quick and cost-effective manner. Additionally, our research involves developing a distributed matrix-free abstraction library that can be used to represent any generic linear and nonlinear operator. This library is scalable and auto-differentiable thanks to the hand-written customized AD rules, allowing us to represent and train any network. We provide an evaluation on the accuracy of FNO simulations compared to traditional PDE simulations in solving various classes of PDEs.},
	keywords = {CCS, deep learning, Fourier neural operators, HPC, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/rex2023ML4SEISMIClsp},
	author = {Richard Rex and Thomas J. Grady II and Rishi Khan and Ziyi Yin and Felix J. Herrmann}
}
@article {louboutin2023lmi,
	title = {Learned multiphysics inversion with differentiable programming and machine learning},
	journal = {The Leading Edge},
	volume = {42},
	year = {2023},
	note = {(The Leading Edge)},
	month = {07},
	pages = {452-516},
	abstract = {We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning.},
	keywords = {CCS, deep learning, Fourier neural operators, GCS, Imaging, inversion, invertible networks, machine learning, monitoring, multiphysics, Normalizing flows, SLIM, software, time-lapse},
	url = {https://library.seg.org/doi/10.1190/tle42070474.1},
	presentation = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.html},
	author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Ali Siahkoohi and Gabrio Rizzuti and Philipp A. Witte and Olav M{\o}yner and Gerard J. Gorman and Felix J. Herrmann}
}
@presentation {li2023ML4SEISMICmci,
	title = {Maximizing CO2 injectivity within fracture pressure},
	year = {2023},
	month = {11},
	abstract = {In geological carbon storage projects, optimizing CO2 injection strategies is paramount to enhance storage efficiency and prevent leakage. The objective is to maximize the CO2 injection volume without surpassing the fracture pressure. Traditional adjoint-based approaches necessitate extensive numerical simulations, leading to significant computational overhead. To circumvent this challenge, we introduce an optimization framework based on physics-informed deep convolutional neural networks. Trained on different permeability slices, our model can rapidly predict the maximal CO2 injection volume for new permeability fields in real-time.},
	keywords = {CCS, ML4SEISMIC, Optimization, reservoir simulation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/li2023ML4SEISMICmci},
	author = {Haoyun Li and Ziyi Yin and Olav M{\o}yner and Felix J. Herrmann}
}
@presentation {herrmann2023ML4SEISMICmsc,
	title = {Monitoring subsurface CO2 plumes with learned sequential Bayesian inference},
	year = {2023},
	month = {11},
	abstract = {Reservoir engineers frequently employ two-phase flow simulations and history-matching to oversee and anticipate the behavior of CO2 plumes within geological carbon storage. These simulations, while valuable for gaining insights, face limitations due to several complex factors, such as uncertainties surrounding the plume{\textquoteright}s dynamics. To investigate this phenomenon more comprehensively, we introduce the concept of stochasticity in the dynamics, accounting for uncertainties in the underlying permeability of the reservoir. To enhance the accuracy of CO2 plume predictions and quantify the uncertainties involved, we utilize machine learning techniques to condition these predictions on time-lapse seismic and well observations. This framework works on the principle of sequential Bayesian inference that continuously assimilates information from time-lapse observations, updates the CO2 plume predictions, and characterizes uncertainties about the plumes.},
	keywords = {Bayesian inference, CCS, deep learning, Imaging, ML4SEISMIC, monitoring, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/herrmann2023ML4SEISMICmsc},
	author = {Abhinav Prakash Gahlot and Ting-ying Yu and Rafael Orozco and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {gahlot2023ML4SEISMICtsm,
	title = {Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model},
	year = {2023},
	month = {11},
	abstract = {During time-lapse seismic monitoring of CO2 plumes, a weak 4D signal below the level of inversion or migration artifacts poses challenges. To address these, low-cost randomized non-replicated acquisitions and a linear joint recovery model (JRM) have been introduced. It takes advantage of the shared information between different vintages in the time-lapse seismic data and subsurface structure undergoing localized changes. Since the relationship between seismic data and subsurface properties is seldom linear, we propose a more versatile nonlinear JRM (nJRM) to invert for the squared slowness of the vintages. The nJRM takes advantage of the full nonlinear relation between these squared slownesses and time-lapse data through the wave equation. Also, careful derivation of the gradients makes the computational cost of nJRM equivalent to the independent recovery. We present a synthetic study for geological carbon storage (GCS) which shows that the non-replication can be beneficial to time-lapse imaging, making seismic monitoring of GCS less costly for the long term sustainability of the technology.},
	keywords = {CCS, JRM, ML4SEISMIC, monitoring, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/gahlot2023ML4SEISMICtsm},
	author = {Abhinav Prakash Gahlot and Mathias Louboutin and Ziyi Yin and Felix J. Herrmann}
}
@presentation {orozco2023ML4SEISMICtgs,
	title = {Towards generative seismic kriging with normalizing flows},
	year = {2023},
	month = {11},
	abstract = {Our goal is to build realistic parameterized (acoustic, velocity, permeability etc) earth models where the training and testing phase of our method uses only data that is available in the field. We first demonstrate the expressive power of normalizing flows to generate detailed realistic earth models by training on supervised pairs of full earth models and borehole wells. Our results are compared with traditional variogram kriging to show that our generated models can be used in parameterizations of various downstream tasks such as simulations of realistic acoustic waves and fluid flow for reservoir simulations. Then we introduce a novel unsupervised training objective that can train normalizing flows to generate full earth models without needing training pairs of the full earth models. By using a known proxy earth model as a testbed, we make preliminary prescriptions on how many wells our method needs to generate permissible earth models in a target area.},
	keywords = {kriging, ML4SEISMIC, Normalizing flows, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/orozco2023ML4SEISMICtgs},
	author = {Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {orozco2023ML4SEISMICuqs,
	title = {Uncertainty quantification so what? Leveraging probabilistic seismic inversion for experimental design},
	year = {2023},
	month = {11},
	abstract = {Combining physics with recent developments of generative machine learning enables a scalable probabilistic framework for tackling seismic inversion problems including Full-Waveform Inversion. These probabilistic results can be proven to be from the Bayesian posterior but how exactly can we use them for practical downstream tasks? In this talk, we answer the question with a practical application of the probabilistic framework towards designing ocean bottom node placement of seismic imaging. With a simple adjustment to the original training objective, we show that jointly optimizing for an experimental design corresponds to maximizing the expected information gain used by the Bayesian community. After verifying this novel joint optimization with a stylized problem, we demonstrate its application for optimizing the placement of ocean bottom nodes in a synthetic seismic imaging experiment.},
	keywords = {bayesian, experimental design, FWI, ML4SEISMIC, Normalizing flows, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/orozco2023ML4SEISMICuqs},
	author = {Rafael Orozco and Mathias Louboutin and Peng Chen and Felix J. Herrmann}
}
@presentation {yin2023ML4SEISMICwise,
	title = {WISE: Full-waveform Inference with Subsurface Extensions},
	year = {2023},
	month = {11},
	abstract = {Quantifying uncertainty in full-waveform inversion is complex given the large sizes of both the model and data. A previous approach employed a variational inference framework, leveraging reverse-time migration to summarize observed data and approximate the posterior distribution through conditional normalizing flows. While reverse-time migration effectively summarizes the data when the background model is close to the true one, its accuracy diminishes with a less accurate background model. In our study, we suggest utilizing subsurface offset gathers as the summary statistics for the variational inference of full-waveform inversion. These gathers retain all the information in seismic data, even when the background model is cycle-skipped or fails to flatten the gathers. Through a case study on Compass model, we confirm our framework{\textquoteright}s effectiveness and show that subsurface offset gathers offer a better summary statistic than just reverse-time migration.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, FWI, inversion, machine learning, ML4SEISMIC, Normalizing flows, SLIM, Summary Statistics, uncertain quantification, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/yin2023ML4SEISMICwise},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {orozco2022ML4SEISMICaos,
	title = {Adjoint operators as summary functions in amortized Bayesian inference frameworks},
	year = {2022},
	month = {11},
	abstract = {An important concern in seismic inverse problems is the large and varying size of observed data. The large size can cause computational cost concerns and its varying size (such as when changing receiver geometries) implies the need to rerun inference algorithms from scratch for each new observation. Motivated by these two problems, we take inspiration from the statistics literature which commonly relies on summary statistic of observed data. Summary statistic compress the observed data leaving only information needed for inference. In this work, we argue that the adjoint operator provides a natural candidate for a summary function in the context of physics-based inverse problems. We first mathematically show that for certain general assumptions transforming data under the adjoint operator defines a new conditional distribution which preserves the expectations of the original posterior. We validate our hypothesis by evaluating our framework in a learned amortized inference algorithm. Our seismic and medical synthetic experiments show computational gains and increased quality of point estimates using our framework. We discuss statistical metrics that show our learned posterior is well calibrated therefore justifying its use in uncertainty quantification.},
	keywords = {Amortized Inference, Bayesian inference, deep learning, Inverse problems, machine learning, medical imaging, ML4SEISMIC, Normalizing flows, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/orozco2022ML4SEISMICaos/index.html},
	author = {Rafael Orozco and Mathias Louboutin and Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
@presentation {yin2022ML4SEISMICavc,
	title = {Amortized velocity continuation with Fourier neural operators},
	year = {2022},
	month = {11},
	abstract = {Velocity continuation aims to map the migration image using one background model to the image using another background model. It is of great importance to quantify the uncertainty in seismic imaging result from various background models. With Fourier neural operators as a learned surrogate, this continuation from a given background model to an unseen background model can be quite accurately estimated with near-zero cost. However, the limitation of the prior art is that the input background model and the survey area are assumed to be fixed. The main contribution of this work is to extend the Fourier neural operator surrogate to be amortized over different given background models and survey areas. We verify the effectiveness of our learned surrogates by a realistic example on different areas of Parihaka dataset against different background models.},
	keywords = {Fourier neural operators, ML4SEISMIC, SLIM, Uncertainty quantification, velocity continuation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICavc/index.html},
	author = {Ali Siahkoohi and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {erdinc2022ML4SEISMICdgp,
	title = {De-risking GCS projects with explainable CO$_2$ leakage detection in time-lapse seismic images},
	year = {2022},
	month = {11},
	abstract = {With the global deployment of Carbon, capture and storage (CCS) technology to combat climate change, there is an associated risk of contamination with CO2 leaking back to the atmosphere. Thus, it requires continuous monitoring of CO2 after the injection stops at the storage site. In this work, we generated synthetic CO2 plume development data with both leakage and no leakage scenarios. We trained a convolutional neural network (CNN) discriminative classifier and also a generative classifier and compared their performances in CO2 leakage detection. The accuracy of our discriminative classifier on the test data is 85\% and that of the generative classifier is 90\%. The Class Activation Mapping (CAM) results of the discriminative classifier and the latent space representation of our dataset in the case of generative classifier strengthens our claims about trustworthy leakage classification.},
	keywords = {CAM, CCS, classification, explainability, GCS, Imaging, JRM, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/erdinc2022ML4SEISMICdgp/index.html},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {grady2022ML4SEISMICesn,
	title = {Effective scaling of numerical surrogates via domain-decomposed Fourier neural operators},
	year = {2022},
	month = {11},
	abstract = {Numerical surrogates are models which learn to mimic a complex physical process (such as the solution to a PDE produced by a solver) from a set of input/output pairs. Fourier neural operators (FNOs) are a specific type of numerical surrogate which use a learned matched filter to quickly approximate solutions to relatively smooth complex physical processes. In the case of carbon capture sequestration (CCS) technology, FNOs have been shown to well-approximate solutions to the two-phase flow equations, with speedups of 1,000 to 10,000 times at inference time versus a tradtitional solver. This speed combined with the fact that FNOs are differentiable with respect to their input parameters allows for inverse and uncertainty quantification problems to theoretically be solved on real 3D data, a previously intractible task. However, due to the size of the input data, network weights, and optimizer state, FNOs have thus far been limited to small to medium 2D and 3D problems, well below the size of an industry standard such as the Sleipner benchmark. Here we alleviate this problem by proposing a model-parallel FNO which makes use of domain decomposition of the input data and network weights, and exploits architectural features of FNOs to also include a natural form of asynchronous pipeline parallelism. Our network can scale to arbitrary problem sizes on CPU and GPU systems.},
	keywords = {CCS, deep learning, Fourier neural operators, HPC, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/grady2022ML4SEISMICesn/index.html},
	author = {Thomas J. Grady II and Rishi Khan and Mathias Louboutin and Ziyi Yin and Philipp A. Witte and Ranveer Chandra and Russell J. Hewett and Felix J. Herrmann}
}
@presentation {herrmann2022ML4SEISMICintro,
	title = {Introduction to 2022 ML4SEISMIC Partners Meeting},
	year = {2022},
	month = {11},
	keywords = {ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/herrmann2022ML4SEISMICintro/index.html},
	author = {Felix J. Herrmann}
}
@presentation {yin2022TRANSFORMjulia,
	title = {Julia for Geoscience},
	year = {2022},
	month = {04},
	abstract = {In this tutorial, we will introduce the Julia programming language to the geoscience community, covering topics such as I/O, data processing, inversion, and machine learning. We will begin by installing Julia and relevant packages. Through a series of tutorials, we will demonstrate Julia{\textquoteright}s abstraction power and show how to load and plot data, write your own functions/operators, form and solve a geophysical inverse problem, and demonstrate how to integrate wave-equation solvers in Julia with machine learning frameworks. The intent of this presentation is to provide an introductory level tutorial that will be useful to members of the geoscience community.},
	keywords = {JOLI, JUDI, Julia, machine learning, SLIM, software, TRANSFORM},
	url = {https://transform.softwareunderground.org/2022-julia-for-geoscience},
	url2 = {https://www.youtube.com/watch?v=HyWfp3NzIbg},
	software = {https://github.com/slimgroup/SLIMTutorials},
	author = {Ziyi Yin and Mathias Louboutin and Philipp A. Witte and Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Henryk Modzelewski and Felix J. Herrmann}
}
@presentation {louboutin2022ML4SEISMIClew,
	title = {Learned extensions for wave-based simulation and inversion},
	year = {2022},
	month = {11},
	abstract = {We introduce a new method that explores velocities as an operator (extended velocities) for wave-equation based inversion. Through this extended formulation, we obtain the known benefits of working with subsurface offset volumes. The offset-dependence of these volumes has been studied in the linear case, i.e as part of extended Born scattering and extended least-squares reverse-time migration, but has been avoided for non-linear inversion due to computational concderns and challenges. By using techniques from randomized linear algebra, we will show that we can work with extended velocities for inversion while maintaining an acceptable computational cost much lower than solving one PDE per extended velocity model.},
	keywords = {deep learning, Imaging, ML4SEISMIC, SLIM, wave equation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/louboutin2022ML4SEISMIClew/index.html},
	author = {Mathias Louboutin and Yadhu Kartha and Rafael Orozco and Felix J. Herrmann}
}
@presentation {siahkoohi2022ML4SEISMICluq,
	title = {Low-cost uncertainty quantification for large-scale inverse problems},
	year = {2022},
	month = {11},
	abstract = {Bayesian inference for large-scale inverse problems is challenged by the computationally costly forward operator evaluations during posterior distribution sampling. Recent advances in variational inference and deep learning reduce these costs by pretraining a neural network capable of sampling the posterior distribution for previously unseen observed data. In geophysical applications, however, the accuracy of these methods depends on sufficiently capturing subsurface variability through a training dataset, which is challenging given the heterogeneity of the Earth{\textquoteright}s subsurface and our lack of access to it. Moreover, these methods may be unreliable in the presence of data distribution shifts, e.g., a change in the number of source experiments, noise distribution, or geological features to be imaged. As such, we present a solution that increases the robustness of deep-learning-based Bayesian inference approaches when faced with changes in data distribution. Our proposed method involves a physics-based adaptation to the latent distribution of a conditional normalizing flow that is pretrained to approximate the posterior distribution for previously unseen data. Instead of feeding standard Gaussian latent samples to the conditional normalizing flow, this method parameterizes the latent distribution by a Gaussian distribution with an unknown mean and diagonal covariance, estimated by minimizing the Kullback-Leibler divergence between predicted and true posterior distributions. This method is applicable to a wide range of inverse problems and has the potential to significantly reduce the costs of Bayesian variational inference. By means of a realistic seismic imaging example we demonstrate that the proposed latent distribution adaptation method mitigates the Bayesian inference errors induced by data distribution shifts, including shifts in the forward model and prior distribution.},
	keywords = {Inverse problems, ML4SEISMIC, Normalizing flows, seismic imaging, SLIM, Uncertainty quantification, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/siahkoohi2022ML4SEISMICluq/siahkoohi2022ML4SEISMICluq_pres.pdf},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Felix J. Herrmann}
}
@presentation {herrmann2022ML4SEISMICmod,
	title = {Meet our digital twin for geological carbon storage},
	year = {2022},
	month = {11},
	abstract = {By embracing recent developments in simulation-based Bayesian inference{\textemdash}i.e., the task of deriving statistical information from a system based on in silico simulations{\textemdash}we envisage the development of an uncertainty-aware Digital Twin for seismic monitoring of Geologic Carbon Storage (GCS). According to IBM, {\textquotedblleft}A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making{\textquotedblright}. This Digital Twin will be designed to maximally benefit from vastly improved abilities to simulate complex phenomena, including the development of CO2 plumes in saline aquifers, and from the ability of neural networks to learn by example as part of inference. For GCS, this means that systematic assessment of uncertainties now becomes possible when observing CO2 plumes from time-lapse geophysical data (e.g., seismic). Because the proposed Digital Twin{\textquoteright}s neural networks are taught to produce samples from the probability distribution for the CO2 plume conditioned by the observed time-lapse data, this approach will provide access to this information on uncertainty. As part of ML4Seismic, we are working on various aspects regarding the development of the Digital Twin including: (i) capability to generate realistic time-lapse data in response to CO2 injection in large strongly heterogeneous reservoirs. This simulation framework will facilitate the design of high-fidelity monitoring systems and is unique since it uses proxy Earth models with realistic CO2 plumes and heterogeneity; (ii) An inversion framework capable of producing high-fidelity time-lapse images of CO2 plumes and reservoir properties from time-lapse data collected in response to CO2 injection; (iii) uncertainty-aware data-assimilation framework based on techniques from sequential Bayes and capable of rapidly producing high-fidelity CO2 plume forecasts that are consistent with observed time-lapse data; (iv) A scalable uncertainty-aware early warning system designed to safeguard CO2 injection operations built on the latest insights from interpretable and trustworthy (explainable and robust) machine learning. After describing how to build a Digital Twin for GSC, early results will be presented on the use of Fourier Neural Networks as surrogates for the two-phase flow equations, seismic monitoring with our joint recovery model, and the use of spectral ratio to design low-cost acquisitions for time-lapse seismic.},
	keywords = {CCS, digital twin, GCS, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/herrmann2022ML4SEISMICmod/index.html},
	author = {Felix J. Herrmann}
}
@presentation {louboutin2022ML4SEISMICmos,
	title = {ML4Seismic open-source software: updates and developments},
	year = {2022},
	month = {11},
	abstract = {Software is at the core of research and development in inverse problems. At SLIM, we have experience developing scalable and performant software, such as our legacy parallel MATLAB framework. With ML4Seismic, we are dedicated to build on this experience to develop HPC open source software (OSS) for the scientific community in collaboration with our partners. In this talk, we will describe our OSS Julia and Python environment, our high-level abstraction principles, and the range of solutions we offer for seismic processing and inversion and for machine learning. We will emphasize our aim to provide scalable software that can be easily applied to industrial problems.},
	keywords = {ML4SEISMIC, open-source, SLIM, software},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/louboutin2022ML4SEISMICmos/index.html},
	author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Felix J. Herrmann}
}
@presentation {yu2022ML4SEISMICmsb,
	title = {Monitoring with sequential Bayesian inference},
	year = {2022},
	month = {11},
	abstract = {For this study, we apply sequential Bayesian inference to monitor the time evolution of subsurface flow of CO2 from indirect acoustic measurements at the surface. Upon receiving new acoustic measurements, we infer the current state of the CO2 by sampling from a learned posterior. Using the incoming data, we then perform online updates of the current posterior. This is accomplished by using the fluid flow model to advance the estimated state variable forward in time in order to update the learned posterior. With a synthetic experiment, we demonstrate this method can track the flow evolution accurately as measured by PSNR metrics. Since the posterior is a learned network, we can compute estimates faster than traditional least squares methods. This method can also quantify the uncertainty due to stochasticity in fluid flow model and the limited-azimuth imaging configuration.},
	keywords = {CCS, conditional normalizing flows, GCS, ML4SEISMIC, monitoring, sequential Bayes, SLIM, time-lapse, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yu2022ML4SEISMICmsb/index.html},
	author = {Ting-ying Yu and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@presentation {orozco2022ML4SEISMICnfr,
	title = {Normalizing flows for regularization of 3D seismic inverse problems},
	year = {2022},
	month = {11},
	abstract = {We present the first known exploration of a normalizing flow (NF) for generative 3D volumes. First, we tackle computational issues surrounding the high dimensionality of our desired 3D volume output. This is of particular concern in normalizing flows since their invertibility constraint implies equal dimension of output and input. Our findings show that by {\textquotedblleft}freezing{\textquotedblright} expensive layers we can efficiently train a normalizing flow on 3D volumes. Using this NF architecture, we train a generative model on volume sections of the 3D BG compass model. Our method produces visually plausible generative samples which are efficient to produce. We demonstrate its practical use by using our trained generative model as an implicit prior in a Maximum A Posteriori (MAP) framework. We evaluate this MAP framework by estimating the solution of a inverse problem in seismic imaging. Our method results in higher SNR estimates than the baseline and in less iterations, importantly saving the computational cost of evaluating the expensive 3D PDE solver during optimization. Finally, through scaling analysis of training cost, we show that NF convolutional layers allow this approach to scale favorably to larger volumes.},
	keywords = {3D, Bayesian inference, deep learning, Inverse problems, machine learning, ML4SEISMIC, Normalizing flows, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/orozco2022ML4SEISMICnfr/index.html},
	author = {Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {yin2022ML4SEISMICsfg,
	title = {Simulation-based framework for geological carbon storage monitoring},
	year = {2022},
	month = {11},
	abstract = {While various monitoring modalities exist to track the behavior of CO2 plumes to ensure safe operations and compliance with regulatory requirements, active 3D time-lapse seismic monitoring has proven superior but costly. At SLIM, we aim to reduce the operating costs by optimizing acquisition design, to help drive innovations in seismic monitoring acquisition design and imaging, and to test novel time-lapse acquisition and imaging technologies in silico at scale. In this talk, we will introduce our open-source software platform simulation-based monitoring design framework. We demonstrate how to make use of proxy models for seismic properties derived from real 3D imaged seismic and well data to conduct realistic synthetic geological carbon storage projects. Furthermore, we discuss our proposed sparse non-replicated seismic acquisition and cutting-edge methodology to recover the dense data or to directly image the sparse non-replicated via joint recovery model. This automatic workflow ends with deep neural classifiers to detect potential CO2 leakage over seal through pressure-induced fault openings. We envisage the development of an automatic workflow to handle the large number of continuously monitored CO2 injection sites needed to help combat climate change.},
	keywords = {CAM, CCS, classification, deep learning, explainability, GCS, Imaging, JRM, machine learning, ML4SEISMIC, SLIM, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICsfg/index.html},
	author = {Ziyi Yin and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {zhang2022ML4SEISMICtss,
	title = {Time-lapse seismic survey design by maximizing the spectral gap},
	year = {2022},
	month = {11},
	abstract = {While time-lapse seismic has been applied successfully to CO2 sequestration monitoring, it remains a challenging problem since replicated dense surveys come at a very high cost in the field. Wavefield reconstruction based on matrix completion (MC) from randomized subsampled data is an efficient way to reduce operational costs. This technique allows for accurate time-lapse reconstruction by employing the joint recovery model (JRM), which capitalizes on the fact that different vintages share a common component. However, combining JRM with optimal time-lapse acquisition survey design remains an unexplored area of research. In expander graph theory, spectral gap (SG) reveals the source-receiver layout connectivity and is related to reconstruction quality during MC. Building on these insights, we proposed a simulation free time-lapse survey design based on JRM that aims to get similar reconstructed quality without insisting on replicate surveys, which significantly reduces the cost in the field. This approach uses the simulated annealing algorithm to find subsampling masks for each vintage. Numerical experiments confirm a direct correlation between increased spectral gap and promising time-lapse reconstruction quality.},
	keywords = {Acquisition, JRM, matrix factorization, ML4SEISMIC, SLIM, spectral gap, survey design, time-lapse, wavefield reconstruction},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/zhang2022ML4SEISMICtss/index.html},
	author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar and Oscar Lopez and Felix J. Herrmann}
}
@presentation {herrmann2022DIRACCudt,
	title = {Uncertainty-aware Digital Twin for Monitoring Geological Carbon Storage},
	year = {2022},
	note = {(Diracc inaugural meeting, Atlanta)},
	month = {11},
	keywords = {CAM, CCS, classification, digital twin, Diracc, explainability, SLIM, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Diracc/2022/herrmann2022DIRACCudt/index.html},
	author = {Felix J. Herrmann}
}
@presentation {yin2022ML4SEISMICutc,
	title = {Uncertainty-aware time-lapse CO$_2$ monitoring with learned end-to-end inversion},
	year = {2022},
	month = {11},
	abstract = {Seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics modeling and wave physics and differentiation through the solvers with respect to the subsurface properties of interest. In this talk, we demonstrate the effectiveness of learned coupled inversion framework using a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator, which greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. We study the effectiveness and correctness of inversion based on Fourier neural operator surrogate and a normalizing flow prior. We also demonstrate the efficacy of this framework on monitoring the growth of CO2 plumes during sequestration, and on uncertainty quantification of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can further forecast the CO2 plume in the future without any acquired seismic data with uncertainty estimation.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, Fourier neural operators, GCS, inversion, machine learning, ML4SEISMIC, Normalizing flows, SLIM, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICutc/index.html},
	author = {Ziyi Yin and Rafael Orozco and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {grady2021ML4SEISMICdfn,
	title = {Distributed Fourier Neural Operators},
	year = {2021},
	month = {11},
	abstract = {Fourier Neural Operators (FNOs) are a class of neural operator, which use weightings on the Fourier transform of their inputs to approximate infinite-dimensional mappings between function spaces. They are particularly useful in approximating the solutions of smooth parametric (e.g. by permeability) partial differential equations (PDEs), and once trained are capable of producing a nearly identical output to a traditional numerical solver roughly three orders of magnitude faster, making them very useful in a wide variety of engineering applications that require repeated PDE solves (e.g. during uncertainty quantification). Until now, FNOs have been limited to small problems, as their memory intensive design makes them difficult to scale in a traditional machine learning setting on a single computer or GPU. In this work, a decomposition scheme is described and implemented in PyTorch using the DistDL distributed deep learning framework, which is capable of scaling FNOs to arbitrary dimension and input size running on many nodes in a distributed memory system. This parallel implementations allows FNOs to be trained and run on problems of a practical scale on both CPU and GPU clusters.},
	keywords = {CCS, deep learning, Fourier neural operators, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/grady2021ML4SEISMICdfn/Tue-12-10-Grady.pdf},
	author = {Thomas J. Grady II and Rishi Khan and Felix J. Herrmann}
}
@presentation {rizzuti2021ML4SEISMICdfw,
	title = {A dual formulation of wavefield reconstruction inversion for large-scale seismic inversion},
	year = {2021},
	month = {11},
	abstract = {Many of the seismic inversion techniques currently proposed that focus on robustness with respect to the background model choice are not apt to large-scale 3D applications, and the methods that are computationally feasible for industrial problems, such as full waveform inversion, are notoriously limited by convergence stagnation and require adequate starting models. We propose a novel solution that is both scalable and less sensitive to starting models or inaccurate parameters (such as anisotropy) that are typically kept fixed during inversion. It is based on a dual reformulation of the classical wavefield reconstruction inversion, whose empirical robustness with respect to these issues is well documented in the literature. While the classical version is not suited to 3D, as it leverages expensive frequency-domain solvers for the wave equation, our proposal allows the deployment of state-of-the-art time-domain finite-difference methods, and is potentially mature for industrial-scale problems.},
	keywords = {FWI, ML4SEISMIC, SLIM, wave-equation, WRI},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/rizzuti2021ML4SEISMICdfw/Tue-10-55-Rizzuti.pdf},
	author = {Gabrio Rizzuti and Mathias Louboutin and Rongrong Wang and Felix J. Herrmann}
}
@presentation {yin2021ML4SEISMICism,
	title = {Improved seismic monitoring of CO2 sequestration with the weighted joint recovery model},
	year = {2021},
	month = {11},
	abstract = {Time-lapse seismic monitoring of CO2 sequestration is challenging because the time-lapse signature of CO2 plumes is weak in amplitude and often contaminated by imaging artifacts due to coarsely sampled, noisy, and non-replicated surveys. In this talk, we present a sparsity-promoting least-squares imaging method where the baseline, and the current and past monitor surveys are inverted jointly. We demonstrate that the sensitivity of seismic monitoring can be improved by inverting for the common component{\textemdash}i.e., the component shared by all vintages, and innovations with respect to this common component. Combining this joint approach with weighted l1,2-norm minimization leads to a monitoring scheme capable of detecting irregular CO2-plume growth in a realistic geological setting.},
	keywords = {CCS, Compressive Sensing, Imaging, JRM, ML4SEISMIC, SLIM, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/yin2021ML4SEISMICism/Tue-11-20-Yin.html},
	author = {Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {zhang2021ML4SEISMICiss,
	title = {Improved seismic survey design by maximizing the spectral gap with global optimization},
	year = {2021},
	month = {11},
	abstract = {Random subsampling is increasingly being used in the acquisition of seismic data to shorten the acquisition time and to reduce costs. However, the design of optimal acquisition geometries is still an ongoing area of research. Matrix completion (MC) is a computationally efficient method to reconstruct fully sampled wavefields from sparsely sampled seismic data. In MC theory, the spectral gap (SG), which is a measure of the connectedness of the graph in expander graph theory, has been used to predict, and to some degree quantify, the quality of wavefield reconstruction, given a specific subsampling scheme (acquisition mask). Building on these insights, we propose an optimization scheme, based on simulated annealing, which finds subsampling masks with large SGs that improve the quality of wavefield reconstruction with MC. The experimental results show that the proposed method successfully increases the SG of the subsampling mask starting from randomly initialized masks. Increasing the SG leads to improved connectivity between the sources and receivers and therefore of the wavefield reconstruction. Numerical experiments confirm a direct relationship between increased SG and improved reconstruction quality. This confirms the value SG analysis brings to the design of seismic surveys without the need to carry out expensive wavefield reconstructions to optimize the acquisition design.},
	keywords = {Acquisition, Compressive Sensing, ML4SEISMIC, SLIM, survey design, wavefield reconstruction},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/zhang2021ML4SEISMICiss/Tue-10-30-Zhang.pdf},
	author = {Yijun Zhang and Felix J. Herrmann}
}
@presentation {herrmann2021ML4SEISMICintro,
	title = {Introduction inaugural ML4Seismic Partners Meeting},
	year = {2021},
	month = {11},
	abstract = {During this presentation, an overview of the 2021 ML4Seismic Program will be given including organization of the meeting, setup of ML4Seismic, and the Informal Sessions in the afternoon.},
	keywords = {ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/herrmann2021ML4SEISMICintro/Mon-09-00-Herrmann.pdf},
	author = {Felix J. Herrmann}
}
@presentation {louboutin2021ML4SEISMICmos,
	title = {ML4Seismic open source software environment},
	year = {2021},
	month = {11},
	abstract = {Software is at the core of research and development in inverse problems. At SLIM, we have experience developing scalable and performant software, such as our legacy parallel MATLAB framework. With ML4Seismic, we are dedicated to build on this experience to develop HPC open source software (OSS) for the scientific community in collaboration with our partners. In this talk, we will describe our OSS Julia and Python environment, our high-level abstraction principles, and the range of solutions we offer for seismic processing and inversion and for machine learning. We will emphasize our aim to provide scalable software that can be easily applied to industrial problems.},
	keywords = {ML4SEISMIC, SLIM, software},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/louboutin2021ML4SEISMICmos/Mon-10-10-Louboutin.pdf},
	author = {Mathias Louboutin}
}
@presentation {siahkoohi2021ML4SEISMICmcn,
	title = {Multifidelity conditional normalizing flows for physics-guided Bayesian inference},
	year = {2021},
	month = {11},
	abstract = {We introduce a scalable Bayesian inference approach that combines techniques from deep learning with a physic-based variational inference formulation. Bayesian inference for ill-posed inverse problems is challenged by the high-dimensionality of the unknown, computationally expensive forward operator, and choosing a prior distribution that accurately encodes prior knowledge on the unknown. To handle this situation and to assess uncertainty, we propose to approximate the posterior distribution using a pretrained conditional normalizing flow, which is trained on existing low- and high-fidelity estimations of the unknown. To further improve the accuracy of this approximation, we use transfer learning and finetune this normalizing flow by minimizing the Kullback-Leibler divergence between the predicted and the desired high-fidelity posterior density. This amounts to minimizing a physic-based variational inference objective with respect to the network weights, which we believe might scale better than Bayesian inference with Markov Chain sampling methods. We apply the proposed Bayesian inference approach to seismic imaging where we use quasi-real data obtained from the Parihaka dataset.},
	keywords = {deep learning, ML4SEISMIC, Normalizing flows, seismic imaging, SLIM, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/siahkoohi2021ML4SEISMICmcn/Mon-11-45-Siahkoohi.pdf},
	author = {Ali Siahkoohi and Rafael Orozco and Gabrio Rizzuti and Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {louboutin2021ML4SEISMICrla,
	title = {Randomized linear algebra for inversion},
	year = {2021},
	month = {11},
	abstract = {Inverse problems in exploration geophysics or machine learning heavily relies on linear algebra and large matrices manipulations. To tackle the growing cost of storing these matrices, randomized algorithms have been developed to obtain information from these matrices via randomized sketching. Inspired by previous work on extended image volumes, we will first show in this talk how the seismic imaging condition can be expressed in a randomized linear algebra framework leading to drastic memory savings. In a second part, we will extend this idea to convolutional neural networks to reduce the memory cost of training by orders of magnitude. We will demonstrate the practicality of these methods on representative examples.},
	keywords = {FWI, HPC, inversion, ML4SEISMIC, randomized linear algebra, SLIM, software},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/louboutin2021ML4SEISMICrla/Tue-10-20-Louboutin.pdf},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@presentation {witte2021ML4SEISMICrtc,
	title = {Redwood {\textendash} towards clusterless supercomputing in the cloud},
	year = {2021},
	month = {11},
	abstract = {We present Redwood, a Julia framework for clusterless supercomputing in the cloud. Redwood provides a set of distributed programming macros that enable users to remotely execute Julia functions in parallel through cloud services for batch and serverless computing. We present the architecture and design of Redwood, as well as its application to existing Julia packages for machine learning and inverse problems.},
	keywords = {cloud computing, clusterless, HPC, Julia, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/witte2021ML4SEISMICrtc/Mon-10-30-Witte.pdf},
	author = {Philipp A. Witte}
}
@presentation {siahkoohi2021ML4SEISMICuqi,
	title = {Uncertainty quantification in imaging and automatic horizon tracking{\textemdash}a Bayesian deep-prior based approach},
	year = {2021},
	month = {11},
	abstract = {In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, typically constitutes only the first stage of a sequential workflow, and UQ becomes even more relevant when applied to subsequent tasks that are highly sensitive to the inversion outcome. In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result. As such, the main contribution of this work consists in a data-guided approach to horizon tracking uncertainty analysis. This work is fundamentally based on a special reparameterization of reflectivity, known as {\textquotedblleft}deep prior{\textquotedblright}. Feasible models are restricted to the output of a convolutional neural network with a fixed input, while weights and biases are Gaussian random variables. Given a deep prior model, the network parameters are sampled from the posterior distribution via a Markov chain Monte Carlo method, from which the conditional mean and point-wise standard deviation of the inferred reflectivities are approximated. For each sample of the posterior distribution, a reflectivity is generated, and the horizons are tracked automatically. In this way, uncertainty on model parameters naturally translates to horizon tracking. As part of the validation for the proposed approach, we verified that the estimated confidence intervals for the horizon tracking coincide with geologically complex regions, such as faults.},
	keywords = {horizon picking, Imaging, machine learning, ML4SEISMIC, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/siahkoohi2021ML4SEISMICuqi/Mon-11-20-Siahkoohi.pdf},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
@presentation {orozco2021ML4SEISMICvia,
	title = {Variational inference for artifact removal of adjoint solutions in photoacoustic problems},
	year = {2021},
	month = {11},
	abstract = {Photoacoustic is a medical imaging modality which combines light and ultrasound waves to image internal structures of biological tissue. The inverse problem reconstructs the tissue initially excited by light given propagated ultrasound data at receivers outside the tissue. Due to noisy, limited-view and sparse receiver data, traditional time-reversal adjoint solutions are highly ill-posed. This necessitates uncertainty quantification to communicate to practitioners which areas of the image can be trusted. We propose a framework which leverages a machine learning based method (Conditional Normalizing Flows) to learn the full posterior distribution of viable solutions given the time-reversal adjoint solution. We show that areas of calculated uncertainty correlate with structures that are known to be difficult to image. In addition, we also propose a MAP based solution, which solves the variational least-squares problem while using the trained Conditional Normalizing Flow as a prior distribution.},
	keywords = {conditional prior, deep image, MAP, ML4SEISMIC, normalizing flow, Photoacoustic, SLIM, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/orozco2021ML4SEISMICvia/Tue-09-00-Orozco.html},
	author = {Rafael Orozco and Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
@presentation {peters2017SINBADFaaj,
	title = {Algorithms and Julia software for FWI with multiple constraints},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present a framework to add multiple convex and non-convex constraints to nonlinear inverse problems, specifically FWI. The constraints mitigate problems related to noisy data, artifacts arising from working with very few simultaneous sources, inaccurate starting models and using approximate physical forward models. Compared to earlier work at SLIM, the current framework is algorithmically simpler and computationally more efficient. We show examples where the model estimation is improved when we use very limited prior knowledge directly as constraints. We also present the software implementation in Julia and how it is used together with other software that compute data-misfit values and gradients w.r.t. the model parameters.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/peters2017SINBADFaaj/peters2017SINBADFaaj.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/peters2017SINBADFaaj/peters2017SINBADFaaj.m4v},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {louboutin2017SINBADFddg,
	title = {Data driven Gradient Sampling for seismic inversion},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present in this work an extension of the Gradient Sampling algorithm presented at the last EAGE in Paris. We previously showed the potential of this algorithm playing with implicit time-shifts to represent the wavefield of a slightly perturbed velocity model. We introduce an extension where the weights of the Gradient Sampling algorithm are obtained with the solve of data-based quadratic subproblem instead of at random. The update direction is the a more accurate representation of the true Gradient Sampling update direction.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFddg/louboutin2017SINBADFddg.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFddg/louboutin2017SINBADFddg.mov},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@presentation {kumar2017SINBADFfas,
	title = {Full-azimuth seismic data processing w/ coil acquisition},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this work, we will demonstrate the performance of our in-house 5D low-rank based interpolation method on a seismic data acquired using coil shooting full-azimuth acquisition. We will show that we can recover full-azimuthal interpolated data from highly subsampled data, where the subsampling ratio is 4\%. This is the first time, we are testing our interpolation ideas on real 3D marine seismic data acquisition. Our findings show that we can avoid the general practice of windowing the data while performing the interpolation, specially using rank-minimization based framework.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.mov},
	author = {Rajiv Kumar and Nick Moldoveanu and Keegan Lensink and Felix J. Herrmann}
}
@presentation {lopez2017SINBADFagf,
	title = {A Guide for Successful Low-Rank Matrix Recovery in Seismic Applications},
	year = {2017},
	publisher = {SINBAD},
	abstract = {This talk presents recent results in the theory of low-rank matrix recovery as heuristics for seismic practitioners. In the theory of matrix completion, we discuss the spectral gap as a means to quantify how successful a given sub sampling scheme will be for trace interpolation. Additionally, we consider previously proposed random sampling techniques and develop conditions on the sampling distribution that guarantees successful low-rank matrix recovery. The results apply to time-jittered acquisition, off-the-grid trace interpolation and source separation for simultaneous towed-streamer marine acquisition. Put together, the talk provides practical instruments that help design acquisition schemes in favor of rank penalization techniques.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFagf/lopez2017SINBADFagf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFagf/lopez2017SINBADFagf.mov},
	author = {Oscar Lopez and Rajiv Kumar}
}
@presentation {herrmann2017SINBADFhrc,
	title = {Highly repeatable 3D compressive full-azimuth towed-streamer time-lapse acquisition {\textendash}- a numerical feasibility study at scale},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Most conventional 3D time-lapse (or 4D) acquisitions are ocean-bottom cable (OBC) or ocean-bottom node (OBN) surveys since these surveys are relatively easy to replicate compared to towed-streamer surveys. To attain high degrees of repeatability, survey replicability and dense periodic sampling has become the norm for 4D surveys that renders this technology expensive. Conventional towed-streamer acquisitions suffer from limited illumination of subsurface due to narrow azimuth. Although, acquisition techniques such as multi-azimuth, wide-azimuth, rich-azimuth acquisition, etc., have been developed to illuminate the subsurface from all possible angles, these techniques can be prohibitively expensive for densely sampled surveys. This leads to uneven sampling, i.e., dense receiver and coarse source sampling or vice-versa, in order to make these acquisitions more affordable. Motivated by the design principles of Compressive Sensing (CS), we acquire economic, randomly subsampled (or compressive) and simultaneous towed-streamer time-lapse data without the need of replicating the surveys. We recover densely sampled time-lapse data on one and the same periodic grid by using a joint-recovery model (JRM) that exploits shared information among different time-lapse recordings, coupled with a computationally cheap and scalable rank-minimization technique. The acquisition is low cost since we have subsampled measurements (about 70\% subsampled), simulated with a simultaneous long-offset acquisition configuration of two source vessels travelling across a survey area at random azimuths. We analyze the performance of our proposed compressive acquisition and subsequent recovery strategy by conducting a synthetic, at scale, seismic experiment on a 3D time-lapse model containing geological features such as channel systems, dipping and faulted beds, unconformities and a gas cloud. Our findings indicate that the insistence on replicability between surveys and the need for OBC/OBN 4D surveys can, perhaps, be relaxed. Moreover, this is a natural next step beyond the successful CS acquisition examples discussed during this session.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFhrc/herrmann2017SINBADFhrc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFhrc/herrmann2017SINBADFhrc.mov},
	author = {Felix J. Herrmann and Rajiv Kumar and Haneet Wason and Shashin Sharan and Felix Oghenekohwo}
}
@presentation {yang2017SINBADFiwm,
	title = {Imaging with multiples in shallow water},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Based on the latest developments of research in inversion technology with optimization, researchers have made significant progress in the implementation of least-squares reverse-time migration (LS-RTM) of primaries. In Marine data however, these applications rely on the success of a pre-imaging separation of primaries and multiples, which can be modeled as a multi-dimensional convolution between the vertical derivative of the surface-free Green{\textquoteright}s function and the down-going receiver wavefield. Instead of imaging the primaries and multiples separately, we implement the LS-RTM of the total down-going wavefield by combining areal source injection and linearized Born modelling, where strong surface related multiples are generated from a strong density variation at the ocean bottom. The advantage including surface related multiples in LS-RTM is the extra illumination we obtain from these multiples without incurring additional computational costs related to carrying out multi-dimensional convolutions part of conventional multiple prediction procedures. Even though we are able to avert these computational costs, our approach shares the large costs of LS-RTM. We reduce these costs by combining randomized source subsampling with our sparsity-promoting imaging technology, which produces artifact-free, high-resolution images, with the surface-related multiples migrated properly.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/yang2017SINBADFiwm/yang2017SINBADFiwm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/yang2017SINBADFiwm/yang2017SINBADFiwm.mov},
	author = {Mengmeng Yang and Emmanouil Daskalakis and Felix J. Herrmann}
}
@presentation {witte2017SINBADFals,
	title = {A large-scale framework in Julia for fast prototyping of seismic inversion algorithms},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present our progress on a large-scale seismic modeling workflow in Julia for wave-equation based inversion. The software offers a range of high-level abstractions to easily express PDE constrained optimization problems in terms of linear algebra expressions, while utilizing the DSL Devito to symbolically express the underlying PDEs and to generate fast and parallel code for solving them. Data containers and linear operators can be set up without much effort from input SEG-Y data and scale to large-scale 3D applications. This talk provides an overview of the basic functionalities of our software and applications to least squares imaging and 3D FWI.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/witte2017SINBADFals/witte2017SINBADFals.mov},
	author = {Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {louboutin2017SINBADFldi,
	title = {Latest developments in Devito},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present an overview of the latest developments in Devito. We introduced Devito in the previous meeting as a prototype finite-difference DSL for seismic modelling and inversion. We are presenting here the latest improvements and functionalities of Devito. We will also discuss the current future plans as well as non-supported features that the audience may be interested in. This presentation will be followed by/mixed with a hands-in tutorial if the time and resources allows it.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/louboutin2017SINBADFldi/louboutin2017SINBADFldi.mov},
	author = {Mathias Louboutin and Michael Lange and Fabio Luporini and Navjot Kurjeka and Jan Hueckelheim and Gerard Gorman and Philipp A. Witte and Felix J. Herrmann}
}
@presentation {graff2017SINBADFlrp,
	title = {Low-rank representation of omnidirectional subsurface extended image volumes},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Extended image volumes are an important migration tool in seismic exploration. However the computation and the storage of omnidirectional subsurface extended image volumes are usually prohibitive. That is why some solutions have been already proposed for instance by focusing on horizontal offsets only. In our work, we will consider a linear algebra approach to deal with the low-rank representation of extended image volumes with full offsets. We will never build entirely the resulting matrix but get only actions of it on well-chosen probing vectors, based on Low-Rank decomposition or randomized SVD. This representation allows us to have access to all the energy of the extended image volume matrix and still limits the storage of the information and the computational cost.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/graff2017SINBADFlrp/graff2017SINBADFlrp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/graff2017SINBADFlrp/graff2017SINBADFlrp.mov},
	author = {Marie Graff-Kray and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {zhang2017SINBADFmsd,
	title = {Massive seismic data compression \& recovery w/ on-the-fly data extraction},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Industrial seismic exploration has moved towards complex geological areas, which requires typically long-offset and dense sampling data in order to avoid aliasing and inaccuracy in wave-equation based inversion algorithms. These strict requirements lead to massive data volume size and prohibitive demands on computational resources. In this work, we propose to compress our dense data in hierarchical Tucker tensor format by exploiting the low-rank structure of the data in a transformed domain. Then, we devise on-the-fly common shot or receiver gather extraction directly via the highly compressed factors. In subsampling scenarios, by interpolating this novel tensor format, we can also reconstruct the shot or receiver gather on a per-query basis rather than expanding the data to its fully-sampled form. We demonstrate the effective performance of our proposed technique on 3D stochastic full-waveform inversion, which allows the stochastic algorithm to extract shot gathers as it requires them throughout the inversion process. Moreover, we finally show how to computational effectively generate the CIGs from this compressed low-rank tensor representation of the data with the help of fast simultaneous shot or receiver gather generation.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/zhang2017SINBADFmsd/zhang2017SINBADFmsd.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/zhang2017SINBADFmsd/zhang2017SINBADFmsd.mov},
	author = {Yiming Zhang and Curt Da Silva and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {lopez2017SINBADFmci,
	title = {Matrix Completion in Parallel Architectures: Julia Implementation},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Matrix completion techniques offer potential tools for frugal seismic data acquisition, where dense acquisition is replaced by optimization. This shift of focus means that efficient numerical methods are critical for the implementation of these techniques in large-scale seismic applications. To this end, this talk modifies rank-penalization methodologies to suit parallel architectures. By adopting factorization-based alternating minimization schemes, each program can be decoupled into independent sub-problems handled in parallel. We showcase a distributed parallel execution in Julia and explore the scalability of the approach.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.mov},
	author = {Oscar Lopez and Keegan Lensink and Rajiv Kumar and Henryk Modzelewski}
}
@presentation {kumar2017SINBADFmdt,
	title = {Multi-domain target-oriented imaging using extreme-scale matrix factorization},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this work, we present an alternative approach to redatum both source and receivers at depth, under the framework of reflectivity-based extended images with two-way wave propagation in the background medium. We propose a randomized svd based probing scheme that takes advantage of the algebraic structure of the extended imaging system to overcome the computational cost and memory usage associated with the number of wave-equation solutions and explicit storage employed by conventional migration methods. Experimental results on complex geological models demonstrate the efficacy of proposed methodology in performing multi-domain target imaging.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFmdt/kumar2017SINBADFmdt.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFmdt/kumar2017SINBADFmdt.mov},
	author = {Rajiv Kumar and Marie Graff-Kray and Ivan Vasconcelos and Felix J. Herrmann}
}
@presentation {wang2017SINBADFnra,
	title = {Noise robust and time-domain formulations of Wavefield Reconstruction Inversion},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We propose a wave-equation-based subsurface inversion method that in many cases is more robust than conventional Full-Waveform Inversion. The new formulation is written in a denoising form that allows the synthetic data to match the observed ones up to a small error. Compared to regular Full-Waveform Inversion, our method treats the noise arising from the data meassuring/recording process and that from the synthetic modelling process separately. Compared to Wavefields Reconstruction Inversion, the new formulation mitigates the difficulty of choosing the penalty parameter λ. To solve the proposed optimization problem, we develop an efficient frequency domain algorithm that alternatively updates the model and the data. Numerical experiments confirm strong stability of the proposed method by comparisons between the results of our algorithm with that from both plain FWI and a weighted formulation of the FWI. We also discuss a new memory efficient time-domain formulation for Wavefield Reconstruction Inversion based on duality.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/wang2017SINBADFnra/wang2017SINBADFnra.mov},
	author = {Rongrong Wang and Mathias Louboutin and Bas Peters and Emmanouil Daskalakis and Felix J. Herrmann}
}
@presentation {herrmann2017SINBADFofp,
	title = {Overview \& Future Plans SINBAD Consortium},
	year = {2017},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFofp/herrmann2017SINBADFofp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/herrmann2017SINBADFofp/herrmann2017SINBADFofp.mov},
	author = {Felix J. Herrmann}
}
@presentation {fang2017SINBADFpfg,
	title = {PDE-free Gauss-Newton Hessian for Wavefield Reconstruction Inversion},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this work, we present a PDE-free Gauss-Newton Hessian for Wavefield Reconstruction Inversion. With this PDE-free Gauss-Newton Hessian, we can compute matrix-vector products without additional PDE solves. Thus, we are able to use the second order optimization method Gauss-Newton method with a roughly equal computational cost of first-order methods such as the gradient-descent method.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/fang2017SINBADFpfg/fang2017SINBADFpfg.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/fang2017SINBADFpfg/fang2017SINBADFpfg.mov},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@presentation {alfaraj2017SINBADFros,
	title = {Reconstruction of S-waves from low-cost randomized acquisition},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier according to the Nyquist sampling criterion. To avoid higher acquisition costs and to utilize the multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. We present two up- and down-going shear wave reconstruction methods: (i) rank minimization reconstruction followed by elastic wavefield decomposition, and (ii) sparsity promoting joint interpolation decomposition using all the multicomponent data in one optimization problem.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.mov},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {siahkoohi2017SINBADFsdi,
	title = {Seismic data interpolation with Generative Adversarial Networks},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this project we implement an algorithm to predict the missing traces in the seismic shot gathers. The missing traces can be either regular or irregular. Any interpolation scheme assumes a prior knowledge on the data. Here the prior information used to interpolate the data is obtained from interaction of two trained deep neural networks, namely Generator and Discriminator. The combination of these two neural networks is called Generative Adversarial Network (GAN). GAN is trained on finely sampled seismic shot gathers. By employing the trained GAN we can project shot gathers with missing traces into the domain of the generator network. Then by computing the output of generator given the found projection, we can fill in the initial gather.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/siahkoohi2017SINBADFsdi/siahkoohi2017SINBADFsdi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/siahkoohi2017SINBADFsdi/siahkoohi2017SINBADFsdi.mov},
	author = {Ali Siahkoohi and Felix J. Herrmann}
}
@presentation {daskalakis2017SINBADFsof,
	title = {Stochastic Optimization from the perspective of dynamical systems},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present improvements to a family of methods (Linearized Bregman, Kaczmarz and Stochastic Gradient Descent) that are often use in optimization problems. We explain the link of those methods with dynamical systems and we draw ideas for improving their performance. We use a simple idea to improve the stability and the performance of our family of optimization methods especially for ill-posed, inconsistent large-scale problems. Finally we present an application at a least squares migration problem, which highlight the importance of the suggested improvements on large scale Geophysical problems.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/daskalakis2017SINBADFsof/daskalakis2017SINBADFsof.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/daskalakis2017SINBADFsof/daskalakis2017SINBADFsof.m4v},
	author = {Emmanouil Daskalakis and Rachel Kuske and Mengmeng Yang and Felix J. Herrmann}
}
@presentation {sharan2017SINBADFtts,
	title = {Tracking the spatial-temporal evolution of fractures by microseismic source collocation},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Unlike conventional reservoirs, unconventional plays are not naturally viable for economical production of oil and gas. They require stimulation by injecting high-pressure fluid causing fractures in the rocks. These fractures make the medium more permeable, hence, the extraction of oil and gas becomes feasible. For drilling purposes and to prevent potentially hazardous situations, we need to have good knowledge of the location of these fractures. Also, we need to have good knowledge about how these fractures originated in time. Hydraulic fracturing changes stress in rocks, which results in the emission of microseismic waves. The opening of cracks due to high pressure fluid injection during hydraulic fracturing mainly causes this change in stress in the rocks. Therefore, microseismic events are mostly localized along these fractures and have finite energy along time. To accurately track the evolution of fractures in both space and time, we need to locate closely spaced microseismic events along these fractures activating at very small time intervals. A naive approach can be the back propagation of the observed data to find out a point in space and time where maximum focusing of back propagating energy occurs. This point corresponds to the location and origin time of a microseismic source. This approach, although simpler, suffers from low resolution and requires scanning of complete 4D volume (3D in space and 1D in time). Hence, this method can be challenging when there are multiple closely spaced microseismic sources originating at different times. We in this work propose a sparsity promotion based method that can locate closely spaced microseismic events, with spatial separation as low as within half a wavelength, activating at small time intervals. We simultaneously estimate the origin time of microseismic events by estimating their source time functions. Our method exploits the fact that microseismic events are localized in space and have finite energy. We use accelerated Linearized Bregman algorithm with a preconditioning operator to arrive at a computationally feasible scheme.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/sharan2017SINBADFtts/sharan2017SINBADFtts.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@presentation {bougher2016SINBADFaaa,
	title = {Amplitude vs. angle analysis as an unsupervised learning problem},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Amplitude vs. angle analysis (AVA) of pre-stack seismic data is a commonly used method for inferring petrophysical information from seismic data. Conventionally, a two-term linearized rock physics model (Shuey equation) is used to invert angle-domain common-image gathers. Multivariate analysis of the inverted terms leads to a background of siliciclastic interfaces, where outlying points are associated with hydrocarbon saturated sands. The acquisition and processing of seismic data does not result in highly-calibrated measurements that adhere to the rock physics model, which often inhibits the success of AVA analysis. We offer an alternative approach that uses PCA-based methods to learn projections directly from the data without the need of a physical model. Results on synthetic and field data show that PCA-based projections can improve segmentation of potential reservoirs in seismic data.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/bougher2016SINBADFaaa/bougher2016SINBADFaaa.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/bougher2016SINBADFaaa/bougher2016SINBADFaaa.mov},
	author = {Ben B. Bougher and Felix J. Herrmann}
}
@presentation {oghenekohwo2016SINBADFast,
	title = {Asymmetric sampling for time-lapse surveys},
	year = {2016},
	publisher = {SINBAD},
	abstract = {In this talk, we use the joint recovery model (JRM), which derives from distributed compressed sensing, to reconstruct time-lapse data acquired via time-jittered marine sources that are significantly subsampled in the monitor survey(s) in comparison to the baseline survey, hence, saving acquisition cost. We analyze the effects of two or more asymmetrically subsampled monitor surveys on wavefield reconstruction via JRM. We leverage the common information shared amongst the vintages during our sparsity-promoting recovery.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.mov},
	author = {Felix Oghenekohwo and Haneet Wason and Felix J. Herrmann}
}
@presentation {dasilva2016SINBADFccs,
	title = {Composite convex smooth optimization with seismic data processing applications},
	year = {2016},
	publisher = {SINBAD},
	abstract = {In this work, we show a general technique for solving optimization problems that are comprised of minimizing a composition of a convex, non-smooth function with a smooth function. We demonstrate this technique in the seismic data processing context applied to robust missing trace interpolation in the low-rank Hierarchical Tucker tensor format as well as cosparsity-based missing trace interpolation.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFccs/dasilva2016SINBADFccs.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFccs/dasilva2016SINBADFccs.mov},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {herrmann2016SINBADFcvp,
	title = {Constraints versus penalties for edge-preserving full-waveform inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Full-waveform inversion is challenging in complex geological areas. Even when provided with an accurate starting model, the inversion algorithms often struggle to update the velocity model. Contrary to other areas in applied geophysics, including prior information in full-waveform inversion is still relatively in its infancy. In part this is due to the fact that incorporating prior information that relates to geological settings where strong discontinuities in the velocity model dominate is difficult, because these settings call for non-smooth regularizations. We tackle this problem by including constraints on the spatial variations and value ranges of the inverted velocities, as opposed to adding penalties to the objective as is more customary in main stream geophysical inversion. By demonstrating the lack of predictability of edge-preserving inversion when the regularization is in the form of an added penalty term, we advocate the inclusion of constraints instead. Our examples show that the latter lead to more predictable results and to significant improvements in the delineation of Salt bodies. This is joint work with Bas Peters.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFcvp/herrmann2016SINBADFcvp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFcvp/herrmann2016SINBADFcvp.mov},
	author = {Felix J. Herrmann and Bas Peters}
}
@presentation {peters2016SINBADFcnc,
	title = {Convex \& non-convex constraint sets for full-waveform inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {We extend some of our previous work on constrained formulations of full-waveform inversion. Some motivating examples illustrate why solving constrained problems directly is a simpler approach for solving non-linear inverse problems than penalized problem formulations. So far we used constraints which can be represented as convex sets, for which most optimization theory is developed. Some non-convex counterparts of convex sets are introduced and examples show that the algorithms developed for convex sets also work well with non-convex sets. The motivation to also explore non-convex sets, is that they sometimes translate prior geological knowledge into mathematical constraints in a more direct way. All presented material is available as a user-friendly toolbox. The toolbox works with any code which can provide a data-misfit function value and a gradient direction.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/peters2016SINBADFcnc/peters2016SINBADFcnc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/peters2016SINBADFcnc/peters2016SINBADFcnc.mov},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {fang2016SINBADFeaq,
	title = {Efficient approach for quantifying uncertainty of wavefield reconstruction inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Due to the noisy measurements, sparse observations, uncertain forward models, and uncertain prior parameter information, significant uncertainty arises in the inverted velocity model of full-waveform inversion. The uncertain velocity model leads to the uncertainty in the correct positions of events in the migrated images, which subsequently influences the important decision-makings in the oil and gas exploration and production (E\&P) business such as the drilling decisions and economic evaluations. Therefore, understanding the uncertainty in the inverted velocity model is essential for the mitigation of the E\&P risks. A common approach to accounting for uncertainty in seismic inverse problems like full-waveform inversion is to describe the unknown parameters in probabilistic by the Bayesian inference. The Bayesian inference aims at incorporating the information from the data, physics, and one{\textquoteright}s prior knowledge to formulate a posterior distribution function to characterize the statistical properties of the unknown parameters. However, characterizing uncertainties for the large-scale full-waveform inversion in seismic exploration is prohibitively expensive since it requires thousands of function evaluations to sample the parameter space (e.g. via Markov chain Monte Carlo sampling). Apart from this, the existing methods of uncertainty quantification for full-waveform inversion is through applying the Bayesian inference to the standard method such as the adjoint-state method with the assumption that no uncertainty arises from the wave-equation. However, the wave-equation may not be able to reflect all the physics, hence, incorporating uncertainty from wave-equation might benefit the quantification of uncertainty. In this work, we propose to perform uncertainty quantification using the recently proposed wavefield reconstruction inversion technique, which gives an appropriate framework to tackle the uncertainty in wave-equation solve by adding the misfit of the wave-equation to the likelihood distribution and relaxing the wave-equation constraint. Moreover, we propose a computationally feasible approach to analyzing the posterior distribution of the wavefield reconstruction inversion method, which incorporates a Gaussian distribution that approximates the posterior distribution with an optimization-driven Gaussian simulator to sample the Gaussian distribution efficiently. Numerical examples show that comparing to the McMC method, our approach is able to produce comparable results with less computational cost. This is joint work with Curt da Silva and Rachel Kuske.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/fang2016SINBADFeaq/fang2016SINBADFeaq.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/fang2016SINBADFeaq/fang2016SINBADFeaq.mov},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Tristan van Leeuwen and Felix J. Herrmann and Rachel Kuske}
}
@presentation {kumar2016SINBADFels,
	title = {Efficient large-scale 5D seismic data acquisition and processing using rank-minimization},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Seismic data collection is becoming challenging because of increased demands for high-quality, long-offset and wide-azimuth data. Leveraging ideas from CS, in this work we establish a cost effective acquisition and processing techniques, which are no longer dominated by survey area size but by the sparsity of seismic data volumes. In the first part of abstract, we establish connections between random time dithering and jittered sampling in space. Specifically, we recover high-quality 5D seismic data volumes from time-jittered marine acquisition where the average inter-shot time is significantly reduced, leading to cheaper surveys due to fewer overlapping shots. The time-jittered acquisition, in conjunction with the shot separation by Singular-Value Decomposition (SVD)-free factorization based rank-minimization approach, allows us to recover high quality 5D seismic data volumes. Results are illustrated for simulations of simultaneous time-jittered continuous recording for a 3D ocean-bottom cable survey, where we outperforms existing techniques, by an order of magnitude computational speedup and using 1/20th of the memory, that use sparsity in transforms domains. The second part of abstract focussed on leveraging low-rank structure in seismic data to solve extremely large data recovery (interpolation) problems. We introduced a large{\textendash}scale SVD-free optimization framework that is robust with respect to outliers and that uses information on the support. We test the efficacy of the proposed interpolation framework on a large-scale 5D seismic data, generated from the geologically complex synthetic 3D Compass velocity model, where 80\% of the data has been removed. Our findings show that major computational and memory gains are possible compared to curvelet-based reconstruction.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFels/kumar2016SINBADFels.mov},
	author = {Rajiv Kumar and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@presentation {sharan2016SINBADFhrm,
	title = {High resolution microseismic source collocation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Microseismic waves generated by hydraulic fracturing are used to locate the fractures created in the reservoir. Most of the existing source collocation methods have their limitations in terms of resolution of very closely spaced microseismic events. In this work we propose a method to collocate microseismic events with high resolution along with simultaneous estimation of source time function. In the context of waves, the finite-difference modelling operator acts as an analysis operator for wavefields. This implies that wavefields under the action of a finite-difference modelling kernel can be focused to sources generating these wavefields. We exploit this property of wavefields to locate microseismic events and estimate the source time function jointly. To arrive at a computational feasible scheme, we use the accelerated version of Linearized Bregman (LBR) algorithm to solve the above mentioned problem. This is joint work with Rongrong Wang.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/sharan2016SINBADFhrm/sharan2016SINBADFhrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/sharan2016SINBADFhrm/sharan2016SINBADFhrm.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@presentation {louboutin2016SINBADFhps,
	title = {High-performance seismic applications of OPESCI},
	year = {2016},
	publisher = {SINBAD},
	abstract = {We present our latest geophysical applications built on OPESCI. By using a high-level symbolic API, we allow for fast development and easy implementation of various (acoustic, VT, TTI) wave propagators relevant to exploration geophysics. We start by highlighting possibilities in an acoustic setting including classical operators such as forward modelling and linearised forward (Born) modelling as well as more advanced operators deriving from wave equations with double dipoles and the application of the PDE to a wavefield instead of applying its inverse. We will also show that the performance (time to solution) of this code is on par with industrial software libraries (10\% faster on the full SEAM model). We finally present our implementation of 3D TTI modelling and its adjoint including out comprehensive testing framework. This is joint work with Gerard Gorman},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/louboutin2016SINBADFhps/louboutin2016SINBADFhps.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/louboutin2016SINBADFhps/louboutin2016SINBADFhps.mov},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@presentation {witte2016SINBADFlst,
	title = {A large-scale time-domain modeling and inversion workflow in Julia},
	year = {2016},
	publisher = {SINBAD},
	abstract = {We present our initial steps towards the development of a large-scale seismic modeling workflow in Julia which provides a framework for wave-equation based inversion methods like full waveform inversion or least squares migration. Our framework is based on the Devito tool for optimized finite difference computations that generates highly optimized code from symbolic PDEs. We aim at developing a flexible workflow which is based on abstract operators and allows coding that is {\textquotedblleft}close to the math{\textquotedblright}, while at the same time relying on extremely fast and efficient, state of the art wave equation solvers.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFlst/witte2016SINBADFlst.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {wason2016SINBADFlcr,
	title = {Low-cost, randomized 3D towed-marine time-lapse seismic acquisition},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Most often conventional 3D towed-marine seismic data acquisition suffers from limited illumination of subsurface due to narrow azimuth. Although, acquisition techniques such as Ocean Bottom Cable (OBC), Ocean Bottom Node (OBN), Rich-Azimuth (RAZ) acquisition, Multi-Azimuth (MAZ) acquisition have been developed to illuminate the subsurface from all possible angles, these acquisition techniques are expensive. Motivated by the design principles of compressed sensing, we acquire randomly subsampled and simultaneous (e.g., simultaneous long offset) 3D towed-marine time-lapse seismic data for few (random) azimuths, and recover densely sampled multiple azimuth baseline and monitor data via sparsity promotion and rank minimization. Our acquisition is low cost since we have subsampled measurements.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/wason2016SINBADFlcr.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wason2016SINBADFlcr/marine_acq_mask.pdf},
	author = {Rajiv Kumar and Felix Oghenekohwo and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@presentation {lopez2016SINBADFlrm,
	title = {Low-rank matrix recovery for parallel architectures},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Low-rank matrix recovery (LRMR) techniques offer potential tools for frugal seismic data acquisition, where dense acquisition is replaced by large-scale optimization. This shift of focus means that efficient numerical methods are critical for successful LRMR implementation in seismic applications. In this talk, we extend the rank-penalization methodology to a parallelizable framework. We adopt a factorization-based alternating minimization scheme and decouple it into an independent system of simpler sub-problems that can be handled in parallel. The methodology is flexible, where the approach can be adapted to the number of workers available. Numerical experiments are conducted to demonstrate that the quality of reconstruction is comparable to existing methods at a fraction of the computational time.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/lopez2016SINBADFlrm/lopez2016SINBADFlrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/lopez2016SINBADFlrm/lopez2016SINBADFlrm.mov},
	author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {kumar2016SINBADFlrm,
	title = {Low-rank methods for on-the-fly slicing \& dicing of seismic data \& image volumes},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Conventional oil and gas fields are increasingly difficult to explore and produce, which calls for more complex wave-equation based inversion (WEI) algorithms that require dense long-offset samplings. These requirements result in an exponential growth in data volumes and prohibitive demands on computational resources. In this work, we propose a fast, resilient, and scalable wave-equation based inversion methodology for both the data and image-domain, which can handle complicated wave physics. First we show that both the data and image domains exhibit low-rank structure in a transform-domain, which can be exploited to compress the dense data or image volumes. Then, by accessing information from the compressed volumes on-the fly, we devise a scalable computational inversion framework driven by gradient calculations, which works with small subsets of source experiments. In the full-waveform inversion context, we demonstrate the efficacy of low-rank interpolation to improve downstream inversion results compared to merely inverting the velocity model using the subsampled data volume directly. We demonstrate the effectiveness of the proposed framework on two different waveform-inversion formulations, specifically performing full-waveform inversion on 5D data set generated using the overthrust model, and wave-equation based migration velocity analysis on the Marmousi model. This is joint work with Curt Da Silva and Yiming Zhang.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/kumar2016SINBADFlrm/kumar2016SINBADFlrm.mov},
	author = {Rajiv Kumar and Curt Da Silva and Yiming Zhang and Felix J. Herrmann}
}
@presentation {gorman2016SINBADFopp,
	title = {Open Performance portablE SeismiC Imaging{\textendash}-OPESCI},
	year = {2016},
	publisher = {SINBAD},
	abstract = {In this project, we introduce OPESCI-FD, a Python package built on symbolic mathematics to automatically generate Finite Difference models from a high-level description of the model equations. We investigate applying this framework to generate the propagator program used in seismic imaging. We implement the 3D acoustic and anisotropic FD scheme as an example and demonstrate the advantages of usability, flexibility and accuracy of the framework. The design of OPESCI-FD aims to allow rapid development, analysis and optimisation of Finite Difference programs. OPESCI-FD is the foundation for continuing development by the OPESCI project team, building on the research presented in this report. This talk concludes by reviewing the further developments that are already under way, as well as the scope for extension to cater for other equations and numerical schemes. This joint work with SINBAD and SENAI CIMATEC and has received additional funding from Intel.},
	keywords = {Presentation, private, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/gorman2016SINBADFopp/gorman2016SINBADFopp.pdf},
	url2 = {https://slim.gatech.edu/Publications/Private/Conferences/SINBAD/2016/Fall/gorman2016SINBADFopp/gorman2016SINBADFopp.mov},
	author = {Gerard Gorman and Marcos de Aguiar and David Ham and Felix J. Herrmann and Paul H. J. Kelly and Navjot Kukreja and Michael Lange and Mathias Louboutin and Fabio Luporini and Paulius Velesko and Vincenzo Pandolfo and Felippe Vieira Zacarias}
}
@techreport {herrmann2016SLBors,
	title = {Overview research at the SINBAD Consortium},
	number = {TR-EOAS-2016-1},
	year = {2016},
	note = {Presented at a seminar at Schlumberger Gould, Cambridge on March 17, 2016.},
	month = {03},
	institution = {UBC},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2016/SLB/herrmann2016SLBors/herrmann2016SLBors.pdf},
	author = {Felix J. Herrmann}
}
@presentation {witte2016SINBADFpve,
	title = {Phase velocity error minimizing scheme for the anisotropic pure P-wave equation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Pure P-wave equations for acoustic modeling in transverse isotropic media are derived by approximating the exact pure P-wave dispersion relation. In this work, we present an alternative to the common Taylor expansion based equations, in which we approximate the exact dispersion relation through a polynomial expansion and determine its coefficients by solving a least squares problem that minimizes the phase velocity error over the complete range of phase angles. We show that this approach is up to an order of magnitude more accurate than other pure P-wave equations at a comparable computational cost.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.mov},
	author = {Philipp A. Witte and Christiaan C. Stolk and Felix J. Herrmann}
}
@presentation {witte2016SINBADFtdl,
	title = {Time-domain least-squares RTM with sparsity promotion on field data},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Least squares reverse time migration (LSRTM) is an inversion based imaging algorithm which can surpress acquisition related artifacts and provides images with increased frequency content, decreased noise level and balanced amplitudes. In this work demonstrate our time-domain LSRTM workflow using various pre-conditioners, source-subsampling and sparsity promotion and show its application to the 2D Machar field data set.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFtdl/witte2016SINBADFtdl.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFtdl/witte2016SINBADFtdl.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {yang2016SINBADFtds,
	title = {Time-domain sparsity-promoting least-squares migration with source estimation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Compared to traditional reverse-time migration (RTM), least-squares RTM (LS_RTM) is able to obtain true amplitude images as solutions of $\ell_2$-norm minimization problems by fitting the synthetic and observed reflection data. The shortcoming is that solutions of these $\ell_2$ problems tend to be overfitted and computationally too expensive. By working with randomized subsets of data only, the computational costs of LS-RTM can be brought down to an acceptable level, producing artifact-free high-resolution images. By including on-the-fly source-time function estimation into the method of Linearized Bregman (LB), we tackle the open issues of these "compressive imaging" in the aspects of algorithmic complexity of solver, guaranteed convergence and source estimation. We also inbvestigate whether the algorithm can be accelerated when we combine LB with the Nesterov method. Application of our algorithm on a 2D synthetic shows that we are able to get high-resolution images, with accurate estimates of the wavelet, for one single data pass.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/yang2016SINBADFtds/yang2016SINBADFtds.m4v},
	author = {Mengmeng Yang and Philipp A. Witte and Zhilong Fang and Felix J. Herrmann}
}
@presentation {wang2016SINBADFtmf,
	title = {Two methods for frequency down-extrapolation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {It is well known that low frequency data are essential to the success of the Full-Waveform Inversion. Field data from a usual marine acquisition, however, typically lack reliable low frequencies due to the existing physical limitations. One research direction focuses on studying low-frequency extrapolation, the process of extending reliable frequency bands of the raw data towards the lower end of the spectrum. We propose two optimization problems, by solving which low frequencies can be made out of the high SNR high frequency component. We present both theoretical explanation and numerical experiments to demonstrate the efficacy as well as limitation of these methods.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wang2016SINBADFtmf/wang2016SINBADFtmf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/wang2016SINBADFtmf/wang2016SINBADFtmf.mov},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@presentation {dasilva2016SINBADFuse,
	title = {A unified 2D/3D software environment for large scale time-harmonic full waveform inversion},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Full Waveform Inversion is a costly and complex procedure, resulting in industrial codebases that are often written entirely in low-level languages, making them hard to understand, maintain, improve, and extend. In this work, we propose a software framework for organizing a 3D FWI environment that helps mitigate design complexities inherent in the problem in a straightforward way while delegating the performance-critical portions to low level languages to maintain efficiency. The result is a software framework that has a unified interface for 2D and 3D and is flexible, efficient, scalable, and demonstrably correct. We demonstrate the effectiveness of this approach on a large scale 3D FWI example.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFuse/dasilva2016SINBADFuse.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/dasilva2016SINBADFuse/dasilva2016SINBADFuse.mov},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {herrmann2016SINBADFwom,
	title = {Welcome \& Overview of the Meeting},
	year = {2016},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFwom/herrmann2016SINBADFwom.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFwom/herrmann2016SINBADFwom.mov},
	author = {Felix J. Herrmann}
}
@presentation {peters2015SINBADF3dw,
	title = {3D WRI},
	year = {2015},
	publisher = {SINBAD},
	abstract = {The Wavefield Reconstruction Inversion (WRI) approach to seismic inversion is a topic of active research in the SLIM group. Several variants have been proposed, all of which rely on the solution of a linear least-squares problem with a Helmholtz discretization. While algorithms based on LU or QR factorization are very efficient for 2D problems, 3D problems required the development of a factorization-free iterative method. This talk introduces the current version of the algorithm, which requires significantly less memory and computation and uses existing Helmholtz solvers as an important building block. We plan to discuss early results of 3-D WRI on a small 3-D synthetic example.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADF3dw/peters2015SINBADF3dw.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADF3dw/peters2015SINBADF3dw.mov},
	author = {Bas Peters and Chen Greif and Felix J. Herrmann}
}
@presentation {kumar2015SINBADFaoi,
	title = {Affordable omnidirectional image volumes extension to 3D},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Image gathers are an important tool for velocity analysis, AVA analysis and targeted imaging, however, the computational cost involving in forming the image volumes makes them prohibitively expensive in case of large scale 3D seismic data acquisition. In this work, we propose the extension of our probing techniques from 2D to 3D to form the extended image volumes efficiently. We show the efficacy of proposed formulation on synthetic data set.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/kumar2015SINBADFaoi/kumar2015SINBADFaoi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/kumar2015SINBADFaoi/kumar2015SINBADFaoi.mov},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {witte2015SINBADFara,
	title = {Anisotropic RTM applied to field data},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk we present our latest reverse time-migration results of the BP Machar data set, a 2D seismic line from the North sea featuring a large chalk dome. In contrast to our prior imaging attempts, we now account for anisotropic effects in the data using our new TTI pure quasi-P wave modeling code in the time domain.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {esser2015SINBADFasd,
	title = {Automatic salt delineation {\textemdash} Wavefield Reconstruction Inversion with convex constraints},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We extend full-waveform inversion by Wavefield Reconstruction Inversion by including convex constraints on the model. Contrary to the conventional adjoint-state formulations, Wavefield Reconstruction Inversion has the advantage that the Gauss-Newton Hessian is well approximated by a diagonal scaling, which allows us to add convex constraints, such as the box- and the edge-preserving total-variation constraint, on the square slowness without incurring significant increases in computational costs. As the examples demonstrate, including these constraints yields far superior results in complex geological areas that contain high-velocity high-contrast bodies (e.g. salt or basalt). Without these convex constraints, adjoint-state and Wavefield Reconstruction Inversion get trapped in local minima for poor starting models.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFasd/esser2015SINBADFasd.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFasd/esser2015SINBADFasd.mov},
	author = {Ernie Esser and Llu{\'\i}s Guasch and Felix J. Herrmann}
}
@presentation {esser2015SINBADasd,
	title = {Automatic salt delineation {\textemdash} Wavefield Reconstruction Inversion with convex constraints},
	year = {2015},
	note = {posthumously presented by Felix J. Herrmann},
	publisher = {SINBAD},
	abstract = {We extend full-waveform inversion by Wavefield Reconstruction Inversion by including convex constraints on the model. Contrary to the conventional adjoint-state formulations, Wavefield Reconstruction Inversion has the advantage that the Gauss-Newton Hessian is well approximated by a diagonal scaling, which allows us to add convex constraints, such as the box- and the edge-preserving total-variation constraint, on the square slowness without incurring significant increases in computational costs. As the examples demonstrate, including these constraints yields far superior results in complex geological areas that contain high-velocity high-contrast bodies (e.g. salt or basalt). Without these convex constraints, adjoint-state and Wavefield Reconstruction Inversion get trapped in local minima for poor starting models.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/esser2015SINBADasd/esser2015SINBADasd.pdf},
	author = {Ernie Esser and Llu{\'\i}s Guasch and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADFcst,
	title = {Comparative study of time-lapse FWI approaches},
	year = {2015},
	publisher = {SINBAD},
	abstract = {I will illustrate the performance of our joint recovery model for time-lapse FWI. Specifically, I will compare our method to other conventional methods, namely, parallel difference and sequential difference FWI. I will illustrate the robustness of our method compared to others especially in the presence of missing data caused by large acquisition gaps. In addition, I will assess the performance of these methods in the detectability of the time-lapse change in synthetic data. Analysis of these methods under different starting FWI model will also be highlighted.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFcst/oghenekohwo2015SINBADFcst.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFcst/oghenekohwo2015SINBADFcst.mov},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {lopez2015SINBADdmc,
	title = {Deterministic matrix completion: applications to seismic trace interpolation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {This talk discusses recent developments in the theory of matrix completion that allow us to analyze how successful a given sub sampling scheme will be for seismic trace interpolation via nuclear norm minimization. By adopting a graph theory perspective, we utilize the spectral gap of our sampling operator as a means to quantify our acquisition design. This approach offers a heuristic technique that is practical and with the potential to achieve trace interpolation via rank penalization in a deterministic manner.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/lopez2015SINBADdmc/lopez2015SINBADdmc.pdf},
	author = {Oscar Lopez and Felix J. Herrmann}
}
@presentation {louboutin2015SINBADFess,
	title = {Extending the search space of time-domain adjoint-state FWI w/ randomized implicit time shifts},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We introduce a modified adjoint-state method for time domain FWI that allows us to extend the research space. As a result, we arrive at a formulation where the sensitivity to cycle skipping is reduced. Our method obtains results with the same computational costs as FWI (The PDE solved is the same) but with significantly reduced memory costs. We use new results in non-convex optimization to justify the method as well as new regularization techniques and stochastic optimization to improve the behavior of the algorithm.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/louboutin2015SINBADFess/louboutin2015SINBADFess.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/louboutin2015SINBADFess/louboutin2015SINBADFess.mov},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@presentation {yang2015SINBADFfis,
	title = {Fast imaging with surface-related multiples: {\textendash} shallow water multiples},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk, we will show surface-related multiples imaging with source estimation. Here the algorithms are based on linearized bregman, and we give the source estimation formulation in this case. With the featured blocked structure of linearized bregman, we can do efficient imaging in straightforward way by randomly choosing sources and frequencies. Another benefit is the simplicity to delivery the algorithm.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.mov},
	author = {Mengmeng Yang and Ning Tu and Felix J. Herrmann}
}
@presentation {herrmann2015SINBADfom,
	title = {Fast {\textquotedblleft}online{\textquotedblright} migration with Compressive Sensing},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present a novel adaptation of a recently developed relatively simple iterative algorithm to solve large-scale sparsity-promoting optimization problems. Our algorithm is particularly suitable to large-scale geophysical inversion problems, such as sparse least-squares reverse-time migration or Kirchoff migration since it allows for a tradeoff between parallel computations, memory allocation, and turnaround times, by working on subsets of the data with different sizes. Comparison of the proposed method for sparse least-squares imaging shows a performance that rivals and even exceeds the performance of state-of-the art one-norm solvers that are able to carry out least-squares migration at the cost of a single migration with all data.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/herrmann2015SINBADfom/herrmann2015SINBADfom.pdf},
	author = {Felix J. Herrmann}
}
@presentation {wang2015SINBADFifw,
	title = {Improving full waveform inversion with spectral extrapolation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We develop a sparsity based frequency extrapolation method to obtain low frequency data from the high frequency ones. As other frequency extrapolation methods, the extrapolation introduces noise due to dispersion and possibly densely distributed reflectors. To mitigate such noise, we incorporate Curvlet coefficient regularization in the extrapolation algorithm. Numerical results demonstrate the effectiveness of the extrapolation in frequency domain FWI in models of moderate complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wang2015SINBADFifw/wang2015SINBADFifw.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wang2015SINBADFifw/wang2015SINBADFifw.mov},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@presentation {wason2015SINBADldr,
	title = {Latest developments in randomized marine acquisition and source separation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present the latest developments in randomized (simultaneous) marine acquisition for time-lapse surveys. We also show a comparison between two source separation algorithms {\textemdash} sparsity-promotion and rank-minimization {\textemdash} for simultaneous data.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/wason2015SINBADldr/wason2015SINBADldr.pdf},
	author = {Haneet Wason and Rajiv Kumar and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADFnso,
	title = {A new software organization for 3D FWI},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this work, I have implemented a scalable and extendable framework for 3D Full Waveform Inversion in Matlab. This approach uses modern software design principles to create a codebase that is easy to understand, maintain, and extend and that also allows for rapid prototyping of new algorithmic ideas that can be easily transferred to large scale problems. The code itself is modularized in a proper way, which allows straightforward testing of each component (e.g., Taylor error test, adjoint tests, etc.). Improvements to the computational kernel (i.e., Helmholtz solves with more efficient matrix-vector products, new preconditioners, etc.) propagate to the entire framework.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFnso/dasilva2015SINBADFans.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFnso/dasilva2015SINBADFsdd.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADogt,
	title = {Off the grid tensor completion for seismic data interpolation},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this work, we extend our previous low-rank tensor techniques for seismic data interpolation to the {\textquotedblleft}off-the-grid{\textquotedblright} case, wherein the data is not acquired on a regularly-spaced grid. This irregular sampling scheme causes the underlying tensor to become high-rank, thus preventing our previous Hierarchical Tucker tensor algorithms from successfully recovering the volume. Instead of applying these techniques naively, we introduce a fictitious, regular grid on which the volume exhibits low-rank behaviour and a resampling operator that interpolates the data from the regular, low-rank domain to the irregular sampling domain. By incorporating these changes in to our previous framework, we can interpolate irregularly sampled data volumes with costs determined primarily by the choice of resampling transform. In our case, we use a non-uniform Fast Fourier Transform, but other choices are possible.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/dasilva2015SINBADogt/dasilva2015SINBADogt.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {lin2015SINBADFosd,
	title = {Our student-driven HPC environment},
	year = {2015},
	publisher = {SINBAD},
	abstract = {A major role of academic environments is to provide learning experiences for students to critically analyze and develop methods, both at a high-level of mathematical rigour, and at a low-enough level of implementation in order to yield experimental results on real datasets. Often these two goals are in conflict with each other, in terms of both learning time and attention. At SLIM, we strive to strike a balance between the two by abstracting away many of the low-level aspects of distributed HPC under a framework that matches syntactically with the mathematics of our field, while exposing enough control parameters for tuning performance characteristics. This talk will touch on the history of our efforts at SLIM, and culminate in an overview of our current method of interacting with the in-house compute cluster.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lin2015SINBADFosd/lin2015SINBADFosd.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {peters2015SINBADFqpf,
	title = {A quadratic-penalty full-space method for waveform inversion},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In PDE-constrained optimization problems other than geophysical full-waveform inversion, full-space optimization methods are commonly used. This type of optimization method updates both the medium parameters and the wavefields, instead of solving wave equations explicitly. In the FWI context, this means the objective function value and gradient can be obtained at very little computational cost. A major obstacle is, however, the requirement to have all wavefields in memory.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFqpf/peters2015SINBADFqpf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFqpf/peters2015SINBADFqpf.mov},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {wason2015SINBADFrtl,
	title = {Randomization of time-lapse marine surveys},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present an extension of our time-jittered (simultaneous) marine acquisition for time-lapse surveys, where we work on more realistic field acquisition scenarios by incorporating irregular spatial grids without insisting on repeatability between the surveys. Since we are always subsampled in both the baseline and monitor surveys, we recover the densely sampled baseline and monitor and then the (complete) 4-D difference from subsampled baseline and monitor data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.mov},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADrdc,
	title = {Recent developments in compressive sensing for time-lapse studies},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present an overview of the advances we have made with respect to extending compressive sensing ideas to time-lapse studies. We show applications in compressive time-lapse seismic data acquisition, imaging and full-waveform inversion.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/oghenekohwo2015SINBADrdc/oghenekohwo2015SINBADrdc.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Ning Tu and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADrii,
	title = {Recent improvements to the 2/3{\textemdash}D imaging \& inversion algorithms in the SLIM software release},
	year = {2015},
	publisher = {SINBAD},
	abstract = {The SLIM software release contains algorithms for frequency domain 2D imaging and inversion based on direct LU and QR factorizations. Recent improvments include significant speedups, by utilizing the latest parallel direct factorization algorithms and inversion strategies which solve for each frequency on its corresponding grid. The iterative Helmholtz solvers available in the software release are now also applicable to 2D problems, extending the 2D algorithms to very large \& high frequency 2D imaging and inversion.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/dasilva2015SINBADrii/dasilva2015SINBADrii.pdf},
	author = {Curt Da Silva and Bas Peters and Felix J. Herrmann}
}
@presentation {peters2015SINBADrwi,
	title = {Regularizing waveform inversion by projections onto convex sets},
	year = {2015},
	publisher = {SINBAD},
	abstract = {A framework is proposed for regularizing the waveform inversion problem by projections onto intersections of convex sets. Multiple pieces of prior information about the geology are represented by multiple convex sets, for example limits on the velocity or minimum smoothness conditions on the model. The data-misfit is then minimized, such that the estimated model is always in the intersection of the convex sets. Therefore, it is clear what properties the estimated model will have at each iteration. This approach does not require any quadratic penalties to be used and thus avoids the known problems and limitations of those types of penalties. It is shown that by formulating waveform inversion as a constrained problem, regularization ideas such as Tikhonov regularization and gradient filtering can be incorporated into one framework. The algorithm is generally applicable, in the sense that it works with any (differentiable) objective function and does not require significant additional computation. The method is demonstrated on the inversion of the 2D marine isotropic elastic synthetic seismic benchmark by Chevron using an acoustic modeling code. To highlight the effect of the projections, we apply no data pre-processing.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/peters2015SINBADrwi/peters2015SINBADrwi.pdf},
	author = {Bas Peters and Brendan R. Smithyman and Felix J. Herrmann}
}
@presentation {peters2015SINBADFrwi,
	title = {Regularizing waveform inversion by projections onto intersections of convex sets},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Common strategies to regularize waveform inversion (and other geophysical inverse problems) are adding quadratic penalty terms to the objective function or filtering the gradients used to update the model estimate. An example are penalties or filters to prevent/filter spurious high spatial frequency oscillations in the model while working with low frequency data. We present an alternative way of regularization, which works by projecting the model onto an intersection of convex sets, where each sets encodes certain desired model properties. This approach has certain theoretical and practical advantages over quadratic penalties or gradient filters. Some examples of useful convex sets in various challenging waveform inversion settings are shown on both real and synthetic data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFrwi/peters2015SINBADFrwi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFrwi/peters2015SINBADFrwi.mov},
	author = {Bas Peters and Brendan R. Smithyman and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {esser2015SINBADFrsa,
	title = {Resolving scaling ambiguities with the L1/L2 norm in a blind deconvolution problem with feedback},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Compared to more mundane blind deconvolution problems, blind deconvolution in seismic applications involves a feedback mechanism related to the free surface. The presence of this feedback mechanism gives us an unique opportunity to remove ambiguities that have plagued blind deconvolution for a long time. While beneficial, this feedback by itself is insufficient to remove the ambiguities even with L1 constraints. However, when paired with an L1/L2 constraint the feedback allows us to resolve the scaling ambiguity under relatively mild assumptions. Inspired by lifting approaches, we propose to split the sparse signal into positive and negative components and apply an \ell_1/\ell_2 constraint to the difference, thereby obtaining a constraint that is easy to implement. Numerical experiments demonstrate robustness to the initialization as well as to noise in the data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.mov},
	author = {Ernie Esser and Rongrong Wang and Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADFsss,
	title = {Scaling SINBAD software to 3-D on Yemoja},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present early results on the scalability of SINBAD{\textquoteright}s wavefield reconstruction and wave-equation based inversion technologies on Yemoja, a 17k core cluster made available to us by BG Group at SENAI CIMATEC Supercomputing Centre in Brazil.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/dasilva2015SINBADFsss/dasilva2015SINBADFsss.mov},
	author = {Curt Da Silva and Haneet Wason and Mathias Louboutin and Bas Peters and Shashin Sharan and Zhilong Fang and Felix J. Herrmann}
}
@conference {lin2015IIPFWIsws,
	title = {Software and workflows at SLIM/SINBAD},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	keywords = {FWI, SLIM, software},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/lin2015IIPFWIsws/lin2015IIPFWIsws_pres.pdf},
	author = {Tim T.Y. Lin and Curt Da Silva and Felix J. Herrmann}
}
@presentation {peters2015SINBADswr,
	title = {Solving WRI{\textquoteright}s data-augmented wave equation in 3D},
	year = {2015},
	publisher = {SINBAD},
	abstract = {The Wavefield Reconstruction Inversion (WRI) approach to seismic inversion is a topic of active research in the SLIM group. Several variants have been proposed, all of which rely on the solution of a linear least-squares problem with a Helmholtz discretization. While algorithms based on LU or QR factorization are very efficient for 2D problems, 3D problems required the development of a factorization-free iterative method. This talk introduces the current version of the algorithm, which requires significantly less memory and computation then a few months ago and uses existing Helmholtz solvers as an important building block.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/peters2015SINBADswr/peters2015SINBADswr.pdf},
	author = {Bas Peters and Chen Greif and Felix J. Herrmann}
}
@presentation {sharan2015SINBADFscu,
	title = {Source collocation using the method of Linearized Bregman},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this work, we present a method to collocate sources from seismic reflections recorded at receiver locations. We use the method of linearized Bregman. This algorithm focuses unknown sources by promoting sparsity in the {\textquotedblleft}Helmholtz domain{\textquotedblright}{\textemdash}i.e., the wavefield under the action of the the Helmholtz system{\textemdash}under the constraint of a data misfit within a particular tolerance. We extend this method to noisy measurements. We are in particular interested in situations where the noise is coherent because it is in the range of the Helmholtz equation.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/sharan2015SINBADFscu/sharan2015SINBADFscu.mov},
	author = {Shashin Sharan and Rongrong Wang and Felix J. Herrmann}
}
@presentation {wason2015SINBADFsss,
	title = {Source separation for simultaneous towed-streamer acquisition via compressed sensing},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We show a comparison between two compressed-sensing based source-separation algorithms, i.e., sparsity promotion and rank minimization, for simultaneous data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFsss/wason2015SINBADFsss.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFsss/wason2015SINBADFsss.mov},
	author = {Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {witte2015SINBADFtdf,
	title = {Time-domain FWI in TTI media},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We develop an inversion workflow for tilted transverse isotropic (TTI) media using a purely acoustic formulation of the wave equation. The anisotropic modeling kernel is used for the forward modeling operator, as well as for the adjoint Jacobian to back propagate the data residual, thus providing the true gradient of the FWI objective function. We apply this workflow on a synthetic FWI example and perform RTM on a field data set. Furthermore, we discuss alternatives for the pseudo-spectral Laplacian operator of the current implementation.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFtdf/witte2015SINBADFtdf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFtdf/witte2015SINBADFtdf.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADFtli,
	title = {Time-lapse imaging with multiples and distributed compressed sensing},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk, we leverage our joint-recovery model, which uses the fact that time-lapse images generally have a lot in common, to improve imaging problems with large gaps. We demonstrate that the adverse effects of large acquisition gaps can be mitigated by using this joint-recovery model especially in combination with imaging with surface-related multiples.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFtli/oghenekohwo2015SINBADFtli.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFtli/oghenekohwo2015SINBADFtli.mov},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {fang2015SINBADFuqw,
	title = {Uncertainty quantification for Wavefield-Reconstruction Inversion using a positive-definite approximated Hessian},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We analyze the Hessian of wavefield reconstruction inversion (WRI) and propose a new approximated Hessian. Instead of requiring PDE solves, the matrix-vector multiplication action of the approximate Hessian can be achieved with several matrix-vector multiplications. The diagonal part of the approximated Hessian can be also calculated without additional PDE solves. We apply this approximated Hessian to uncertainty quantification and obtain statistical parameters such as standard deviation and confidence interval. Numerical example illustrate the accuracy of the estimated Hessian and the feasibility of the method to quantify uncertainties.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFuqw/fang2015SINBADFuqw.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFuqw/fang2015SINBADFuqw.mov},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@presentation {lopez2015SINBADFumc,
	title = {Universal matrix completion: applications to seismic data acquisition},
	year = {2015},
	publisher = {SINBAD},
	abstract = {This talk discusses the potential applications of universal matrix completion for infill management and acquisition design. We consider recent developments in the theory of matrix completion that produce practical tools to quantify how successful a given sub sampling mask will be for seismic trace interpolation via nuclear norm minimization. This approach provides an on the fly infill management system as well as instruments to design acquisition schemes that favor interpolation via rank penalization techniques.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/lopez2015SINBADFumc/lopez2015SINBADFumc.mov},
	author = {Oscar Lopez and Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {fang2015SINBADwri,
	title = {Wavefield reconstruction inversion with source estimation and its application to 2014 Chevron synthetic blind test dataset},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present a robust wavefield reconstruction inversion with source estimation. The source wavelet is estimated with the reconstruction of wavefield simultaneously by solving an extended data augmentation problem. We apply this method to the 2014 Chevron synthetic blind test dataset and show the robustness of our method.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/fang2015SINBADwri/fang2015SINBADwri.pdf},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@presentation {fang2015SINBADFwri,
	title = {Wavefield-reconstruction inversion with source estimation and minimum smoothness constraint {\textendash} application to the Chevron 2014 dataset},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present a robust wavefield reconstruction inversion with source estimation. The source wavelet is estimated with the reconstruction of wavefield simultaneously by solving an extended data-augmented problem. We apply this method to the 2014 Chevron synthetic blind test dataset and show the robustness of our method.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFwri/fang2015SINBADFwri.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFwri/fang2015SINBADFwri.mov},
	author = {Zhilong Fang and Bas Peters and Felix J. Herrmann}
}
@presentation {herrmann2015SINBADFwom,
	title = {Welcome \& Overview of the Meeting},
	year = {2015},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/herrmann2015SINBADFwom/herrmann2015SINBADFwom.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/herrmann2015SINBADFwom/herrmann2015SINBADFwom.mov},
	author = {Felix J. Herrmann}
}
@presentation {li2014SINBADtsi,
	title = {2D/3D time-stepping for imaging and inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk, we present a SLIM version of 2D/3D time-stepping modeling kernel for inversion and imaging. One key aspect of this framework is that the implementation follows some mathematic principles, such as linear operator test. Aside from obeying mathematic principles, we also developed a strategy that allows us to compute the gradient of conventional FWI objective or RTM imaging without storing extra wavefield. We also show some applications of this framework on the Chevron 2014 benchmark elastic synthetic data set.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/li2014SINBADtsi/li2014SINBADtsi.pdf},
	author = {Xiang Li and Felix J. Herrmann}
}
@presentation {petrenko2014SINBADaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	year = {2014},
	publisher = {SINBAD},
	abstract = {An implementation of seismic wave simulation on a platform consisting of a conventional host processor and a reconfigurable hardware accelerator is presented. This research is important in the field of exploration for oil and gas resources, where a 3D model of the subsurface is frequently required. By comparing seismic data collected in a real-world survey with synthetic data generated by simulated waves, it is possible to deduce such a model. However this requires many time-consuming simulations with different Earth models to find the one that best fits the measured data. Speeding up the wave simulations would allow more models to be tried, yielding a more accurate estimate of the subsurface. The reconfigurable hardware accelerator employed in this work is a field programmable gate array (FPGA). FPGAs are computer chips that consist of electronic building blocks that the user can configure and reconfigure to represent their algorithm in hardware. Whereas a traditional processor can be viewed as a pipeline for processing instructions, an FPGA is a pipeline for processing data. The chief advantage of the FPGA is that all the instructions in the algorithm are already hardwired onto the chip. This means that execution time depends only on the amount of data to be processed, and not on the complexity of the algorithm. The main contribution is an implementation of the well-known Kaczmarz row projection algorithm on the FPGA, using techniques of dataflow programming. This kernel is used as the preconditioning step of CGMN, a modified version of the conjugate gradients method that is used to solve the time-harmonic acoustic isotropic constant density wave equation. Using one FPGA accelerator, the current implementation allows seismic wave simulations to be performed over twice as fast, compared to running on one Intel Xeon E5-2670 core. I also discuss the effect of modifications of the algorithm necessitated by the hardware on the convergence properties of CGMN. Finally, a specific plan for future work is set-out in order to fully exploit the accelerator platform, and my work is set in its larger context},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/petrenko2014SINBADaih/petrenko2014SINBADaih.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Diego Oriato and Simon Tilbury and Felix J. Herrmann}
}
@presentation {warner2014SINBADawi,
	title = {Adaptive waveform inversion - FWI without cycle skipping},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Conventional FWI minimises the direct differences between observed and predicted seismic datasets. Because seismic data are oscillatory, this approach will suffer from the detrimental effects of cycle skipping if the starting model is inaccurate. We reformulate FWI so that it instead adapts the predicted data to the observed data using Wiener filters, and then iterates to improve the model by forcing the Wiener filters towards zero-lag delta functions. This adaptive FWI scheme is demonstrated on synthetic data where it is shown to be immune to cycle skipping, and is able to invert successfully data for which conventional FWI fails entirely. The new method does not require low frequencies or a highly accurate starting model to be successful. Adaptive FWI has some features in common with wave-equation migration velocity analysis, but it works for all types of arrivals including multiples and refractions, and it does not have the high computational costs of WEMVA in 3D.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/warner2014SINBADawi/warner2014SINBADawi.pdf},
	author = {Mike Warner}
}
@presentation {yilmaz2014SINBADadc,
	title = {Analog-to-digital conversion in compressive sampling},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Compressive sampling theory is now established as an effective method for dimension reduction when the underlying signals (e.g., seismic data) are sparse with respect to some suitable basis or frame (e.g., curvelets in the case of seismic). One important problem directly related to the acquisition of analog signals is how to perform analog-to-digital conversion. This is directly related to two important issues: (i) how accurately one can acquire a signal using compressive sampling (in terms of bit depth), and (ii) how {\textquoteleft}{\textquoteleft}compressed{\textquoteright}{\textquoteright} is compressive sampling (in terms of the total number of bits one ends up using after acquiring the signal). We will present recent results that provide answers to these questions. Specifically, we provide an analog-to-digital conversion method that achieves nearly optimal compression using cheap analog devices with a large margin for imperfections.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/yilmaz2014SINBADadc/yilmaz2014SINBADadc.pdf},
	author = {Ozgur Yilmaz}
}
@presentation {peters2014SINBADcda,
	title = {Challenges and developments arising from 2D FWI of a land VSP dataset in the Permian Basin},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Full-waveform inversion was carried out to map 3D velocity structures for a site in the Permian Basin of Texas, USA. The data come from co-located surface and 3D VSP seismic surveys that share common source vibration points. This challenging on-land setting has been used as a testbed to evaluate our FWI algorithms and their effectiveness when applied to real-data problems. We discuss the challenges posed by this case study, and several new techniques and methodological refinements that have been developed and tested as a result. These include: regularization and incorporation of multiple datasets in an on-land FWI workflow; restrictions on the types of models allowed (viz., smoothness regularization) by quadratic penalty and by Projected Quasi-Newton (PQN) inversion; corrections to enable 2D FWI and recovery of 3D models; the state of ongoing work in WRI ({\textquoteleft}{\textquoteleft}Penalty Method{\textquoteright}{\textquoteright} inversion) and the handling of anisotropy.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADcda/peters2014SINBADcda.pdf},
	author = {Brendan R. Smithyman and Bas Peters and Felix J. Herrmann}
}
@presentation {lin2014SINBADdag,
	title = {Dealing with acquisition gaps in Robust EPSI without interpolation using wavefield auto-convolution},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Straightforward modifications to the Estimation of Primaries by Sparse Inversion (EPSI) problem allows us to mitigate acquisition holes without any reconstruction of the missing traces, neither prior to nor during the inversion process. This is achieved by simulating the missing multiple contributions with terms involving auto-convolutions of the primary wavefield. In this formulation we no longer need to treat the missing data as another unknown in the inversion process, which is important in eliminating a significant source of possible local minima from attempting to invent data using incorrect primary and multiple models. In this talk we investigate the necessary modifications to the Robust EPSI problem, as well as algorithms that account for the resulting non-linear modelling operator. We will also investigate the reconstruction limits of this approach in relation to acquisition geometry and subsurface characteristics.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lin2014SINBADdag/lin2014SINBADdag.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {wang2014SINBADdwi,
	title = {Denoising the wavefield inversion problem through source blending and penalty method},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Previous FWI denoising techniques, such as source blending and stacking, are designed applied to the seismic traces. Hence no physical information is used to improve the fidelity of the reconstruction, even when a good initial model is available. Moreover, traditional source blending requires the existence of common source gathers, which might be missing from the data. In this talk, we incorporate the source blending idea into the penalty method, and extract the synthetic sources corresponding to the principle directions of the gradient update. By blending the sources, we can not only achieve an acceleration and storage efficiency of the penalty method, but also make the reconstruction more robust to the random noise. This method is applicable to any source receiver position but is more efficient if common source gathers are available.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wang2014SINBADdwi/wang2014SINBADdwi.pdf},
	author = {Rongrong Wang and Felix J. Herrmann}
}
@presentation {herrmann2014SINBADdns,
	title = {DNOISE III {\textendash} the next step},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this presentation I will discuss our plans for the NSERC Collaborative Research and Development Grant DNOISE III. With this grant, we aim to match the industry contributions of SINBAD dollar-for-dollar.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADdns/herrmann2014SINBADdns.pdf},
	author = {Felix J. Herrmann}
}
@presentation {zheglova2014SINBADead,
	title = {Exploring applications of depth stepping in seismic inverse problems},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We are exploring applications of stable depth extrapolation with the full wave equation to imaging and inversion. Depth stepping with full wave equation can be advantageous to the time and frequency domain modelling if special care is taken to stabilize the depth exptrapolator efficiently, since it reduces the higher dimensional modelling problem to a number of lower dimensional subproblems. We are interested in exploring applications in inversion, modelling and imaging. For example, just as the reverse time migration can be shown to be the gradient of the reduced formulation of the full waveform inversion problem, it is interesting to explore whether a formulation of the inversion problem can be achieved whose gradient can be computed using depth stepping techniques. We are also interested in such applications as preconditioning of iterative methods for Helmholtz equation and imaging.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/zheglova2014SINBADead/zheglova2014SINBADead.pdf},
	author = {Polina Zheglova and Felix J. Herrmann}
}
@presentation {kumar2014SINBADeia,
	title = {Extended images in action {\textendash}- image gather with surface-related multiples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Common image gathers are used in building velocity models, invert anisotropy parameters and to analyze reservoir attributes. Often primary reflections are used to form the image gathers and multiples are typically attenuated in processing. However, researchers have shown that, if correctly used, they can provide useful information about the subsurface during reverse-time migration. In this work, i will show how we can use multiples along with primaries to form the image-gathers.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/kumar2014SINBADeia/kumar2014SINBADeia.pdf},
	author = {Rajiv Kumar and Ning Tu and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {tu2014SINBADfis2,
	title = {Fast imaging with source estimation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In seismic imaging, an inaccurate estimate of the source wavelet may result in degraded seismic images. While conventional reverse-time migration requires the knowledge of the source wavelet as prior information, we propose to include source estimation in the fast compressive imaging procedure. Using the proposed method, we can obtain seismic images that are comparable to those imaged with the true source wavelet, with computational costs that are comparable to conventional RTM images with all the data. We also extend the proposed method to image data with surface-related multiples, where these multiples help to mitigate the amplitude ambiguity in source estimation. We verify the proposed method using realistic synthetic examples.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADfis2/tu2014SINBADfis2.pdf},
	author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {tu2014SINBADfis,
	title = {Fast imaging with surface-related multiples by sparse inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {When used correctly, surface-related multiples can provide extra illumination coverage compared with primaries. In this talk, I will discuss how to jointly image primaries and surface-related multiples in a computationally efficient fashion. We bring down the computational cost by two means. First we use wave-equation solvers to implicitly carry out the expensive dense matrix-matrix multiplications in the prediction of surface-related multiples. Second we bring down the simulation cost by subsampling the frequencies and monochromatic source experiments together with curvelet-domain sparsity-promoting and rerandomization. As a result, we obtain true-amplitude least-squares migrated seismic images with computational costs that are comparable to a single RTM with all the data. We demonstrate the efficacy of the proposed method using realistic synthetic examples.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADfis/tu2014SINBADfis.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {lago2014SINBADhfw,
	title = {Heuristics in full-waveform inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {For many full-waveform inversion techniques, the most computationally intensive step is the computation of a numerical solution for the wave equation on every iteration. In the frequency domain approach, this requires the solution of very large, complex, sparse, ill-conditioned linear systems. In this abstract we bring out attention specifically to CGMN method for solving PDEs, known for being flexible (i.e. it is able to treat equally acoustic data as well as visco-elastic or more complex scenarios) efficient with respect both to memory and computation time, and controllable accuracy of the final approximation. We propose an improvement for the known CGMN method by imposing a minimal residual condition, which incurs in one extra model vector storage. The resulting algorithm called CRMN enjoys several interesting properties as monotonically nonincreasing behaviour of the norm of the residual and minimal residual, guaranteeing optimal convergence for the relative residual criterion. We discuss numerical experiments both in an isolated PDE solve and also within the inversion procedure, showing that in a realistic scenario we can expect a speedup around 25\% when using CRMN rather than CGMN. Joint work with Art Petrenko, Zhilong Fang, Felix J. Herrmann.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/lago2014SINBADhfw/lago2014SINBADhfw.pdf},
	author = {Rafael Lago and Felix J. Herrmann}
}
@presentation {tu2014SINBADind,
	title = {Imaging the Nelson data set using surface-related multiples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We will present out latest finding on migration with multiples on the Nelson dataset.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADind/tu2014SINBADind.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {lin2014SINBADiit,
	title = {Implicit interpolation of trace gaps in REPSI using auto-convolution terms},
	year = {2014},
	publisher = {SINBAD},
	abstract = {It is possible to solve the Estimation of Primaries by Sparse Inversion problem from a sesimic record with large holes without any explicit data reconstruction, by instead simulating the missing multiple contributions with terms involving auto-convolutions of the primary wavefield. Exclusion of the unknown data as an inversion variable from the REPSI process is desireable, since it eliminates a significant source of local minima that arises from attempting to invert for the unobserved traces using primary and multiple models that may be far-away from the true solution. In this talk we investigate the necessary modifications to the REPSI algorithm to account for the resulting non-linear modeling operator, and demonstrate that just a few auto-convolution terms are enough to satisfactorily mitigate the effects of data gaps during the inversion process.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/lin2014SINBADiit/lin2014SINBADiit.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {peters2014SINBADiss,
	title = {Iterative solution strategy for least-squares problem with a PDE-block},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Waveform inversion using the Wavefield Reconstruction Inversion (WRI) is a quadratic-penalty based method developed in our group and has shown to have advantages over the reduced Lagrangian based conventional FWI methods. WRI involves no explicit PDE solves, but a least-squares problem with a PDE block. This type of least-squares problem is very challenging to solve using iterative methods. In this talk we show algorithms we developed specifically for this application and which involve a combination of preconditioning, low-rank decomposition and deflation.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADiss/peters2014SINBADiss.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {dasilva2014SINBADlss,
	title = {Large-scale seismic data interpolation in a parallel computing environment},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk will outline the details for a parallel implementation of the SPG-LR algorithm for matrix completion and its applications to large-scale seismic data interpolation. Previous work done in our group on LR-based matrix factorization has been shown to be extremely efficient for seismic data interpolation that fits in memory. We extend the SPGL1 framework that this method is built on to instances where the full seismic data volume cannot entirely fit in the memory of one computational node and must be run in a distributed environment. We will also look at off-the-grid regularization for tensor completion using a non-uniform FFT operator and examine its effects on interpolating an irregularly sampled, 3D seismic frequency slice.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/dasilva2014SINBADlss/dasilva2014SINBADlss.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {herrmann2014SINBADldm,
	title = {Latest developments in marine (4D) acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show the advantages of randomly dithered marine acquisition with ocean bottom nodes and how randomization techniques from compressive sensing affect the way we think about time-lapse surveys. This is joint work with Felix and Haneet.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADldm/herrmann2014SINBADldm.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2014SINBADldm2,
	title = {Latest developments in randomized (4D) acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We will present the latest developments in randomized (4D) seismic data acquisition.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/herrmann2014SINBADldm2/herrmann2014SINBADldm2.pdf},
	author = {Felix J. Herrmann}
}
@presentation {esser2014SINBADlcs,
	title = {A lifted $\ell_1$/$\ell_2$ constraint for sparse blind deconvolution},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We propose a modification to a sparsity constraint based on the ratio of l1 and l2 norms for solving blind seismic deconvolution problems in which the data consist of linear convolutions of different sparse reflectivities with the same source wavelet. No assumptions are made about the location of the support of either the wavelet or the sparse signals. Minimizing the ratio of l1 and l2 norms has been previously shown to promote sparsity in a variety of applications including blind deconvolution. Most existing implementations are heuristic or require smoothing the l1/l2 penalty. Lifted versions of l1/l2 constraints have also been proposed but are still challenging to implement. Inspired by the lifting approach, we propose to split the sparse signals into positive and negative components and apply an l1/l2 constraint to the difference, thereby obtaining a constraint that is easy to implement without smoothing the l1 or l2 norms. We show that a method of multipliers implementation of the resulting model can recover source wavelets that are not necessarily minimum phase and approximately reconstruct the sparse reflectivities. It appears to be robust to the initialization and to small amounts of noise in the data. We also discuss extensions to the Estimation of Primaries by Sparse Inversion (EPSI) convolution model.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/esser2014SINBADlcs/esser2014SINBADlcs.pdf},
	author = {Ernie Esser and Tim T.Y. Lin and Rongrong Wang and Felix J. Herrmann}
}
@presentation {dasilva2014SINBADlrp,
	title = {Low-rank promoting transformations and tensor interpolation - applications to seismic data denoising},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this presentation, we extend our previous work in Hierarchical Tucker (HT) tensor completion, which uses an extremely efficient representation for representing high-dimensional tensors exhibiting low-rank structure, to handle subsampled tensors with noisy entries. We consider a {\textquoteleft}low-noise{\textquoteright} case, so that the energies of the noise and the signal are nearly indistinguishable, and a {\textquoteleft}high-noise{\textquoteright} case, in which the noise energy is now scaled to the amplitude of the entire data volume. For the low-noise case in particular, standard trace-by-trace energy comparisons cannot distinguish noise from signal. We examine the behaviour of noise in terms of the singular values along different matricizations of the data, i.e. reshaping of the tensor along different dimensions. By interpreting this effect in the context of tensor completion, we demonstrate the inefficacy of denoising by this method in the source-receiver domain. In light of this observation, we transform the decimated, noisy data in to the midpoint-offset domain, which promotes low-rank behaviour in the signal and high-rank behaviour in the noise. This distinction between signal and noise allows low-rank interpolation to effectively denoise the signal, without knowledge of the noise location, with only a marginal increase in computational cost. We demonstrate the effectiveness of this approach on a 4D frequency slice.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/dasilva2014SINBADlrp/dasilva2014SINBADlrp.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {lago2014SINBADmri,
	title = {Minimal-residual iterative methods for time-harmonic wave-equation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this work we aim at comparing the performance of some iterative methods for solving the time-harmonic acoustic wave-equation, one of the most challenging problems in the numerical linear algebra community. Widely used in frequency domain full waveform inversion, the discrete wave-equation yields very large, complex, sparse, ill-conditioned linear systems. In this extended abstract we aim at further comparing our recently proposed method, CRMN, against CGMN and another method renown for being very memory efficient, the two-level preconditioner with shifted Laplacian operator. We propose several improvements for the two-level preconditioner by combining this method with CRMN in several senses.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lago2014SINBADmri/lago2014SINBADmri.pdf},
	author = {Rafael Lago and Felix J. Herrmann}
}
@presentation {lin2014SINBADmas,
	title = {Multilevel acceleration strategy for REPSI},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses a multilevel inversion strategy that aims to substantially reduce the computational costs of the Robust Estimation of Primaries by Sparse Inversion algorithm. The proposed method solves early iterations of REPSI at very coarse spatial sampling grids while gradually ramping-up the spatial sampling when more accuracy is desired. No changes to the the core implementation of the original algorithm are necessary while in addition only requiring trace decimation, low-pass filtering, and rudimentary interpolation techniques.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/lin2014SINBADmas/lin2014SINBADmas.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {peters2014SINBADmpw,
	title = {Multi-parameter waveform inversion; exploiting the structure of penalty-methods},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk I consider the problem of inverting waveforms for multiple medium parameters. The governing PDE is chosen to be the Helmholtz equation with compressibility and buoyancy as the unknowns. Both unknowns occur in the same equation and practice has shown it is very hard to estimate both equally accurate; the buoyancy estimate (or density if a slightly different parametrization is used) is typically much smoother than the compressibility (or velocity). Here I introduce a new waveform inversion algorithm: a full Newton-type method based on a penalty method which adds the PDE constraint as a quadratic penalty term. This method updates both the {\textquoteright}wavefields{\textquoteright} and medium parameters, without explicitly solving PDE{\textquoteright}s. One of the main advantages is the availability of a sparse Hessian and exact gradient which are not the result of any PDE solves. We asses if the availability of the Hessian, which includes information about the coupling between the two medium parameters, can help reconstruct both compressibility and buoyancy.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/peters2014SINBADmpw/peters2014SINBADmpw.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {lopez2014SINBADogm,
	title = {Off-the-grid matrix completion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses a modified rank minimization workflow designed to handle unstructured matrix completion problems. We extend current rank minimization based trace interpolation techniques to optimally handle undersampled irregular data, in such a way that allows us to recover regularized densely sampled data. This is achieved by introducing a regularization operator, that accurately allows us to simulate the effect of an unstructured grid. We provide reconstruction error bounds to demonstrate the potential of our procedure.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lopez2014SINBADogm/lopez2014SINBADogm.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@presentation {peters2014SINBADqpb,
	title = {Quadratic-penalty based full-space methods for waveform inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Waveform inversion problems are commonly solved by eliminating the field variables. Full-space methods store and update the fields, rather than solving for them. This leads to a problem with nice properties, such as free function value evaluation and exact gradients and Hessians at no cost. The existing literature is almost exclusively based on the Lagrangian formulation. We propose to work with a quadratic penalty formulation, which allows us to reduce the storage requirements and gives the Newton system a favourable structure which we exploit to obtain a intrinsically parallel updating scheme for the fields and medium parameters. We will show a comparison with reduced-space methods.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADqpb/peters2014SINBADqpb.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {wason2014SINBADrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show how randomization techniques from (distributed) compressed sensing affect the way we think about time-lapse surveys.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wason2014SINBADrrt/wason2014SINBADrrt.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {oghenekohwo2014SINBADrsw,
	title = {Randomized sampling without repetition in time-lapse seismic surveys},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk, we will show a method for acquiring time-lapse data, where we do not have to repeat the survey geometry. Our method works provided our acquisition is randomized, where sources and receivers are at random locations on a computational grid, and provided we know the spatial locations of the shots and receivers. In addition, we show the implications of either (sub)sampling more for the baseline and less for the monitor or vice-versa, and how this sampling scheme affects the 4-D signal.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/oghenekohwo2014SINBADrsw/oghenekohwo2014SINBADrsw.pdf},
	author = {Felix Oghenekohwo and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@presentation {oghenekohwo2014SINBADrst,
	title = {Randomized subsampling in time-lapse surveys and recovery techniques},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk centres on new insights on how we think of acquisition of time-lapse data using ideas from compressed sensing. Specifically, we will show how simultaneous processing of time-lapse data from randomized subsampling gives more reliable estimates of the vintages and time-lapse signal, compared to parallel processing.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/oghenekohwo2014SINBADrst/oghenekohwo2014SINBADrst.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@presentation {lopez2014SINBADrma,
	title = {Rank minimization via alternating optimization},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses the benefits of an alternating optimization approach for seismic trace interpolation via rank minimization. We consider recent factorization approaches to rank minimization problems where we write our low rank matrix in bi-linear form, and modify this workflow by alternating our optimization to handle a single matrix factor at a time. This allows for a more tractable procedure that can better handle highly sub sampled data sets without increasing the time complexity. We demonstrate the potential of this approach with several seismic trace interpolation experiments.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lopez2014SINBADrma/lopez2014SINBADrma.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {herrmann2014SINBADrpe,
	title = {Relax the physics \& expand the search space {\textendash} FWI via Wavefield Reconstruction Inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will present a new formulation of full-waveform inversion that combines the best of full-space constrained methods and reduced-space unconstrained methods. Instead of eliminating the constraint, which leads to the reduced adjoint-state method that underpins most formulations of full-waveform inversion, our method relaxes the PDE constraint by replacing it by an additive (least-squares) penalty term. By using the method of variable projection, we arrive at a formulation that alternates between solving for the wavefield, given the velocity model \& data, and solving for velocity-model updates, given the wavefields. We named this method Wavefield Reconstruction Inversion (WRI) because it inverts for the model updates by reconstructing the wavefield everywhere given data observed at the receivers and the physics of the wave equation. During the talk, we present this new method and discuss how the increased search space, now consisting of the wavefields and model, mitigates the effects of local minima. We will also discuss recent extensions including multi-parameter inversion and regularization. This is joint work with Tristan van Leeuwen, Bas Peters, and Ernie Esser.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADrpe/herrmann2014SINBADrpe.pdf},
	author = {Felix J. Herrmann}
}
@presentation {esser2014SINBADsgp,
	title = {A scaled gradient projection method for total variation regularized full waveform inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We propose a modification to the quadratic penalty formulation for seismic full waveform inversion proposed by van Leeuwen and Herrmann that includes convex constraints on the model. In particular, we show how to simultaneously constrain the total variation of the slowness squared while enforcing bound constraints to keep it within a physically realistic range. Synthetic experiments show that including total variation regularization can improve the recovery of a high velocity perturbation to a smooth background model.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/esser2014SINBADsgp/esser2014SINBADsgp.pdf},
	author = {Ernie Esser and Tristan van Leeuwen and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@presentation {peters2014SINBADsmp,
	title = {Single- and multi-parameter WRI {\textendash}- synthetic examples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We demonstrate the performance of WRI on a number of single- and multi-parameter synthetic examples. The examples shot that WRI is less reliant on accurate starting models while this formulation is also conducive to multi-parameter inversion.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/peters2014SINBADsmp/peters2014SINBADsmp.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {fang2014SINBADsew,
	title = {Source estimation for WRI and its application},
	year = {2014},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/fang2014SINBADsew/fang2014SINBADsew.pdf},
	author = {Zhilong Fang and Xiang Li and Bas Peters and Felix J. Herrmann}
}
@presentation {wason2014SINBADsss,
	title = {Source separation via SVD-free rank-minimization in the hierarchical semi-separable representation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show a source separation algorithm for blended marine acquisition, where two sources are deployed at different depths (over/under acquisition). The separation method incorporates the Hierarchical Semi-Separable (HSS) structure inside rank-regularized least-squares formulations. We also compare this deblending scheme with the sparsity-promoting one-norm minimization scheme.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wason2014SINBADsss/wason2014SINBADsss.pdf},
	author = {Haneet Wason and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {tu2014SINBADsis,
	title = {Sparse inversion simplified},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Implementation of the SPGl1 solver in low-level computer languages such as C or Fortran may sound daunting to many. This talk is tailored for you if you would like to reap benefits from using sparse constraints to regularize your inversion problem, and at the same time want some easy-to-implement algorithm to solve it. We discuss in this talk the linearized Bregman projection method that embraces both ingredients, and show promising applications in fast least-squares imaging and seismic data interpolation problems.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADsis/tu2014SINBADsis.pdf},
	author = {Ning Tu and Navid Ghadermarzy and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {fang2014SINBADsqn,
	title = {A stochastic quasi-Newton McMC method for uncertainty quantification of full-waveform inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this work, we present a fast McMC method using the stochastic l-BFGS Hessian to quantify the uncertainty of full-waveform inversion. Using the stochastic l-BFGS Hessian, we do not need the assumption that the Hessian of data misfit is low rank and we also reduce the computational cost of estimating the Hessian. Numerical result shows the capability of this fast McMC method.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/fang2014SINBADsqn/fang2014SINBADsqn.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Felix J. Herrmann}
}
@presentation {kumar2014SINBADsfm,
	title = {SVD-free matrix completion for seismic data reconstruction},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Seismic data interpolation via rank-minimization techniques has been recently introduced in the seismic community. All the existing rank-minimization techniques assumes the underlying grid to be regular. Irregularity is one of the common impediments in acquisition. In this work, we studied the effect of irregularity on structured and show that how we can modify the existing techniques to handle it. Other then irregularity, we often have missing data. We also show that we can tackle both regularization and interpolation issue simultaneously. The objective of this work is to extend our existing method of interpolation on structured grid to unstructured grid. We illustrate the advantages of the modification in existing methodology using a seismic line from Gulf of Suez to obtain high quality results for regularization and interpolation, a key application in exploration geophysics.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/kumar2014SINBADsfm/kumar2014SINBADsfm.pdf},
	author = {Rajiv Kumar and Oscar Lopez and Ernie Esser and Felix J. Herrmann}
}
@presentation {kumar2014SINBADtjm,
	title = {Time-jittered marine acquisition: low-rank v/s sparsity},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this work we will show how simultaneous or blended acquisition can be setup as a rank-minimization (deblending) problem. The simultaneous acquisition scenario is a pragmatic time-jittered marine acquisition scheme, where a source vessel (with two airgun arrays) sails across an ocean-bottom array firing at jittered source locations and instances in time, resulting in better spatial sampling and acquisition speedup. We make comparisons with sparsity-promoting based techniques and demonstrate that rank-minimization based debelending techniques are computationally faster and memory efficient. This is joint work with Haneet Wason.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/kumar2014SINBADtjm/kumar2014SINBADtjm.pdf},
	author = {Rajiv Kumar and Haneet Wason and Felix J. Herrmann}
}
@presentation {esser2014SINBADtvc,
	title = {Total variation constrained full waveform inversion with continuation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Adding a total variation constraint to the Wavefield Reconstruction Inversion model proposed by van Leeuwen and Herrmann can improve robustness and remove artifacts by encouraging piecewise smooth solutions. However, if the parameter controlling the strength of the TV regularization is not well chosen, important details can be lost. We investigate continuation strategies that gradually reduce the strength of the TV constraint so that these important details are eventually allowed back into the solution. Preliminary numerical experiments suggest that this can improve the solution path and lead to better velocity models than if TV regularization were not used during the intermediate steps.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/esser2014SINBADtvc/esser2014SINBADtvc.pdf},
	author = {Ernie Esser and Llu{\'\i}s Guasch and Tristan van Leeuwen and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@presentation {fang2014SINBADuqw,
	title = {Uncertainty quantification for Wavefield Reconstruction Inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses the uncertainty quantification for the wavefield reconstruction inversion. The Hessian and gradient of the WRI can be easily obtained once the wave field is generated. As a result, we use a Newton type McMC method to sample the posterior distribution. Statistical property such as mean, standard deviation, confidence interval are calculated based on the sampled distribution.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/fang2014SINBADuqw/fang2014SINBADuqw.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@presentation {oghenekohwo2014SINBADuwc,
	title = {Use what{\textquoteleft}s in common: time-lapse FWI with distributed Compressive Sensing},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk, we will extend our joint recovery formulation to FWI in time-lapse seismic, using the modified Gauss-Newton approach. Leveraging ideas from distributed compressive sensing, we exploit shared information in the sparse model perturbations of the baseline and monitor data.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/oghenekohwo2014SINBADuwc/oghenekohwo2014SINBADuwc.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@presentation {herrmann2014SINBADwri,
	title = {Wavefield Reconstruction Inversion (WRI) - a new take on wave-equation based inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We discuss a recently proposed novel method for waveform inversion: Wavefield Reconstruction Inversion (WRI). As opposed to conventional FWI {\textendash}- which attempts to minimize the error between observed and predicted data obtained by solving a wave equation {\textendash}- WRI reconstructs a wave-field from the data and extracts a model-update from this wavefield by minimizing the wave-equation residual. The method does not require explicit computation of an adjoint wavefield as all the necessary information is contained in the reconstructed wavefield. We show how the corresponding model updates can be interpreted physically analogously to the conventional imaging-condition-based approach.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/herrmann2014SINBADwri/herrmann2014SINBADwri.pdf},
	author = {Felix J. Herrmann}
}
@presentation {ghadermarzy2014SINBADwrr,
	title = {Wavefield reconstruction via randomized sampling},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Regular sampling along the time axis is a common approach for seismic imaging. However, seismic data are often spatially undersampled due to economical reasons as well as ground surface limitations. Operational conditions might result in noisy traces or even gaps in coverage and irregular sampling which often leads to image artifacts. Seismic trace interpolation aims to interpolate missing traces in an otherwise regularly sampled seismic data to a complete regular grid. In this talk, we explain some techniques, with low computational complexity, that can be used to improve the results. In particular, we talk about weighting, switching to midpoint-offset domain from frequency-space domain, and uniform versus jittered undersampling. We illustrate the advantages of these modifications using a real seismic line from the Gulf of Suez.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/ghadermarzy2014SINBADwrr/ghadermarzy2014SINBADwrr.pdf},
	author = {Navid Ghadermarzy and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {li2014SINBADwmg,
	title = {Why the modified Gauss-Newton method?},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In our earlier work, we develop the modified Gauss-Newton method for FWI, which requires each update of FWI to be sparse in the curvelet domain. Our empirical observation of the MGN method is that it can find us a solution for FWI problem with sparse perturbation of the initial guess without changing the underlying objective. In this talk, we will analyze the MGN method to find out the reason why it can generate a sparse perturbation of the initial model, because sum of spares updates could easily generate non-sparse perturbations. Moreover, we will illustrate when do we expect the modified Gauss-Newton method to yield a solution with sparse perturbation and in what circumstances should we use it in place of other algorithms like standard Gauss-Newton.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/li2014SINBADwmg/li2014SINBADwmg.pdf},
	author = {Xiang Li and Ernie Esser and Felix J. Herrmann}
}
@presentation {petrenko2013SINBADaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Solution of the Helmholtz equation is the main computational burden of full-waveform inversion in the frequency domain. For this task we employ the CARP-CG algorithm (Gordon \& Gordon 2010), an iterative solver that preconditions the original Helmholtz system into an equivalent symmetric positive definite system and then applies the method of conjugate gradients. Forming the matrix for the new system is not necessary as its multiplicative action on a vector is implemented using a series of projections onto the rows of the original system. Our contribution is implementing CARP-CG for a host + accelerator (FPGA) computing environment. The computational paradigm is one of dataflow: vector and matrix elements are streamed from memory through the accelerator which applies the row projections. The advantage of an FPGA to process streams of data is that unless the algorithm is memory bandwidth limited, computation time is directly proportional to the amount of data. The complexity of the algorithm implemented on the FPGA is irrelevant since all the operations programmed onto the FPGA happen in the same clock tick. In contrast, on a CPU, more complex algorithms require more clock ticks as the instructions are executed sequentially, or with only a small amount of parallelism. Ongoing work porting the CARP-CG algorithm to the accelerator is presented.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/petrenko2013SINBADaih/petrenko2013SINBADaih.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Felix J. Herrmann and Diego Oriato and Simon Tilbury}
}
@presentation {esser2013SINBADasr,
	title = {Applications of phase retrieval methods to blind seismic deconvolution},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Phase retrieval is the non-convex optimization problem of recovering a signal from magnitudes of complex linear measurements. Solving convex semi-definite program (SDP) relaxations has been shown to be a robust approach, but it remains too expensive to apply to large problems. We will discuss methods for accelerating computations and explore applications to seismic deconvolution.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/esser2013SINBADasr/esser2013SINBADasr.pdf},
	author = {Ernie Esser and Felix J. Herrmann}
}
@presentation {lin2013SINBADbre,
	title = {Bootstrapping robust EPSI with coarsely sampled data},
	year = {2013},
	publisher = {SINBAD},
	abstract = {The EPSI method of surface multiple removal directly inverts for the free-surface operator, i.e., the multiple-free Greens function of the subsurface seismic response. One peculiar feature of this approach is the theoretical independence of the spectrum of the free-surface operator from the spectrum of the observed data. The SRME approach requires coarsely sampled data to be low-pass filtered sufficiently in order to avoid aliasing in multiple contribution gathers, which in turn limits the temporal resolution of the demultipled result. Conversely, such limitations in temporal resolution do not directly apply to the inversion solution of EPSI. This property can in turn be exploited both to significantly lower the cost of EPSI and to mitigate the effect of under sampled data in a controlled way.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/lin2013SINBADbre/lin2013SINBADbre.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {hargreaves2013SINBADbfo,
	title = {The bridge from orthogonal to redundant transforms and weighted $\ell_1$ optimization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Traditional arguments in synthesis $\ell_1$ -optimization require our forward operator to be orthogonal, though we use redundant transforms in practice. These traditional arguments do not translate to redundant transforms, and other arguments require impractical conditions on our effective measurement matrix. Recent theory in one-norm analysis, namely the optimal dual $\ell_1$ analysis of Shidong et al, have provided point-wise reconstruction error estimates for synthesis using an equivalence relationship where we can use weaker assumptions. This exposes an important model assumption indicating why analysis might outperform synthesis, for which careful consideration in seismic is necessary, and the need for models such as the cosparse model. In this talk we will discuss these ideas, provide evidence which indicates this theory should generalize to uniform error estimates(and thus not signal dependent), and how redundancy, support information, and weighting play important roles.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/hargreaves2013SINBADbfo/hargreaves2013SINBADbfo.pdf},
	author = {Brock Hargreaves and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {yeung2013SINBADcsr,
	title = {Compressed sensing, recovery of signals using random Turbo matrices},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Compressed sensing is an emerging technique that allows us to recover an image using far fewer number of measurements than classical sampling techniques. Designing the measurement matrices with certain properties are critical to this task. Gaussian matrices are most commonly used. We discover a new class of random matrices that can outperform the Gaussian matrices when we are in a situation of taking an outrageously small number of samples.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/yeung2013SINBADcsr/yeung2013SINBADcsr.pdf},
	author = {Enrico Au-Yeung and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {tu2013SINBADrerand,
	title = {Controlling linearization errors with rerandomization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Least squares migration aims to fit the observed seismic data with data predicted by linearized modelling, by solving an PDE-constrained optimization problem. This problem is challenging mostly because of its prohibitive computational cost. To address the issue, dimensionality reduction techniques were proposed in the literature. However, the solution of the reduced problem can deviate from that of the full problem when there are components in the observed data that cannot be explained by linearized modelling. We solve the problem by rerandomizing our $\ell_1$ regularized inversion. In this presentation, I will explain the method and demonstrate what we can achieve with rerandomization, especially for resolving fine sub-salt structures.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/tu2013SINBADrerand/tu2013SINBADrerand.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {lin2013SINBADcsd,
	title = {Cosparse seismic data interpolation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Over the years we have investigated seismic data interpolation and redatuming algorithms rely on on the assumption that seismic records and images permit sparse approximations under certain representations, such as Curvelet coefficients. Recent findings have suggested that for redundant representations (of which Curvelet is an example), the analysis operator that maps the physical signal to coefficients may also play a crucial role in recovering data from incomplete observations. This insight elevates the significance of a question that often goes unaddressed: is it better for the transform-domain sparsity to be achieved through explicit construction of sparse representations (e.g., by thresholding of small transform-domain coefficients), or by demanding that the algorithm return physical signals which produces sparse coefficients when hit with the forward transform? Recent results show that the two approaches give rise to different solutions when the transform is redundant, and that the latter approach imposes a whole new class of constraints. In particular, the number of zero-valued coefficients given by the analysis operator acting on the signal, referred to as its "cosparsity", have an analogous role to the sparsity of the signal in terms of the coefficients. From this framework, a new reconstruction algorithm is proposed which may allow better reconstruction from subsampled signaled than what the sparsity assumption alone would predict. In this work we apply the new framework and algorithm to the case of seismic data interpolation under the curvelet domain, and show that it admits better reconstruction than some existing L1 sparsity-based methods derived from compressive sensing for a range of subsampling factors. We will also investigate different analysis operators and their impact on both sparsity and cosparsity-based algorithms.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/lin2013SINBADcsd/lin2013SINBADcsd.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {macedo2013SINBADdap,
	title = {A dual approach to PhaseLift via gauge programming and bundle methods},
	year = {2013},
	publisher = {SINBAD},
	abstract = {A feature common to many sparse optimization problems is that the number of variables may be significantly larger than the number of constraints- e.g., the matrix-lifting approach taken by PhaseLift for phase retrieval results in a problem where the number of variables is quadratic in the number of constraints. We consider a duality framework and numerical methods to leverage the relatively small number of constraints. Preliminary numerical results illustrate our approach and its flexibility.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/macedo2013SINBADdap/macedo2013SINBADdap.pdf},
	author = {Ives Macedo and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {oghenekohwo2013SINBADedt,
	title = {Estimating 4D differences in time-lapse using randomized sampling techniques},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Repeatability in the seismic survey and processing has been cited as the main reason for which 4D seismic technology work. In the last decade, concerted efforts have been spent to make the 4D seismic process highly repeatable, without significant success. On the contrary, Compressed Sensing, which is also a relatively new sampling paradigm proposes that one can recover an estimate of a fully sampled signal from a noisy, under-sampled measurements provided the acquisition architecture satisfies some properties. By observing different under-sampled and random measurements from each vintage, corresponding to different acquisition geometries, we show that one can still detect the 4D change in time using recent ideas from Compressed sensing. Using a realistic synthetic model, we show two methods of estimating the 4D difference and compare their relative performance to each other.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/oghenekohwo2013SINBADedt/oghenekohwo2013SINBADedt.pdf},
	author = {Felix Oghenekohwo and Ernie Esser and Felix J. Herrmann}
}
@presentation {peters2013SINBADepm,
	title = {Examples from the penalty-method},
	year = {2013},
	publisher = {SINBAD},
	abstract = {A novel penalty method for PDE-constrained optimization was recently proposed by van Leeuwen and Herrmann (2013). The conventional PDE-constrained optimization formulation used in seismic waveform inversion is based on calculating the gradient of the data misfit objective functional via the adjoint-state method, at the cost of two PDE solves. The penalty method requires only one solution of an overdetermined linear system, in the least-squares sense. In this talk some numerical properties of this linear system will be exposed. The penalty method for PDE-constrained optimization involves a parameter, balancing the data misfit and PDE-misfit parts of the objective functional. This talk will address how to select this very important parameter. Some examples will be shown in which the penalty method outperforms the conventional method in non-linear waveform inversion, as well as linearized seismic imaging by migration.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/peters2013SINBADepm/peters2013SINBADepm.pdf},
	author = {Bas Peters and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {herrmann2013SINBADeia,
	title = {Extended images in action},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Image gathers as a function of subsurface offset are an important tool for migration-velocity and amplitude-versus-angle analysis in areas of complex geology. Traditionally, these gathers are thought of as multidimensional correlations of the source and receiver wavefields. The bottleneck in computing the gathers lies in the fact that one needs to store and compute these wavefields and in correlating the wavefields to obtain the desired image gathers. Therefore, the image gathers are typically only computed for a limited number of subsurface points and for a limited range of subsurface offsets. In this presentation, we offer a new perspective on such gathers by organizing the extended image as a function of all subsurface offsets for all subsurface points in a matrix whose (i,j) entry captures the interaction between gridpoints i and j. Of course, it is infeasible to form and store this matrix. Instead, we propose an efficient algorithm to glean information from the image volume via the action of matrix-vector products with this matrix. We illustrate how this can be used to (i) form conventional image gathers and construct objective functions for automated MVA and (ii) calculate true two-way wave-equation angle gathers that allow us to carry out linearized angle-versus-offset inversion with geologic-dip correction. This is joint work with Tristan van Leeuwen and Rajiv Kumar.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADeia/herrmann2013SINBADeia.pdf},
	author = {Felix J. Herrmann}
}
@presentation {kumar2013SINBADava,
	title = {Extended images in action: efficient AVA via probing},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Common image gathers (CIG) are an important tool to perform AVA analysis in areas of complex geology. Unfortunately, it is prohibitively very expensive to compute these CIG for all the subsurface points. In this work, we present an efficient way to compute CIG for all subsurface offsets without explicitly calculating the source and receiver wavefields for all the sources. Because the CIG contain all possible subsurface offsets, we compute the angle-domain image gathers by selecting the subsurface offset that is aligned with the local geologic dip. We propose a method to compute the local dip information directly from common-image-point gathers. To assess the quality of the angle-domain common-image-points gathers we compute the angle-dependent reflectivity coefficients and compare them with theoretical reflectivity coefficients yielded by the (linearized) Zoeppritz equations for a few synthetic models.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADava/kumar2013SINBADava.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {kumar2013SINBADeia,
	title = {Extended images in action: efficient WEMVA via randomized probing},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Extended images as a function of the full subsurface offset are an important tool to perform wave-equation based migration velocity analysis (WEMVA) in areas of complex geology. Unfortunately, computation \& storage of these extended images is prohibitively expensive. In this work, we present an efficient way to compute extended images for all subsurface offsets without explicitly calculating the source and receiver wavefields for all the sources. Instead, we calculate actions of extended image volumes on probing vectors that live in the image space. The probing can either be defined as vectors from the Dirac basis, which allows us to form the extended image at the location of the point diffractor, or they can be defined in terms of Gaussian noise. The latter corresponds to sources with random weights firing simultaneously at every grid point. We demonstrate that this probing leads to a computationally efficient implementation of WEMVA. This is joint work with Tristan van Leeuwen.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADeia/kumar2013SINBADeia.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {miao2013SINBADfid,
	title = {Fast imaging via depth stepping with the two-way wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In this presentation we propose a fast imaging algorithm via depth stepping with the two-way wave equation. Within the framework of survey sinking, a stabilized depth extrapolation operator is computed using a spectral projector which can efficiently split evanescent wave components. The computation of the spectral projector features with an Hierarchically Semi-Seperable (HSS) matrix representation speeded up polynomial recursion, resulting in an accleration from cubic numerical complexity to linear numerical complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/miao2013SINBADfid/miao2013SINBADfid.pdf},
	author = {Lina Miao and Felix J. Herrmann}
}
@presentation {tu2013SINBADvp,
	title = {Fast imaging with multiples and source estimation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Multiples are usually treated as unwanted components in seismic data. However, if correctly used, they can provide valuable information about the subsurface. In this presentation, I will talk about how to make use of multiples in $\ell_1$ regularized least-squares imaging, and how to estimate the source wavelet on the fly using multiples. Synthetic examples show that using multiples not only helps to retrieve the true-amplitude seismic image and the source wavelet, it also increases the wavenumber contents in the seismic image. By using dimensionality reduction techniques with rerandomization, we also greatly decrease the simulation cost without compromising the image quality.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/tu2013SINBADvp/tu2013SINBADvp.pdf},
	author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {tu2013SINBADfim,
	title = {Fast imaging with multiples and source estimation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {During this talk, we present a computationally efficient (cost of 1-2 RTMs with all data) iterative sparsity-promoting inversion framework where surface-related multiples are jointly imaged with primaries and where the source signature is estimated on the fly. Our imaging algorithm is computationally efficient because it works during each iteration with small independent randomized subsets of data. The multiples are handled by introducing an areal source term that includes the upgoing wavefield. We update the source signature for each iteration using a variable projection method. The resulting algorithm removes imaging artifacts from surface-related multiples, estimates and removes the imprint of the source, recovers true amplitudes, is fast, and robust to linearization errors by virtue of the statistical independence of the subsets of data we are working with at each iteration.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/tu2013SINBADfim/tu2013SINBADfim.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@article {VanLeeuwen11TRfwiwse,
	title = {Fast waveform inversion without source encoding},
	journal = {Geophysical Prospecting},
	volume = {61},
	year = {2013},
	note = {Article first published online: 10 JULY 2012},
	month = {06},
	pages = {10-19},
	abstract = {Randomized source encoding has recently been proposed as a way to dramatically reduce the costs of full waveform inversion. The main idea is to replace all sequential sources by a small number of simultaneous sources. This introduces random crosstalk in the model updates and special stochastic optimization strategies are required to deal with this. Two problems arise with this approach: i) source encoding can only be applied to fixed-spread acquisition setups, and ii) stochastic optimization methods tend to converge very slowly, relying on averaging to get rid of the cross-talk. Although the slow convergence is partly offset by the low iteration cost, we show that conventional optimization strategies are bound to outperform stochastic methods in the long run. In this paper we argue that we don{\textlnot}{\o}t need randomized source encoding to reap the benefits of stochastic optimization and we review an optimization strategy that combines the benefits of both conventional and stochastic optimization. The method uses a gradually increasing batch of sources. Thus, iterations are very cheap initially and this allows the method to make fast progress in the beginning. As the batch size grows, the method behaves like conventional optimization, allowing for fast convergence. Numerical examples suggest that the stochastic and hybrid method perform equally well with and without source encoding and that the hybrid method outperforms both conventional and stochastic optimization. The method does not rely on source encoding techniques and can thus be applied to non fixed-spread data.},
	keywords = {FWI, Optimization, SLIM},
	doi = {10.1111/j.1365-2478.2012.01096.x},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2478.2012.01096.x/abstract},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2013/VanLeeuwen11TRfwiwse/VanLeeuwen11TRfwiwse.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {herrmann2013SINBADffwi,
	title = {Frugal FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Seismic waveform inversion aims at obtaining detailed estimates of subsurface medium parameters, such as soundspeed, from seismic data. A formulation in the frequency-domain leads to an optimization problem constrained by a Helmholtz equation with many right-hand sides. Application of this technique in 3D precludes the use of factorization techniques to solve the Helmholtz equation due the large number of gridpoints and the bandwidth of the matrix. While many sophisticated pre-conditioned iterative techniques have been developed for the Helmholtz equation, they often include model-specific tuning parameters and are thus not very attractive for inversion since the medium parameters change from one iteration to the next. In this paper, we propose a method for 3D seismic waveform inversion that addresses both the need to efficiently solve the Helmholtz equation as well as the computational cost induced by the many right-hand sides. To solve the Helmholtz equation, we consider a simple generic preconditioned iterative method (CARP-CG) that is well-suited for inversion because of its robustness. We extend this method to a block-iterative method that can efficiently handle multiple right-hand sides. To reduce the computational cost of of the overall optimization procedure, we use recently proposed techniques from stochastic optimization that allow us to work with approximate gradient information. These approximations are obtained by evaluating only a small portion of the right-hand sides and/or by solving the PDE approximately. We propose heuristics to adaptively determine the required accuracy of the PDE solves and the sample-size and illustrate the algorithms on synthetic benchmark models. This is joint work with Tristan van Leeuwen.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/herrmann2013SINBADffwi/herrmann2013SINBADffwi.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2013SINBADfwi,
	title = {Frugal FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Seismic waveform inversion aims at obtaining detailed estimates of subsurface medium parameters, such as soundspeed, from seismic data. A formulation in the frequency-domain leads to an optimization problem constrained by a Helmholtz equation with many right-hand- sides. Application of this technique in 3D precludes the use of factorization techniques to solve the Helmholtz equation due the large number of gridpoints and the bandwidth of the matrix. While many sophisticated pre-conditioned iterative techniques have been developed for the Helmholtz equation, they often include model-specific tuning parameters and are thus not very attractive for inversion since the medium parameters change from one iteration to the next. In this paper, we propose a method for 3D seismic waveform inversion that addresses both the need to efficiently solve the Helmholtz equation as well as the computational cost induced by the many right-hand-sides. To solve the Helmholtz equation, we consider a simple generic preconditioned iterative method (CARP-CG) that is well-suited for inversion because of its robustness. We extend this method to a block-iterative method that can efficiently handle multiple right-hand sides. To reduce the computational cost of of the overall optimization procedure, we use recently proposed techniques from stochastic optimization that allow us to work with approximate gradient information. These approximations are obtained by evaluating only a small portion of the right-hand sides and/or by solving the PDE approximately. We propose heuristics to adaptively determine the required accuracy of the PDE solves and the sample-size and illustrate the algorithms on synthetic benchmark models. This is joint work with Tristan van Leeuwen.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADfwi/herrmann2013SINBADfwi.pdf},
	author = {Felix J. Herrmann}
}
@presentation {dasilva2013SINBADSPRhtuck,
	title = {Hierarchical Tucker tensor optimization - applications to 4D seismic data interpolation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In this work, we develop optimization algorithms on the manifold of Hierarchical Tucker (HT) tensors, an extremely efficient format for representing high-dimensional tensors exhibiting particular low-rank structure. With some minor alterations to existing theoretical developments, we develop an optimization framework based on the geometric understanding of HT tensors as a smooth manifold, a generalization of smooth curves/surfaces. Building on the existing research of solving optimization problems on smooth manifolds, we develop Steepest Descent and Conjugate Gradient methods for HT tensors. The resulting algorithms converge quickly, are immediately parallelizable, and do not require the computation of SVDs. We also derive efficient Gauss-Newton based algorithms which converge much faster than standard, first-order methods. We also extend ideas about favourable sampling conditions for missing-data recovery from the field of Matrix Completion to Tensor Completion and demonstrate how the organization of data can affect the success of recovery. As a result, if one has data with randomly missing source pairs, using these ideas, coupled with an efficient solver, one can interpolate large-scale seismic data volumes with missing sources and/or receivers by exploiting the multidimensional dependencies in the data. We are able to recover data volumes amidst extremely high subsampling ratios (in some cases, > 75\%) using this approach and we demonstrate our recovery on a synthetic 5D data set provided to us by BG.},
	keywords = {3D data interpolation, hierarchical tucker, Presentation, SINBAD, SINBADSPRING2013, SLIM, structured tensor},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/dasilva2013SINBADSPRhtuck/dasilva2013SINBADSPRhtuck.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {zheglova2013SINBADihss,
	title = {Imaging with hierarchical semi separable matrices},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Hierarchically Semi Separable (HSS) matrices are (in general) dense matrices that have low rank off-diagonal blocks that can be represented economically. Exploiting a specific structure of HSS representation, fast algorithms have been devised for matrix matrix multiplication, addition and computing a matrix inverse. We are interested in developing fast algorithms for seismic imaging using ideas from this approach. An overview of HSS representation and some methods using HSS representations of operators will be shown in this talk.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/zheglova2013SINBADihss/zheglova2013SINBADihss.pdf},
	author = {Polina Zheglova and Felix J. Herrmann}
}
@presentation {lago2013SINBADksf,
	title = {Krylov solvers in frequency domain FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {We briefly discuss here several aspects rising from the use of Krylov solvers for frequency domain FWI. Although several powerful preconditioners are in constant development by the linear algebra community targeting this application, some issues as the multishot and multifrequency scenarios as well as advanced Krylov method techniques in combination with these powerful preconditioners are rarely addressed. We provide an overview of some of the recent research on this regard and discuss possibility of use of some of these techniques in the context of an inversion.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/lago2013SINBADksf/lago2013SINBADksf.pdf},
	author = {Rafael Lago and Felix J. Herrmann}
}
@presentation {herrmann2013SINBADldc,
	title = {Latest developments on the Chevron GOM and other datasets},
	year = {2013},
	publisher = {SINBAD},
	abstract = {During this talk, we will give a brief overview on our imaging and FWI results on various synthetic and field datasets we have been working on. This is joint work with Andrew Calvert, Brendan R. Smithyman, and the SLIM team.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADldc/herrmann2013SINBADldc.pdf},
	author = {Felix J. Herrmann}
}
@presentation {li2013SINBADgom,
	title = {Lessons learned from Chevron Gulf of Mexico data set},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Chevron Gulf of Mexico data set is very challenging for FWI because of elastic phases, limited offset, lack of low frequencies and salt structure. To overcome these issue, we first use ray-based tomography on the hand-picked first breaks to generate initial model for FWI, and then we apply curvelet-denosing techniques to improve the poor signal-to-noise ratio of the observed data at low frequencies. Finally, Curvelet domain sparsity promoting Gauss-Newton FWI helps to suppress model space artifacts caused by elastic phases. This is joint work with Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Brendan R. Smithyman, Haneet Wason and Felix J. Herrmann},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADgom/li2013SINBADgom.pdf},
	author = {Xiang Li and Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Brendan R. Smithyman and Haneet Wason and Felix J. Herrmann}
}
@presentation {akalin2013SINBADmtc,
	title = {Matrix and tensor completion for large-scale seismic interpolation: a comparative study},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Owing to their high dimensionality, interpolating 3D seismic data volumes remains a computationally daunting task. In this work, we outline a comprehensive framework for sampling and interpolating such volumes based on the well-understood theory of Matrix and Tensor completion. This interpolation theory consists of three components major signal structure, structure-destroying sampling, and structure-restoring optimization. By viewing interpolation in the context of this theory, we are able to specify exactly when these approaches are expected to perform well. We also introduce structure-revealing transformations that promote the inherent low-rank structure in seismic data as well as a factorization approach that scales to large problem sizes. Our methods are able to handle large-scale data volumes more accurately and more quickly compared to other more ad-hoc approaches, as we will demonstrate. This is joint work with Curt Da Silva, Rajiv Kumar, Ben Recht, and Felix J. Herrmann.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/akalin2013SINBADmtc/akalin2013SINBADmtc.pdf},
	author = {Okan Akalin and Curt Da Silva and Rajiv Kumar and Ben Recht and Felix J. Herrmann}
}
@presentation {herrmann2013SINBADmlm,
	title = {Mitigating local minima in full-waveform inversion by expanding the search space with the penalty method},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Wave-equation based inversions, such as full-waveform inversion, are challenging because of their computational costs, memory requirements, and reliance on accurate initial models. To confront these issues, we propose a novel formulation of full-waveform inversion based on a penalty method. In this formulation, the objective function consists of a data-misfit term and a penalty term which measures how accurately the wavefields satisfy the wave-equation. Because we carry out the inversion over a larger search space, including both the model and synthetic wavefields, our approach suffers less from local minima. Our main contribution is the development of an efficient optimization scheme that avoids having to store and update the wavefields by explicit elimination. Compared to existing optimization strategies for full-waveform inversion, our method differers in two main aspects; i. The wavefields are solved from an augmented wave-equation, where the solution is forced to solve the wave-equation and fit the observed data, ii. no adjoint wavefields are required to update the model, which leads to significant computational savings. We demonstrate the validity of our approach by carefully selected examples and discuss possible extensions and future research. This is joint work with Tristan van Leeuwen.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/herrmann2013SINBADmlm/herrmann2013SINBADmlm.pdf},
	author = {Felix J. Herrmann}
}
@presentation {li2013SINBADmsd,
	title = {Model-space versus data-space FWI with the acoustic wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Inverting data with elastic phases using an acoustic wave equation can lead to erroneous results, especially when the number of iterations is too high, which may lead to over fitting the data. Several approaches have been proposed to address this issue. Most commonly, people data- filtering operations that are aimed to deemphasize the elastic phases in the data in favor of the acoustic phases. Examples of this approach are nested loops over offset range and Laplace parameters. In this presentation, we discuss two complementary optimization-driven methods where the minimization process decides adaptively which of the data or model components are consistent with the objective. Specifically, we compare the s t misfit function as the data-space alternative and curvelet-domain sparsity promotion as the model-space alternative. Application of these two methods to a realistic synthetic lead to comparable results that we believe can be improved by combining these two methods.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADmsd/li2013SINBADmsd.pdf},
	author = {Xiang Li and Anais Tamalet and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {wang2013SINBADnru,
	title = {Noise reduction by using interferometric measurements},
	year = {2013},
	publisher = {SINBAD},
	abstract = {The interferometric formulation of linear wave-based inversion problem was proposed by Demanet and Jugnon recently. Instead of directly fitting the data, they proposed to fit a subset of the data{\textquoteright}s cross-correlation. It can be verified that if the full cross-correlation is used, then the problem is equivalent to the usual least square problem. The subsampling, which is usually considered to cause instability to the solution, is surprisingly useful in this setting. Numerical experiments for the inverse source problem and the inverse scatting problem have both suggested that a {\textquoteright}good{\textquoteright} sampling strategy can actually increase the stability under modeling error caused by the uncertainty of a kinetic nature. We will study the mathematical mechanism behind this phenomenon, and try to see whether or not there exists a universally {\textquoteright}good{\textquoteright} sampling strategy independent of the types of forward operators we use.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/wang2013SINBADnru/wang2013SINBADnru.pdf},
	author = {Rongrong Wang and Bas Peters and Felix J. Herrmann}
}
@presentation {fang2013SINBADp3dfwi,
	title = {Parallel 3D FWI with simultaneous shots},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In this work, we manage to build the workflow for the parallel 3D full-waveform inversion. In the forward simulation part, we generate the Helmholtz matrix in parallel and use parallel CARPCG to solve the Helmholtz equation. In the inversion process, simultaneous shots are used to reduce the computational costs. Additionally, we propose a method to select the number of simultaneous shots and tolerance of CARPCG dynamically, to reach a compromise between the computational costs and accuracy.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADp3dfwi/fang2013SINBADp3dfwi.pdf},
	author = {Zhilong Fang and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {smithyman2013SINBADprb,
	title = {Phase-residual based quality-control methods and techniques for mitigating cycle skips},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Most full-waveform inversion algorithms use local optimization methods to iteratively improve the numerical earth model. All of these make the implicit assumption that the the model close to the true earth to avoid cycle. In practice this may not be true. We explore two questions: 1. How do we understand and visualize the cycle skip phenomenon in order to recognize it if it exists? 2. How do we automate this quality control step and use the rich information from multiple data to mitigate cycle skips and avoid local minima?},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/smithyman2013SINBADprb/smithyman2013SINBADprb.pdf},
	author = {Brendan R. Smithyman and Felix J. Herrmann}
}
@presentation {pong2013SINBADppg,
	title = {The proximal-proximal gradient algorithm},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In many applications, one has to minimize the sum of a smooth loss function modeling misfit and a regularization term inducing structures. In this talk, we consider the case when the regularization is a composition of a convex function, whose proximal mapping is easy to compute, and a nonzero linear map. Such instances arise in system identification and realization problems. In this talk, we present a new algorithm, the proximal-proximal gradient algorithm, which admits easy subproblems. Our algorithm reduces to the proximal gradient algorithm if the linear map is just the identity map, and can be viewed as "very inexact" inexact proximal gradient algorithm. We show that the whole sequence generated from the algorithm converges to an optimal solution, and establish an upper bound on iteration complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/pong2013SINBADppg/pong2013SINBADppg.pdf},
	author = {Ting Kei Pong and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {nutini2013SINBADpcb,
	title = {Putting the curvature back into sparse solvers},
	year = {2013},
	publisher = {SINBAD},
	abstract = {For many problems in signal and image processing, we seek a sparse solution that solves that approximately solves the problem Ax $\approx$ b, where A is an m-by-n matrix and b is an m-vector. Many of the most used approaches to problem thissuch as iterative soft thresholding SPGL1 andare first-order methods. As a result, these methods can sometimes be slow to converge. In this talk, we present an approach that takes advantage of the easily-obtainable second-order information. By exploiting this available second-order information, we are able to put the curvature back into sparse solvers and improve upon the convergence rates of existing solvers.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/nutini2013SINBADpcb/nutini2013SINBADpcb.pdf},
	author = {Julie Nutini and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {warner2013SINBADrfwi,
	title = {Reflection FWI with a poor starting model},
	year = {2013},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/warner2013SINBADrfwi/warner2013SINBADrfwi.pdf},
	author = {Mike Warner}
}
@presentation {vanleeuwen2013SINBADrpp,
	title = {Relaxing the physics: a penalty method for full-waveform inversion},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Computationally efficient method of solving partial differential equation (PDE)-constrained optimization problems that occur in (geophysical) inversion problems. The method takes measured data from a physical system as input and minimizes an objective function that depends on an unknown model for the physical parameters, fields, and additional nuisance parameters such as the source function. The invention consists of a minimization procedure involving a cost functional comprising of a data-misfit term and a penalty term that measures how accurately the fields satisfy the PDE. The method is composed of two alternating steps, namely the solution of a system of equations forming the discretization of the data-augmented PDE, and the solution of physical model parameters from the PDE itself given the field that solves the data-augmented system and an estimate for the sources. Compared to all-at-once approaches to PDE-constrained optimization, there is no need to update and store the fields for all sources leading to significant memory savings. As in the all-at-once approach, the proposed method explores a larger search space and is therefore less sensitive to initial estimates for the physical model parameters. Contrary to the reduced formulation, the proposed method does not require the solution of an adjoint PDE, effectively halving the number of PDE solves and memory requirement. As in the reduced formulation, fields can be computed independently and aggregated, possibly in parallel.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/vanleeuwen2013SINBADrpp/vanleeuwen2013SINBADrpp.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {kumar2013SINBADhss,
	title = {Seismic data interpolation via low-rank matrix factorization in the hierarchical semi-separable representation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Recent developments in matrix rank optimization have allowed for new computational approaches in the field of seismic data interpolation. One of the main requirements of exploiting rank-minimization approaches is that the target data set should exhibit a low-rank structure. Seismic frequency slices exhibit low-rank structure at the low-frequencies, but not at the high frequencies. This behavior is due to the increase in oscillations as we move from low to high-frequency slices, even though the energy remains focused around the diagonal. Therefore, interpolation via rank minimization in the high-frequency range requires extended formulations that incorporate low-rank structure. We propose an approach for seismic data interpolation which incorporates the Hierarchical Semi-Separable Structure (HSS) inside rank-regularized least-squares formulations for the missing-trace interpolation problem. The proposed approach is suitable for large scale problems, since it avoids SVD computations and uses a low-rank factorized formulation instead. We illustrate the advantages of the new HSS approach by interpolating a seismic line from the Gulf of Suez and compare the reconstruction with conventional rank minimization.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/kumar2013SINBADhss/kumar2013SINBADhss.pdf},
	author = {Rajiv Kumar and Hassan Mansour and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@presentation {tu2013SINBADmachar,
	title = {SLIM{\textquoteright}s findings on the Machar dataset},
	year = {2013},
	publisher = {SINBAD},
	abstract = {By courtesy of BP, we are able to get some hands-on experience imaging seismic data from the Machar field in North Sea. We performed reverse-time migration and sparsity-promoting migration with source-estimation to this dataset (or a cropped section of the dataset in many cases due to computational constraints), and had some interesting findings. In this presentation, we will show some conclusive results we have got, and explain the key techniques in imaging this dataset.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/tu2013SINBADmachar/tu2013SINBADmachar.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {vanleeuwen2013SINBADsda,
	title = {Solving the data-augmented wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {The recently proposed penalty method promises to mitigate some of the non-linearity inherent in full-waveform inversion by relaxing the requirement that the wave-equation needs to be solved exactly. The basic workflow of this new method is as follows; i) solve an overdetermined wave-equation (the data-augmented wave-equation), where the data serves as additional constraints for the wavefields, ii) compute the wavefield-residual by substituting this wavefield in the wave-equation, and iii) correlate the wavefield with the wavefield-residual to obtain a model-update. As opposed to the conventional workflow, no explicit adjoint solve is needed to compute the model-update. However, instead of solving a wave-equation, we need to solve a data-augmented wave-equation. In this talk we explore some of the challenges of solving this data-augmented wave-equation and review some possible solution strategies for both time and frequency-domain applications.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/vanleeuwen2013SINBADsda/vanleeuwen2013SINBADsda.pdf},
	author = {Tristan van Leeuwen and Bas Peters and Felix J. Herrmann}
}
@presentation {dasilva2013SINBADstf,
	title = {Structured tensor formats for missing-trace interpolation and beyond},
	year = {2013},
	publisher = {SINBAD},
	abstract = {High-dimensional data, alternatively known as tensors, occurs in a variety of seismic problems. By exploiting the fact that seismic data can be well represented as a structured tensor, we design algorithms that operate on much lower dimensional parameters. In this talk, we will review some recent developments in interpolating seismic data volumes in the so-called Hierarchical Tucker format as well as demonstrate the need for such formats when tackling high-dimensional problems such as Uncertainty Quantification.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/dasilva2013SINBADstf/dasilva2013SINBADstf.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@presentation {fang2013SINBADsfwi,
	title = {Swift FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In 3D case, there is much more data and modeling is much more expensive. As a result, parallel computing is very important for 3D full-waveform inversion. Both domain decomposition and data decomposition need a large number of parallel computing resources. However, programs based on parallel MATLAB suffer from the limitation of licenses. In order to obtain a MATLAB licenses free solution, we use SWIFT, which is a fast and easy parallel scripting language. Once the original Mat file is complied to an executable file, SWIFT can run the code inside the executable file in parallel without using parallel MATLAB. We use the SWIFT to compute the object functions and gradients of different shots in parallel, and test the parallel 3D FWI code with overthrust data. This is joint work with Thomas Lai, Harsh Juneja, Bas Peters.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADsfwi/fang2013SINBADsfwi.pdf},
	author = {Zhilong Fang and Henryk Modzelewski and Tristan van Leeuwen and Thomas Lai and Harsh Juneja and Bas Peters and Felix J. Herrmann}
}
@presentation {goh2013SINBADttt,
	title = {Taming time through tangents},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Given two vectors of (possibly) different lengths, the edit distance considers all possible alignments between the two and picks the one that minimizes the number of operations needed to turn one into the other. Though highly non-smooth and riddled with local minima, we show a way to compute the convex envelope of this function, which opens the door to using the approximate edit distance as a surrogate for the L2 distance and comparing vectors of different lengths.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/goh2013SINBADttt/goh2013SINBADttt.pdf},
	author = {Gabriel Goh and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {wason2013SINBADtjm,
	title = {Time-jittered marine sources},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Current efforts towards dense shot (and/or receiver) sampling and full azimuthal coverage to produce higher-resolution images have led to the deployment of multiple source vessels across the survey area. A step ahead from multi-source seismic acquisition is simultaneous or blended acquisition where different source arrays/vessels fire shots at near-simultaneous or slightly random times. Seismic data acquisition with simultaneous (or blended) sources has helped improve acquisition efficiency and mitigate acquisition related costs. Deblending then aims to recover unblended data, as acquired during conventional acquisition, from blended data since many processing techniques rely on full, regular sampling. We present a simultaneous/blended marine acquisition setup where shots fire at significantly jittered instances in time resulting in jittered shot locations for a given speed of the source vessel. The conventional, unblended data is recovered from the blended, jittered/irregular data by sparsity-promoting inversion using the non-equispaced fast discrete curvelet transform. The optimization scheme aims to deblend the blended data along with regularization and interpolation to a (finer) regular grid.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/wason2013SINBADtjm/wason2013SINBADtjm.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@presentation {wason2013SINBADtjo,
	title = {Time-jittered ocean bottom seismic acquisition},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Leveraging ideas from the field of compressed sensing, we show how simultaneous or blended acquisition can be setup as a {\textendash} compressed sensing problem. This helps us to design a pragmatic time-jittered marine acquisition scheme where multiple source vessels sail across an ocean-bottom array firing airguns at {\textendash} jittered source locations and instances in time, resulting in better spatial sampling, and speedup acquisition. Furthermore, we can significantly impact the reconstruction quality of conventional seismic data (from jittered data) and demonstrate successful recovery by sparsity promotion. In contrast to random (under)sampling, acquisition via jittered (under)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. Results are illustrated with simulations of time-jittered marine acquisition, which translates to jittered source locations for a given speed of the source vessel.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/wason2013SINBADtjo/wason2013SINBADtjo.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}
@presentation {fang2013SINBADuafwi,
	title = {Uncertainty analysis for FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Uncertainty analysis is important for seismic interpretation. Based on the framework of Bayesian, we can analyse different statistic parameters of our FWI result. However, directly sampling the posterior probability density function (pdf) is computationally intractable. In order to make this problem computationally tractable, in this work, we use Gaussian distribution approximation and low rank approximation to generate the posterior pdf. Simultaneous shots are also used to reduce the computational costs.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADuafwi/fang2013SINBADuafwi.pdf},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@presentation {ghadermarzy2013SINBADups,
	title = {Using prior support information in approximate message passing algorithms},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Consider the standard compressed sensing problem. We want to recover sparse compressible aorsignal from few linear measurements. In this talk we investigate recovery performance when we have prior information about the support, i.e., the indices of the non-zero entries, of the signal to be recovered. First we briefly review the results of "weighted $\ell_p$ minimization algorithm with p = 1 and 0 < p < 1". Then we derive a weighted approximate message passing (AMP) algorithm which incorporates prior support information into the AMP algorithm. We empirically show that this algorithm recovers sparse signals significantly faster than weighted $\ell_1$ minimization. We also introduce a reweighting scheme for AMP and weighted AMP which, we observe, substantially improves the recovery conditions of these algorithms. We illustrate our results with extensive numerical experiments on synthetic data and seismic data reconstruction.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/ghadermarzy2013SINBADups/ghadermarzy2013SINBADups.pdf},
	author = {Navid Ghadermarzy and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {kumar2013SINBADwrs,
	title = {Wavefield reconstruction with SVD-free low-rank matrix factorization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {As shown in past, we can leverage the ideas from the field of compressed sensing to cast problems like seismic data interpolation or sequential shot data recovery from simultaneous data, as a compressed sensing problem. In this work we will show how we can borrow the same ideas of compressed sensing and cast these problems as matrix completion problems. Instead of sparsity we will show that we can exploit the low-rank structure of seismic data to solve these problems. One of the impediments in rank-minimization problem is the computation of singular values. We will also show how we can solve the rank minimization problems SVD-free. The practical application is divided into three parts: 1. In case of sequential seismic data acquisition, how jittered subsampling helps to recover the better quality data as compared to random subsampling. 2. How the incorporation of reciprocity principles help to enhance the quality of recovered fully sampled data. 3. How we can recover the sequential source data from simultaneous source data.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADwrs/kumar2013SINBADwrs.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Hassan Mansour and Ernie Esser and Felix J. Herrmann}
}
@presentation {vanleeuwen2012SINBAD3dfd,
	title = {3D frequency-domain waveform inversion using a row-projected Helmholtz solver},
	year = {2012},
	publisher = {SINBAD},
	abstract = {3D frequency-domain full waveform inversion relies on being able to efficiently solve the 3D Helmholtz equation. Iterative methods require sophisticated preconditioners because the Helmholtz matrix is typically indefinite. In the first part of the talk I review a preconditioning technique that is based on row-projections. Notable advantages of this preconditioner over existing ones are that it has low algorithmic complexity, is easily parallelizable and extendable to time-harmonic vector equations. In the second part of the talk I discuss how the row-projected solver can be used in the context of waveform inversion. Key aspects are: the use of block-iterative methods for multiple sources and adapting the accuracy of the solver.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/vanleeuwen2012SINBAD3dfd/vanleeuwen2012SINBAD3dfd_pres.pdf},
	author = {Tristan van Leeuwen}
}
@presentation {miao2012SINBADasp,
	title = {Accelerating on sparse promoting recovery and its benefits in seismic application},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Sparse promoting recovery problem arises more and more frequently with the broad application of compressed sensing tool in exploration seismology. Because of the curse of dimensionality, the prohibitive computation burden on iteratively evaluating objective functions is one of the key issues that constrain high performance l1 solver. In this paper, we try to further improve the convergence performance of SPGl1, one of the state-of-the-art large scale sparse recovery solver, and as a result limit the number of objective function evaluations by introducing a projected quasi Newton method. Examples showing acceleration on seismic data collection, data processing as well as inversion are included.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/miao2012SINBADasp/miao2012SINBADasp_pres.pdf},
	author = {Lina Miao}
}
@presentation {hargreaves2012SINBADavs,
	title = {Analysis versus synthesis in weighted sparse recovery},
	year = {2012},
	publisher = {SINBAD},
	abstract = {The synthesis model for compressive sensing has been the model of choice for many years and various weighting schemes have been shown to improve it{\textquoteright}s performance (see Yilmaz, Mansour, and Ghadermarzy talks). However, there is a counterpart model to synthesis, namely the analysis model, which has been less popular but recently attracted more attention (see Lin{\textquoteright}s talk). In this talk, weighting in the analysis model is discussed and applied to the seismic trace interpolation problem.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/hargreaves2012SINBADavs/hargreaves2012SINBADavs_pres.pdf},
	author = {Brock Hargreaves}
}
@presentation {warner2012SINBADafw,
	title = {Anisotropic 3D full-waveform inversion of the Tommeliten Alpha field},
	year = {2012},
	publisher = {SINBAD},
	abstract = {We have implemented a robust and practical scheme for anisotropic 3D acoustic full-waveform inversion. We demonstrate this scheme on a field data set, applying it to a four-component ocean-bottom survey over the Tommeliten Alpha field in the North Sea. This shallow-water data set provides good azimuthal coverage to offsets of 7 km, with reduced coverage to a maximum offset of about 11 km. The reservoir lies at the crest of a high-velocity antiformal chalk section, overlain by about 3000 m of clastics within which a low-velocity gas cloud produces a seismic obscured area. We inverted only the hydrophone data, and we retained free-surface multiples and ghosts within the field data. We invert in six narrow frequency bands, in the range 3 to 6.5 Hz. At each iteration, we selected only a subset of sources, using a different subset at each iteration; this strategy is more efficient than inverting all the data every iteration. Our starting velocity model was obtained using standard PSDM model building including anisotropic reflection tomography, and contained epsilon values as high as 20\%. We have also attempted full-elastic inversion of these data to recover a shallow isotropic model of both p and s-wave velocities. The final FWI velocity model shows a network of shallow high-velocity channels that match similar features in the reflection data. Deeper in the section, the FWI velocity model reveals a sharper and more-intense low-velocity region associated with the gas cloud in which low-velocity fingers match the location of gas-filled faults visible in the reflection data. The resulting velocity model provides a better match to well logs, and better flattens common image gathers, than does the starting model. Reverse-time migration, using the FWI velocity model, provides significant uplift to the migrated image, simplifying the planform of the reservoir section at depth. The workflows, inversion strategy, and algorithms that we have used have broad application to invert a wide-range of analogous field data sets.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/warner2012SINBADafw/warner2012SINBADafw_pres.pdf},
	author = {Mike Warner}
}
@presentation {petrenko2012SINBADcarp,
	title = {CARP-CG: a computational study},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Forward modelling of the wave equation is a key ingredient in seismic full waveform inversion (FWI). Simulation in the time domain and solution of the wave equation in the frequency domain are two competing approaches to modelling. Frequency domain approaches can further be categorized as using either direct or iterative solvers. For 3D FWI, iterative solvers in the frequency domain are attractive, partly because they require less memory than the other methods. This is due to the fact that there is no need to store the wavefield at each time step, or compute a factorization of the Helmholtz operator that will not be as sparse as the original matrix. One iterative solver that has been applied to the Helmholtz equation is CARP-CG. CARP-CG uses Kaczmarz row projections for each block of a domain decomposition scheme to precondition the Helmholtz system into being symmetric and positive semidefinite. The method of conjugate gradients is then used to solve the preconditioned system. We present a comparison of the performance of CARP-CG implemented in several languages (MATLAB, C, FORTRAN, julia) and in two different hardware environments: the LIMA HPC cluster hosted at UBC, and the Checkers cluster which is part of the Westgrid consortium. Parallelization of the algorithm via domain decomposition implemented with MPI (distributed memory) and OMP (shared memory) is also examined.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/petrenko2012SINBADcarp/petrenko2012SINBADcarp_pres.pdf},
	author = {Art Petrenko}
}
@presentation {oghenekohwo2012SINBADcs,
	title = {Compressed sensing: a tool for eliminating repeatability in acquisition of 4D (time-lapse) seismic data},
	year = {2012},
	publisher = {SINBAD},
	abstract = {In 4D (time-lapse) seismic data acquisition, a very significant step is the repeatability of the acquisition process. In other words, the geophones must be placed at the exact location as they were, during baseline survey and acquisition. This condition is required to be able to produce an image of the same location over time and this enhances a proper reservoir characterization. The cost of repeating the seismic acquisition is very expensive, as geophones (receivers) have to be left at the same location over the period for which the data will be acquired. In this talk, we attempt to highlight the effort of Compressed Sensing, to eliminate this condition of repeatability of the acquisition. We show that a random sampling of the shots or random placement of the geophones is able to reproduce the same image over time, hence eliminating any acquisition imprints on the final seismic image.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/oghenekohwo2012SINBADcs/oghenekohwo2012SINBADcs_pres.pdf},
	author = {Felix Oghenekohwo}
}
@presentation {au-yeung2012SINBADcs,
	title = {Compressed sensing, random Fourier matrix and jitter sampling},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Compressed sensing is an emerging signal processing technique that allows signals to be sampled well below the Nyquist rate, when the signal has a sparse representation in an orthonormal basis. By using a random Fourier matrix or a Gaussian matrix as our measurement matrix, we can reconstruct a signal from far fewer measurements than required by Shannon sampling theorem. In this talk, we will discuss the role of uniform versus jitter sampling, both in a theoretical and practical viewpoint.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/au-yeung2012SINBADcs/au-yeung2012SINBADcs_pres.pdf},
	author = {Enrico Au-Yeung and Hassan Mansour and Ozgur Yilmaz}
}
@article {haber10TRemp,
	title = {An effective method for parameter estimation with PDE constraints with multiple right hand sides},
	journal = {SIAM Journal on Optimization},
	volume = {22},
	number = {3},
	year = {2012},
	month = {07},
	abstract = {Often, parameter estimation problems of parameter-dependent PDEs involve multiple right-hand sides. The computational cost and memory requirements of such problems increase linearly with the number of right-hand sides. For many applications this is the main bottleneck of the computation. In this paper we show that problems with multiple right-hand sides can be reformulated as stochastic programming problems by combining the right-hand sides into a few {\quotedblbase}simultaneous{\textquotedblright} sources. This effectively reduces the cost of the forward problem and results in problems that are much cheaper to solve. We discuss two solution methodologies: namely sample average approximation and stochastic approximation. To illustrate the effectiveness of our approach we present two model problems, direct current resistivity and seismic tomography.},
	keywords = {FWI, Optimization, SLIM},
	url = {http://dx.doi.org/10.1137/11081126X},
	author = {Eldad Haber and Matthias Chung and Felix J. Herrmann}
}
@article {herrmann11GPelsqIm,
	title = {Efficient least-squares imaging with sparsity promotion and compressive sensing},
	journal = {Geophysical Prospecting},
	volume = {60},
	number = {4},
	year = {2012},
	month = {07},
	pages = {696-712},
	address = {University of British Columbia, Vancouver},
	abstract = {Seismic imaging is a linearized inversion problem relying on the minimization of a least-squares misfit functional as a function of the medium perturbation. The success of this procedure hinges on our ability to handle large systems of equations{\textendash}-whose size grows exponentially with the demand for higher resolution images in more and more complicated areas{\textendash}-and our ability to invert these systems given a limited amount of computational resources. To overcome this "curse of dimensionality" in problem size and computational complexity, we propose a combination of randomized dimensionality-reduction and divide-and-conquer techniques. This approach allows us to take advantage of sophisticated sparsity-promoting solvers that work on a series of smaller subproblems each involving a small randomized subset of data. These subsets correspond to artificial simultaneous-source experiments made of random superpositions of sequential-source experiments. By changing these subsets after each subproblem is solved, we are able to attain an inversion quality that is competitive while requiring fewer computational, and possibly, fewer acquisition resources. Application of this concept to a controlled series of experiments showed the validity of our approach and the relationship between its efficiency{\textendash}-by reducing the number of sources and hence the number of wave-equation solves{\textendash}-and the image quality. Application of our dimensionality-reduction methodology with sparsity promotion to a complicated synthetic with well-log constrained structure also yields excellent results underlining the importance of sparsity promotion.},
	keywords = {Compressive Sensing, Imaging, Optimization, SLIM},
	doi = {10.1111/j.1365-2478.2011.01041.x},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2012/herrmann11GPelsqIm/herrmann11GPelsqIm.pdf},
	url2 = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2478.2011.01041.x/full},
	author = {Felix J. Herrmann and Xiang Li}
}
@presentation {aravkin2012SINBADenp,
	title = {Estimating nuisance parameters in inverse problems},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Many inverse problems include nuisance parameters which, while not of direct interest, are required to recover primary parameters. In this talk, we present the idea of "projecting out" these variables, and how this idea allows us to design methods for solving a broad class of problems with nuisance parameters, such as variance or degrees of freedom. We then discuss several geophysical applications, including including estimation of unknown variance parameters in the Gaussian model for full waveform inversion, degree of freedom (d.o.f.) parameter estimation in the context of robust imaging problems, and robust source estimation.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/aravkin2012SINBADenp/aravkin2012SINBADenp_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@presentation {xiang2012SINBADfgn,
	title = {Fast Gauss-Newton full-waveform inversion with sparsity regularization},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Full-waveform inversion (FWI) can be considered as a controlled data fitting process, in which we approximately fit observed data by iteratively updating the initial velocity model, we expect the final model can reveal subsurface structure till the wavefield misfit can converge to designed tolerance. The conventional FWI approach is expensive since it requires the inversion of a linear system, which involves extremely large multi-experiment data volumes. To overcome this issue we percent a curvetlet based sparsity-promoting Gauss-Newton inversion method. In this presentation we invert for the model updates by replacing the normal Gauss-Newton linearized subproblem for subsampled FWI with a sparsity promoting FWI formulation. We speed up the algorithm and avoid over fitting the data by solving the problem approximately. Aside from this, we control wavefield dispersion by gradually increasing grid size as we move to higher frequencies. Our approach is successful because it reduces the size of seismic data volumes without loss of information. With this reduction, we can compute a Newton-like update with the reduced data volume at the cost of roughly one gradient update for the fully sampled wavefield.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/xiang2012SINBADfgn/xiang2012SINBADfgn_pres.pdf},
	author = {Xiang Li}
}
@presentation {ning2012SINBADfim,
	title = {Fast imaging with multiples},
	year = {2012},
	publisher = {SINBAD},
	abstract = {If correctly used, multiple energy can be mapped to the correct subsurface locations. However, simply applying the cross-correlation imaging condition will introduce non-causal artifacts into the final image. Here we propose an inversion approach to image primaries and multiples simultaneously that yields an artifact-free image. To address the high computational cost associated with inversion, we propose to: i) have the wave-equation solver carry out the multi-dimensional convolutions implicitly, and ii) reduce the number of PDE solves by randomized subsampling. We then propose to improve the overall performance of this algorithm by a process called rerandomization, which helps to cancel the correlation built between model iterate and the subsampling operator. We show the merits of our approach on a number of examples.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/ning2012SINBADfim/ning2012SINBADfim_pres.pdf},
	author = {Ning Tu}
}
@presentation {ning2012SINBADSPRINGfim,
	title = {Fast imaging with multiples by sparse inversion},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/ning2012SINBADSPRINGfim/ning2012SINBADSPRINGfim_pres.pdf},
	author = {Ning Tu}
}
@article {Li11TRfrfwi,
	title = {Fast randomized full-waveform inversion with compressive sensing},
	journal = {Geophysics},
	volume = {77},
	number = {3},
	year = {2012},
	month = {05},
	pages = {A13-A17},
	abstract = {Wave-equation based seismic inversion can be formulated as a nonlinear inverse problem where the medium properties are obtained via minimization of a least- squares misfit functional. The demand for higher resolution models in more geologically complex areas drives the need to develop techniques that explore the special structure of full-waveform inversion to reduce the computational burden and to regularize the inverse problem. We meet these goals by using ideas from compressive sensing and stochastic optimization to design a novel Gauss-Newton method, where the updates are computed from random subsets of the data via curvelet-domain sparsity promotion. Application of this idea to a realistic synthetic shows improved results compared to quasi-Newton methods, which require passes through all data. Two different subset sampling strategies are considered: randomized source encoding, and drawing sequential shots firing at random source locations from marine data with missing near and far offsets. In both cases, we obtain excellent inversion results compared to conventional methods at reduced computational costs.},
	keywords = {Compressive Sensing, FWI, Optimization, SLIM},
	doi = {10.1190/geo2011-0410.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2012/Li11TRfrfwi/Li11TRfrfwi.pdf},
	author = {Xiang Li and Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {herrmann2012SINBADals,
	title = {Fast sparsity-promoting imaging with message passing},
	year = {2012},
	publisher = {SINBAD},
	abstract = {To meet current-day challenges, exploration seismology increasingly relies on more and more sophisticated algorithms that require multiple paths through all data. This requirement leads to problems because the size of seismic data volumes is increasing exponentially, exposing bottlenecks in IO and computational capability. To overcome these bottlenecks, we follow recent trends in machine learning and compressive sensing by proposing a sparsity-promoting inversion technique that works on small randomized subsets of data only. We boost the performance of this algorithm significantly by modifying a state-of-the-art l1-norm solver to benefit from message passing, which breaks the build up of correlations between model iterates and the randomized linear forward model. We demonstrate the performance of this algorithm on a toy sparse-recovery problem and on a realistic reverse-time-migration example with random source encoding. The improvements in speed, memory use, and output quality are truly remarkable.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADals/herrmann2012SINBADals_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {peters2012SINBADfde,
	title = {Frequency domain 3D elastic wave propagation in general anisotropic media},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Elastic wave propagation in 3 spatial dimensions is modeled using a wave equation containing the full stiffness tensor consisting of 21 independent components. This allows modeling in general anisotropic media. The wave equation is discretized on several Cartesian and rotated Cartesian staggered finite-difference grids (using a 2nd order approximation). The grids are linearly combined and, in combination with a antilumped mass strategy, minimize numerical dispersion while requiring a low number of grid points per wavelength. In case not all 21 components need to be modeled, an approximation of the stiffness tensor can be used (e.g., orthorhombic anisotropy, TTI, ...). This results in a linear system of equations, which is solved using an iterative method. The modeling of all 21 components of the stiffness tensor (or an approximation) enables the development of new waveform inversion functionalities.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/peters2012SINBADfde/peters2012SINBADfde_pres.pdf},
	author = {Bas Peters}
}
@presentation {aravkin2012SINBADgsf,
	title = {Generalized SPGL1: from theory to applications},
	year = {2012},
	publisher = {SINBAD},
	abstract = {The SPGL1 solver has been effectively used for many geophysical applications, including curvelet data interpolation, imaging, and as a subroutine in full waveform inversion. In this talk, we present an overview of the theoretical foundation of the solver, along with a broad generalization of this foundation. We then introduce several applications, including robust \& sparse imaging, sparse deconvolution, and data interpolation by matrix completion.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/aravkin2012SINBADgsf/aravkin2012SINBADgsf_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@presentation {dasilva2012SINBADhtt,
	title = {Hierarchical Tucker tensor optimization - applications to 4D seismic data interpolation},
	year = {2012},
	publisher = {SINBAD},
	abstract = {There has been a swell of research in the scientific computing community in the last couple of years which tries to extend notions of linear algebra (rank, the SVD, linear systems, etc.) to higher dimensional arrays, or tensors. Much work has been proposed to try to overcome the so called "curse of dimensionality", the O(N^d) storage required for a d-dimensional array, where N is the size of each dimension. The hierarchical Tucker format is one such tensor representation which manages to decompose a hierarchy of dimensions into parameter matrices of very manageable size, requiring at most dNK + (d - 2)K^3 + K^2 parameters, where K is an internal rank parameter. In this work, we extend ideas of matrix completion to the tensor case, where we only know a small number of randomly distributed entries from various 4D frequency slices, and try to recover the fully sampled tensor based on the knowledge that it has low hierarchical tucker rank in a particular arrangement of dimensions. Using this approach, we exploit the multi-dimensional dependencies within the full data in order to achieve very promising interpolation results even from heavily subsampled data.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/dasilva2012SINBADhtt/dasilva2012SINBADhtt_pres.pdf},
	author = {Curt Da Silva}
}
@presentation {lin2012SINBADics,
	title = {An introduction to cosparse signal reconstruction},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Undersampling techniques in exploration seismology usually relies on the assumption that seismic records and images permit sparse approximations under certain representations, such as Curvelet coefficients. Recent findings have suggested that for redundant representations (of which Curvelet is an example), the analysis operator that maps the physical signal to coefficients may also play a crucial role in recovering data from incomplete observations. In particular, the number of zero-valued coefficients given by the analysis operator acting on the signal, referred to as its "cosparsity", have an analogous role to the sparsity of the signal in terms of the coefficients. The cosparsity of the signal permits recovery guarantees that are completely separate from sparsity-based models, and gives rise to distinct sets of reconstruction algorithms and performances compared to sparsity-based approaches. We present in this talk some initial findings on the viability of cosparse reconstruction for a variety of seismic applications that previously relied on sparse signal reconstruction, such as data interpolation and source separation.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/lin2012SINBADics/lin2012SINBADics_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {aravkin2012SINBADipu,
	title = {Inverse problems using Student{\textquoteright}s t},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/aravkin2012SINBADipu/aravkin2012SINBADipu_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@presentation {Akalin2012SINBADlss,
	title = {Large scale seismic data interpolation with matrix completion},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Seismic surveys amass large and incomplete data sets, and designing algorithms to interpolate the missing data at very large scales poses a daunting and critical challenge. We study how to apply scalable matrix completion methods to such interpolation problems. Recent studies in matrix completion have shown that a matrix that has low rank can be exactly completed when only a small number of observations are available. However, there are two challenges to applying matrix completion to seismic data. Matrix completion is typically applied to two dimensional or dyadic data whereas seismic data is often tensorial. Also successful matrix completion requires a low-rank matrix structure. We address these problems by organizing the seismic data on a matrix grid which exhibits a low-rank structure. This encoding allows us to apply the Jellyfish algorithm, developed at the University of Wisconsin, which achieves state-of-the-art performance for large-scale matrix completion. The proposed framework makes it possible to complete high-SNR interpolations of gigabytes of 4-D seismic data in minutes on standard multicore workstations. Our preliminary experimental results suggest that matrix completion provides a promising new approach to the seismic data interpolation problem.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/Akalin2012SINBADlss/Akalin2012SINBADlss_pres.pdf},
	author = {Okan Akalin}
}
@presentation {herrmann2012SINBADlds,
	title = {Latest developments in seismic-data recovery},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/herrmann2012SINBADlds/herrmann2012SINBADlds_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {ghadermarzy2012SINBADncc,
	title = {Non-convex compressed sensing using partial support information},
	year = {2012},
	publisher = {SINBAD},
	abstract = {In this talk, we will address the recovery conditions of weighted $\ell_p$ minimization for signal reconstruction from compressed sensing measurements when (possibly inaccurate) partial support information is available. First we will motivate the use of (weighted) $\ell_p$ minimization with $p<1$ and point out its advantages over weighted $\ell_1$ minimization when there is prior information on the support of the signal that is possibly partial and inaccurate. Then we will provide theoretical guarantees of sufficient recovery conditions for weighted $\ell_p$ minimization, which are better than those for (unweighted) $\ell_p$ minimization as well as those for weighted $\ell_1$. In the last part of the talk, we will illustrate our results with some numerical experiments stylized applications.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/ghadermarzy2012SINBADncc/ghadermarzy2012SINBADncc_pres.pdf},
	author = {Navid Ghadermarzy}
}
@presentation {vanleeuwen2012SINBADoof,
	title = {An object-oriented framework for frequency-domain FWI},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/vanleeuwen2012SINBADoof/vanleeuwen2012SINBADoof_pres.pdf},
	author = {Tristan van Leeuwen}
}
@presentation {wason2012SINBADobs,
	title = {Ocean bottom seismic acquisition via jittered sampling},
	year = {2012},
	publisher = {SINBAD},
	abstract = {We present a pragmatic marine acquisition scheme where a single (or multiple) vessel sails across an ocean-bottom array firing airguns at ? optimally jittered source locations and instances in time. Following the principles of compressive sensing, we can significantly impact the reconstruction quality of conventional seismic data (from jittered data) and demonstrate successful recovery by sparsity promotion. In contrast to random (under)sampling, acquisition via jittered (under)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. Results are illustrated with simulations of optimally jittered marine acquisition, and periodic time-dithering marine acquisition.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/wason2012SINBADobs/wason2012SINBADobs_pres.pdf},
	author = {Haneet Wason}
}
@presentation {herrmann2012SINBADfwi,
	title = {Our findings on the Chevron benchmark dataset},
	year = {2012},
	publisher = {SINBAD},
	abstract = {During this presentation, we will review our findings working with the synthetic GOM data released as part of the post SEG workshop:"Gulf of Mexico Imaging Challenges: What Can Full Waveform Inversion Achieve?". This is joint work with Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Xiang Li, Brendan R. Smithyman, Eric Takam Takougang, and Haneet Wason.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADfwi/herrmann2012SINBADfwi_pres.pdf},
	author = {Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Xiang Li and Brendan R. Smithyman and Eric Takam Takougang and Haneet Wason and Felix J. Herrmann}
}
@presentation {wason2012SINBADrma,
	title = {Randomized marine acquisition for ocean-bottom surveys},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/wason2012SINBADrma/wason2012SINBADrma_pres.pdf},
	author = {Haneet Wason}
}
@presentation {friedlander2012SINBADrsh,
	title = {Randomized sampling: How confident are you?},
	year = {2012},
	publisher = {SINBAD},
	abstract = {At last year{\textquoteright}s consortium meeting, I described an inexact gradient method and sampling scheme for data fitting. The randomization method has good convergence properties, at least as measured by the distance to the solution{\textendash}{\textendash}in expectation. But as one insightful critic rightly pointed out, we don{\textquoteright}t usually observe the expectation, at least not in a single run. In this talk I will characterize the convergence of the method in terms of bounds on the probability of being too far away from the solution.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/friedlander2012SINBADrsh/friedlander2012SINBADrsh_pres.pdf},
	author = {Michael P. Friedlander}
}
@presentation {dasilva2012SINBADrdp,
	title = {Recent developments in preconditioning the wave-equation Hessian},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/dasilva2012SINBADrdp/dasilva2012SINBADrdp_pres.pdf},
	author = {Curt Da Silva}
}
@presentation {Lin2012SINBADrdr,
	title = {Recent developments on the robust estimation of primaries by sparse inversion},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Robust estimation of primaries by sparse inversion is a next-generation surface multiple removal technique with an objective to truly invert an operator that models the free-surface. Key to the success of this approach is the imposition of a sparsity constraint on the primary impulse response in the time domain. This is accomplished by carefully applying large-scale convex optimization techniques on an extended L1 minimization problem. One of the benefits of our approach is that many extensions to the algorithm can be devised under this optimization framework to improve the quality of the solution given fixed computational costs and mitigating various shortcomings in field data. This talk will first review the basic technique of Robust EPSI and follow with some highlights on recent further developments of the algorithm, including a discussion on the role of regularization by reciprocity and the interpolation of near-offset data, as well as investigations into optimality and robustness to data outliers.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/Lin2012SINBADrdr/Lin2012SINBADrdr_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {kumar2012SINBADsdi,
	title = {Seismic data interpolation using SVD free Pareto curve based low rank optimization},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Seismic data acquisition is cursed by missing data caused by physical and/or budget constraints. Aim of interpolation technique is to spatially transform irregularly acquired data to regularly sampled data while maintaining the events coherency. While transform-domain sparsity promotion has proven to be an effective tool to solve this recovery problem, recent developments in Rank penalizing techniques opens new horizon to improved recovery by exploiting low-rank structure. A major downside of current state of the art techniques is their reliance on the SVD of seismic data structures, which can be prohibitively expensive. Fortunately, recent work allows us to circumvent this problem by working with matrix factorizations. We review a novel approach to rank penalization, and successfully apply it to the seismic interpolation problem by exploiting the low-rank structure of seismic data. Experiments for the recovery of 2D and 3D acquisition support the feasibility and potential of the new approach.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/kumar2012SINBADsdi/kumar2012SINBADsdi_pres.pdf},
	author = {Rajiv Kumar}
}
@presentation {mansour2012SINBADsti,
	title = {Seismic trace interpolation via sparsity promoting reweighted algorithms},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Missing-trace interpolation aims to reconstruct regularly sampled wavefields from periodically sampled data with gaps caused by physical constraints. While transform-domain sparsity promotion has proven to be an effective tool to solve this recovery problem, current recovery techniques make no use of a priori information on the transform-domain coefficients. To overcome these vulnerabilities in solving the recovery problem for large-scale problems, we propose recovery by weighted one-norm minimization, which exploits correlations between locations of significant coefficients of different partitions, e.g., shot records, common-offset gathers, or frequency slices, of the acquired data. Moreover, in situations where no prior support estimate is available, we propose the WSPGL1 algorithm that outperforms standard $\ell_1$ minimization in finding sparse solutions to underdetermined linear systems of equations. Our algorithm is a modification of the SPGL1 algorithm and enjoys better sparse recovery performance at no additional computational cost. We illustrate the improved recovery using WSPGL1 for randomly subsampled seismic traces.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/mansour2012SINBADsti/mansour2012SINBADsti_pres.pdf},
	author = {Hassan Mansour}
}
@presentation {herrmann2012SINBADhpc,
	title = {SLIM{\textquoteright}s perspective on HPC \& big data},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADhpc/herrmann2012SINBADhpc_pres.pdf},
	author = {Tim T.Y. Lin and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {herrmann2012SINBADsls,
	title = {Supercool(ed) least-squares imaging: latest insights in sparsity-promoting migration},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/herrmann2012SINBADsls/herrmann2012SINBADsls_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {tamalet2012SINBADvpe,
	title = {Variance parameters estimation - application to full waveform inversion},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Many inverse problems include nuisance parameters. While not of direct interest, these parameters are required to recover primary parameters. In order to estimate these nuisance parameters as well as the primary parameters in large-scale inverse problems, a method based on variable projection, which consists in projecting out a subset over the variables, has been developed. We present here the application of this method to the problem of variance parameters estimation in multiple datasets, which is an important problem in many areas including geophysics. More precisely, we apply the method to Full Waveform Inversion and demonstrate the improvement in recovery of the model parameters in the case where the variance of the noise increases with the frequency.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/tamalet2012SINBADvpe/tamalet2012SINBADvpe_pres.pdf},
	author = {Anais Tamalet}
}
@presentation {xiang2012SINBADweb,
	title = {Wave-equation based inversion with joint sparsity promotion},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/xiang2012SINBADweb/xiang2012SINBADweb_pres.pdf},
	author = {Xiang Li}
}
@presentation {yilmaz2012SINBADwms,
	title = {Weighted methods in sparse recovery},
	year = {2012},
	publisher = {SINBAD},
	abstract = {In the recent years we have successfully employed "weighted" algorithms to recover sparse signals from few linear, non-adaptive measurements. The general principle here is to use prior knowledge about the signal to be recovered, e.g., approximate locations of large-in-magnitude transform coefficients, if such information is available. An example for this is the use of weighted 1-norm minimization to improve wavefield reconstruction from randomized (sub)sampling. We will review these results and outline some new directions we have explored during the last year, such as weighted non-convex sparse recovery (see Ghadermarzy{\textquoteright}s talk), weighted analysis-based recovery (see Hargreaves{\textquoteright}s talk), and a weighted randomized Kaczmarz algorithm for solving large overdetermined systems of equations that are known to admit a (nearly) sparse solution. Various examples in seismic will be shown.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/yilmaz2012SINBADwms/yilmaz2012SINBADwms_pres.pdf},
	author = {Ozgur Yilmaz}
}
@presentation {krislock2012SINBADwsn,
	title = {Wireless sensor network localization},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Locating the position of sensors connected together in a wireless network given only the position of a small number of the sensors and estimates of some of the distances between the sensors is a difficult problem with many modern applications. Within the last few years research in wireless sensor network localization has greatly increased due to the many new applications using wireless sensors, from lightweight sensors used to monitor the environment to ocean-bottom sensors used in geophysical applications. A second reason for this increased interest is our recent ability to efficiently solve these problems using modern semidefinite optimization solvers. We will discuss how semidefinite optimization can be used to solve such problems and possible directions for future work.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/krislock2012SINBADwsn/krislock2012SINBADwsn_pres.pdf},
	author = {Nathan Krislock}
}
@presentation {vanleeuwen2012SINBADyap,
	title = {Yet another perspective on image volumes},
	year = {2012},
	publisher = {SINBAD},
	abstract = {An extended image is defined as the multi-dimensional cross-correlation of the source and receiver wavefields used for imaging. This extended image will reveal velocity errors by de-focusing and can thus be used for velocity analysis. However, for optimal sensitivity to velocity errors, the subsurface offset has to be aligned with the local dip. As this dip is not known a priori, we consider forming the extended image for subsurface offsets in all directions. However, computing and storing such a large image volume is not computationally feasible. We organize the image volume in a matrix and use matrix-probing techniques to glean information form the matrix without explicitly forming it. A matrix-vector multiply with the image-volume matrix can be performed at the cost of two wave-equation solves and does not require any explicit cross-correlations of the wavefields. Such techniques can also be used to evaluate focusing penalties without forming the whole image volume. Finally, the matrix-viewpoint allows us to derive a 2-way equivalent of the DSR equation in a straightforward manner and provides a possible avenue for developing new velocity-continuation techniques.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/vanleeuwen2012SINBADyap/vanleeuwen2012SINBADyap_pres.pdf},
	author = {Tristan van Leeuwen}
}
@presentation {herrmann2011SINBADcoc,
	title = {Challenges and opportunities for compressive sensing in seismic acquisition},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADcoc/herrmann2011SINBADcoc_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2011SINBADcos,
	title = {Challenges and opportunities in sparse wavefield inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADcos/herrmann2011SINBADcos_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {yilmaz2011SINBADcsp,
	title = {Compressed sensing with prior information},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/yilmaz2011SINBADcsp/yilmaz2011SINBADcsp_pres.pdf},
	author = {Ozgur Yilmaz}
}
@presentation {jumah2011SINBADdrEPSI,
	title = {Dimensionality-reduced estimation of primaries by sparse inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/jumah2011SINBADdrEPSI/jumah2011SINBADdrEPSI_pres.pdf},
	author = {Bander Jumah}
}
@presentation {herrmann2011SINBADdre,
	title = {Dimensionality-reduced estimation of primaries by sparse inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBASPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/herrmann2011SINBADdre/herrmann2011SINBADdre_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {li2011SINBADels,
	title = {Efficient least-squares imaging with sparsity promotion and compressive sensing},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/li2011SINBADels/li2011SINBADels_pres.pdf},
	author = {Xiang Li}
}
@article {kumar2010TNPecr,
	title = {Enhancing crustal reflection data through curvelet denoising},
	journal = {Technophysics},
	volume = {508},
	number = {1-4},
	year = {2011},
	month = {07},
	pages = {106-116},
	abstract = {Suppression of incoherent noise, which is present in the seismic signal and may often lead to ambiguous interpretation, is a key step in processing associated with crustal reflection data. In this paper, we make use of the parsimonious representation of seismic data in the curvelet domain to perform the noise attenuation while preserving the coherent energy and its amplitude information. Curvelets are a recently developed mathematical transform that has as one of its properties minimal overlap between seismic signal and noise in the transform domain, thereby facilitating signal-noise separation. The problem is cast as an inverse problem and the results are obtained by updating the solution at each iteration. We demonstrate the effectiveness of this procedure at removing noise on both synthetic shot gathers and a synthetic stacked seismic section. We then apply curvelet denoising to deep crustal seismic reflection data where the signal-to-noise ratio is low. The reflection data were recorded along Lithoprobe{\textquoteright}s SNORCLE Line 1 across Paleoproterozoic-Archean domains in Canada{\textquoteright}s Northwest Territories. After initial processing, we apply the iterative curvelet denoising to both pre-stack shot gathers and post-stack data. Ground roll, random noise and much of the anomalous vertical energy is removed from the pre-stack shot gathers, to the extent that crustal reflections, including those from the Moho, are clearly seen on individual gathers. Denoised stacked data show a series of dipping reflections in the lower crust that extend into the Moho. The Moho itself is relatively flat and characterized by a sharp, narrow band of reflections. Comparing the results for the stacked data with those from F-X deconvolution, curvelet denoising outperforms the latter by attenuating incoherent noise with minimal harm to the signal. Because curvelet denoising retains amplitude information, it provides opportunities for further studies of seismic sections through attribute analyses. Curvelet denoising provides an important new tool in the processing toolbox for crustal seismic reflection data.},
	keywords = {Processing, SLIM},
	doi = {10.1016/j.tecto.2010.07.01},
	url = {http://www.sciencedirect.com/science/article/pii/S0040195110003227},
	author = {Vishal Kumar and Jounada Oueity and Ron Clowes and Felix J. Herrmann}
}
@presentation {aravkin2011SINBADesp,
	title = {Extensions to sparsity promotion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADesp/aravkin2011SINBADesp_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@conference {herrmann2011SEGffw,
	title = {Fast full-waveform inversion with compressive sensing},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2011},
	keywords = {Full-waveform inversion, SEG, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/HerrmannSEG2011fws/HerrmannSEG2011fws.pdf},
	author = {Aleksandr Y. Aravkin and Felix J. Herrmann and Tristan van Leeuwen and Xiang Li}
}
@presentation {vanleeuwen2011SINBADfwi,
	title = {Fast waveform inversion without source encoding},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/vanleeuwen2011SINBADfwi/vanleeuwen2011SINBADfwi_pres.pdf},
	author = {Tristan van Leeuwen}
}
@conference {aravkin2011SIAMfwi,
	title = {Full waveform inversion with compressive updates},
	booktitle = {SIAM},
	year = {2011},
	publisher = {SIAM CS\&E 2011},
	organization = {SIAM CS\&E 2011},
	abstract = {Full-waveform inversion relies on large multi-experiment data volumes. While improvements in acquisition and inversion have been extremely successful, the current push for higher quality models reveals fundamental shortcomings handling increasing problem sizes numerically. To address this fundamental issue, we propose a randomized dimensionality-reduction strategy motivated by recent developments in stochastic optimization and compressive sensing. In this formulation conventional Gauss-Newton iterations are replaced by dimensionality-reduced sparse recovery problems with source encodings.},
	keywords = {Full-waveform inversion, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2011/aravkin2011SIAMfwi/aravkin2011SIAMfwi.pdf},
	author = {Aleksandr Y. Aravkin and Felix J. Herrmann and Tristan van Leeuwen and James V. Burke and Xiang Li}
}
@presentation {vanleeuwen2011SINBADfwiso,
	title = {A hybrid stochastic-deterministic method for waveform inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/vanleeuwen2011SINBADfwiso/vanleeuwen2011SINBADfwiso_pres.pdf},
	author = {Tristan van Leeuwen}
}
@presentation {lin2011SINBADirEPSI,
	title = {Inside the robust EPSI formulation},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/lin2011SINBADirEPSI/lin2011SINBADirEPSI_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {vanderneut2011SINBADirs,
	title = {Interferometric redatuming by sparse inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/vanderneut2011SINBADirs/vanderneut2011SINBADirs_pres.pdf},
	author = {Joost van der Neut}
}
@presentation {ning2011SINBADmsr,
	title = {Migration from surface-related multiples},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/ning2011SINBADmsr/ning2011SINBADmsr_pres.pdf},
	author = {Ning Tu}
}
@conference {herrmann2011SPIEmsp,
	title = {A modified, sparsity promoting, Gauss-Newton algorithm for seismic waveform inversion},
	booktitle = {Proc. SPIE},
	number = {81380V},
	year = {2011},
	month = {08},
	abstract = {Images obtained from seismic data are used by the oil and gas industry for geophysical exploration. Cutting-edge methods for transforming the data into interpretable images are moving away from linear approximations and high-frequency asymptotics towards Full Waveform Inversion (FWI), a nonlinear data-fitting procedure based on full data modeling using the wave-equation. The size of the problem, the nonlinearity of the forward model, and ill-posedness of the formulation all contribute to a pressing need for fast algorithms and novel regularization techniques to speed up and improve inversion results. In this paper, we design a modified Gauss-Newton algorithm to solve the PDE- constrained optimization problem using ideas from stochastic optimization and compressive sensing. More specifically, we replace the Gauss-Newton subproblems by randomly subsampled, -$\ell_1$ regularized subproblems. This allows us us significantly reduce the computational cost of calculating the updates and exploit the compressibility of wavefields in Curvelets. We explain the relationships and connections between the new method and stochastic optimization and compressive sensing (CS), and demonstrate the efficacy of the new method on a large-scale synthetic seismic example.},
	keywords = {Compressive Sensing, Full-waveform inversion, Optimization, SLIM},
	issn = {1},
	doi = {10.1117/12.893861},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SPIE/2011/herrmann2011SPIEmsp/herrmann2011SPIEmsp.pdf},
	author = {Felix J. Herrmann and Xiang Li and Aleksandr Y. Aravkin and Tristan van Leeuwen}
}
@presentation {wason2011SINBADode,
	title = {Only dither: efficient marine acquisition {\textquoteleft}{\textquoteleft}without" simultaneous sourcing},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/wason2011SINBADode/wason2011SINBADode_pres.pdf},
	author = {Haneet Wason}
}
@presentation {min2011SINBADpss,
	title = {Parameter-selection strategy for density in frequency-domain elastic waveform inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/min2011SINBADpss/min2011SINBADpss_pres.pdf},
	author = {Dong-Joo Min}
}
@presentation {vanleeuwen2011SINBADpei,
	title = {Probing the extended image volume},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/vanleeuwen2011SINBADpei/vanleeuwen2011SINBADpei_pres.pdf},
	author = {Tristan van Leeuwen}
}
@presentation {aravkin2011SINBADrsp,
	title = {A randomized, sparsity promoting, Gauss-Newton algorithm for seismic waveform inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrsp/aravkin2011SINBADrsp_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrsp/li2011SINBADrsp_pres.pdf},
	author = {Aleksandr Y. Aravkin and Xiang Li}
}
@presentation {dasilva2011SINBADrdp,
	title = {Recent developments in preconditioning the FWI Hessian},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/dasilva2011SINBADrdp/dasilva2011SINBADrdp_pres.pdf},
	author = {Curt Da Silva}
}
@presentation {lin2011SINBADrep,
	title = {Robust EPSI in a curvelet-like representation domain},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/lin2011SINBADrep/lin2011SINBADrep_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {aravkin2011SINBADrfwi,
	title = {Robust FWI using Student{\textquoteright}s t \& robust source estimation},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrfwi/aravkin2011SINBADrfwi_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@conference {aravkin2011ICIAMrfwiu,
	title = {Robust FWI using Student{\textquoteright}s t-distribution},
	booktitle = {ICIAM},
	year = {2011},
	month = {07},
	publisher = {ICIAM 2011},
	organization = {ICIAM 2011},
	abstract = {Iterative inversion algorithms require repeated simulation of 3D time-dependent acoustic, elastic, or electromagnetic wave fields, extending hundreds of wavelengths and hundreds of periods. Also, seismic data is rich in information at every representable scale. Thus simulation-driven optimization approaches to inversion impose great demands on simulator efficiency and accuracy. While computer hardware advances have been of critical importance in bringing inversion closer to practical application, algorithmic advances in simulator methodology have been equally important. Speakers in this two-part session will address a variety of numerical issues arising in the wave simulation, and in its application to inversion.},
	keywords = {Full-waveform inversion, ICIAM, Optimization, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICIAM/2011/aravkin2011ICIAMrfwiu/aravkin2011ICIAMrfwiu.pdf},
	author = {Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {aravkin2011SINBADSPRINGrfwi,
	title = {Robust FWI using Student{\textquoteright}s t-distribution},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/aravkin2011SINBADSPRINGrfwi/aravkin2011SINBADSPRINGrfwi_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@presentation {friedlander2011SINBADrir,
	title = {Robust inversion, data-fitting, and inexact gradient methods},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/friedlander2011SINBADrir/friedlander2011SINBADrir_pres.pdf},
	author = {Michael P. Friedlander}
}
@article {vanLeeuwen2010IJGswi,
	title = {Seismic waveform inversion by stochastic optimization},
	journal = {International Journal of Geophysics},
	volume = {2011},
	year = {2011},
	note = {Article ID: 689041, 18pages},
	month = {12},
	abstract = {We explore the use of stochastic optimization methods for seismic waveform inversion. The basic principle of such methods is to randomly draw a batch of realizations of a given misfit function and goes back to the 1950s. The ultimate goal of such an approach is to dramatically reduce the computational cost involved in evaluating the misfit. Following earlier work, we introduce the stochasticity in waveform inversion problem in a rigorous way via a technique called randomized trace estimation. We then review theoretical results that underlie recent developments in the use of stochastic methods for waveform inversion. We present numerical experiments to illustrate the behavior of different types of stochastic optimization methods and investigate the sensitivity to the batch size and the noise level in the data. We find that it is possible to reproduce results that are qualitatively similar to the solution of the full problem with modest batch sizes, even on noisy data. Each iteration of the corresponding stochastic methods requires an order of magnitude fewer PDE solves than a comparable deterministic method applied to the full problem, which may lead to an order of magnitude speedup for waveform inversion in practice.},
	keywords = {FWI, Optimization, SLIM},
	doi = {10.1155/2011/689041},
	url = {https://slim.gatech.edu/Publications/Public/Journals/InternationJournalOfGeophysics/2011/vanLeeuwen10IJGswi/vanLeeuwen10IJGswi.pdf},
	author = {Tristan van Leeuwen and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@presentation {lin2011SINBADslim,
	title = {SLIM{\textquoteright}s software design principles},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/lin2011SINBADslim/lin2011SINBADslim_pres.pdf},
	author = {Tim T.Y. Lin}
}
@conference {aravkin2011ICIAMspf,
	title = {Sparsity promoting formulations and algorithms for FWI},
	booktitle = {ICIAM},
	year = {2011},
	note = {Presented at AMP Medical and Seismic Imaging, 2011, Vancouver BC.},
	month = {07},
	publisher = {ICIAM 2011},
	organization = {ICIAM 2011},
	abstract = {Full Waveform Inversion (FWI) is a computational procedure to extract medium parameters from seismic data. FWI is typically formulated as a nonlinear least squares optimization problem, and various regularization techniques are used to guide the optimization because the problem is ill-posed. We propose a novel sparse regularization which exploits the ability of curvelets to efficiently represent geophysical images. We then formulate a corresponding sparsity promoting constrained optimization problem, which we solve using an open source algorithm. The techniques are applicable to any inverse problem where sparsity modeling is appropriate. We demonstrate the efficacy of the formulation on a toy example (stylized cross-well experiment) and on a realistic Seismic example (partial Marmoussi model). We also discuss the tradeoff between model fit and sparsity promotion, with a view to extend existing techniques for linear inverse problems to the case where the forward model is nonlinear.},
	keywords = {Full-waveform inversion, Optimization, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICIAM/2011/aravkin2011ICIAMspf/aravkin2011ICIAMspf_pres.pdf},
	author = {Aleksandr Y. Aravkin and Tristan van Leeuwen and James V. Burke and Felix J. Herrmann}
}
@presentation {herrmann2011SINBADrnr,
	title = {To redraw or not to redraw: recent insights in randomized dimensionality reduction for inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADrnr/herrmann2011SINBADrnr_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {vanleeuwen2011SINBADtem,
	title = {Towards extended modelling for velocity inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/vanleeuwen2011SINBADtem/vanleeuwen2011SINBADtem_pres.pdf},
	author = {Tristan van Leeuwen}
}
@presentation {vanleeuwen2011SINBADoofwi,
	title = {Wavefield modelling and inversion in Matlab},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/vanleeuwen2011SINBADoofwi/vanleeuwen2011SINBADoofwi_pres.pdf},
	author = {Tristan van Leeuwen}
}
@presentation {herrmann2011SINBADoverview,
	title = {Welcome and overview of SINBAD \& DNOISE},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADoverview/herrmann2011SINBADoverview_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {mansour2011SINBADwcw,
	title = {Why do curvelets work?},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/mansour2011SINBADwcw/mansour2011SINBADwcw_pres.pdf},
	author = {Hassan Mansour}
}
@presentation {friedlander2010SINBADaso,
	title = {Algorithms for sparse optimization},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/friedlander2010SINBADaso/friedlander2010SINBADaso_pres.pdf},
	author = {Michael P. Friedlander}
}
@presentation {saab2010SINBADcsk,
	title = {Compressed sensing using Kronecker products},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/saab2010SINBADcsk/saab2010SINBADcsk_pres.pdf},
	author = {Rayan Saab}
}
@presentation {li2010SINBADci,
	title = {Compressive imaging},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/li2010SINBADci/li2010SINBADci_pres.pdf},
	author = {Xiang Li}
}
@presentation {herrmann2010SINBADcss,
	title = {Compressive sensing and sparse recovery in exploration seismology},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/herrmann2010SINBADcss/herrmann2010SINBADcss_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2010SINBADdrf,
	title = {Dimensionality reduction for full-waveform inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/herrmann2010SINBADdrf/herrmann2010SINBADdrf_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {almatar2010SINBADesf,
	title = {Estimation of surface-free data by curvelet-domain matched filtering and sparse inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/almatar2010SINBADesf/almatar2010SINBADesf_pres.pdf},
	author = {Mufeed H. AlMatar}
}
@presentation {aravkin2010SINBADesf,
	title = {Exploiting sparsity in full-waveform inversion: nonlinear basis pursuit denoise algorithm},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/aravkin2010SINBADesf/aravkin2010SINBADesf_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@presentation {li2010SINBADfwi,
	title = {Full-waveform inversion with randomized L1 recovery for the model updates},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/li2010SINBADfwi/li2010SINBADfwi_pres.pdf},
	author = {Xiang Li}
}
@presentation {schmidt2010SINBADhsd,
	title = {Hybrid stochastic-deterministic methods},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/schmidt2010SINBADhsd/schmidt2010SINBADhsd_pres.pdf},
	author = {Mark Schmidt}
}
@presentation {aravkin2010SINBADicc,
	title = {Introduction to convex composite optimization},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/aravkin2010SINBADicc/aravkin2010SINBADicc_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@presentation {friedlander2010SINBADisl,
	title = {Introduction to Spot: a linear-operator toolbox},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/friedlander2010SINBADisl/friedlander2010SINBADisl_pres.pdf},
	author = {Michael P. Friedlander}
}
@presentation {pacteau2010SINBADkpo,
	title = {Kronecker product optimization in SPOT},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/pacteau2010SINBADkpo/pacteau2010SINBADkpo_pres.pdf},
	author = {Sebastien Pacteau}
}
@presentation {lin2010SINBADlib,
	title = {Leveraging informed blind deconvolution techniques for the estimation of primaries by sparse inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/lin2010SINBADlib/lin2010SINBADlib_pres.pdf},
	author = {Tim T.Y. Lin}
}
@article {hennenfent2010GEOPnct,
	title = {Nonequispaced curvelet transform for seismic data reconstruction: a sparsity-promoting approach},
	journal = {Geophysics},
	volume = {75},
	number = {6},
	year = {2010},
	month = {12},
	pages = {WB203-WB210},
	publisher = {SEG},
	abstract = {We extend our earlier work on the nonequispaced fast discrete curvelet transform (NFDCT) and introduce a second generation of the transform. This new generation differs from the previous one by the approach taken to compute accurate curvelet coefficients from irregularly sampled data. The first generation relies on accurate Fourier coefficients obtained by an l2-regularized inversion of the nonequispaced fast Fourier transform (FFT) whereas the second is based on a direct l1-regularized inversion of the operator that links curvelet coefficients to irregular data. Also, by construction the second generation NFDCT is lossless unlike the first generation NFDCT. This property is particularly attractive for processing irregularly sampled seismic data in the curvelet domain and bringing them back to their irregular record-ing locations with high fidelity. Secondly, we combine the second generation NFDCT with the standard fast discrete curvelet transform (FDCT) to form a new curvelet-based method, coined nonequispaced curvelet reconstruction with sparsity-promoting inversion (NCRSI) for the regularization and interpolation of irregularly sampled data. We demonstrate that for a pure regularization problem the reconstruction is very accurate. The signal-to-reconstruction error ratio in our example is above 40 dB. We also conduct combined interpolation and regularization experiments. The reconstructions for synthetic data are accurate, particularly when the recording locations are optimally jittered. The reconstruction in our real data example shows amplitudes along the main wavefronts smoothly varying with limited acquisition imprint.},
	keywords = {curvelet transforms, data acquisition, geophysical techniques, Processing, seismology, SLIM},
	doi = {10.1190/1.3494032},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2010/hennenfent2010GEOPnct/hennenfent2010GEOPnct.pdf},
	author = {Gilles Hennenfent and Lloyd Fenelon and Felix J. Herrmann}
}
@presentation {kumar2010SINBADpoe,
	title = {Parallelizing operations with ease using Parallel SPOT},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/kumar2010SINBADpoe/kumar2010SINBADpoe_pres.pdf},
	author = {Nameet Kumar}
}
@presentation {moghaddam2010SINBADrfwi,
	title = {Randomized full-waveform inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/moghaddam2010SINBADrfwi/moghaddam2010SINBADrfwi_pres.pdf},
	author = {Peyman P. Moghaddam}
}
@article {herrmann2010GEOPrsg,
	title = {Randomized sampling and sparsity: getting more information from fewer samples},
	journal = {Geophysics},
	volume = {75},
	number = {6},
	year = {2010},
	month = {12},
	pages = {WB173-WB187},
	publisher = {SEG},
	abstract = {Many seismic exploration techniques rely on the collection of massive data volumes that are subsequently mined for information during processing. Although this approach has been extremely successful in the past, current efforts toward higher-resolution images in increasingly complicated regions of the earth continue to reveal fundamental shortcomings in our workflows. Chiefly among these is the so-called {\textquotedblleft}curse of dimensionality{\textquotedblright} exemplified by Nyquist{\textquoteright}s sampling criterion, which disproportionately strains current acquisition and processing systems as the size and desired resolution of our survey areas continue to increase. We offer an alternative sampling method leveraging recent insights from compressive sensing toward seismic acquisition and processing for data that are traditionally considered to be undersampled. The main outcome of this approach is a new technology where acquisition and processing related costs are no longer determined by overly stringent sampling criteria, such as Nyquist. At the heart of our approach lies randomized incoherent sampling that breaks subsampling related interferences by turning them into harmless noise, which we subsequently remove by promoting transform-domain sparsity. Now, costs no longer grow significantly with resolution and dimensionality of the survey area, but instead depend only on transform-domain sparsity. Our contribution is twofold. First, we demonstrate by means of carefully designed numerical experiments that compressive sensing can successfully be adapted to seismic exploration. Second, we show that accurate recovery can be accomplished for compressively sampled data volumes sizes that exceed the size of conventional transform-domain data volumes by only a small factor. Because compressive sensing combines transformation and encoding by a single linear encoding step, this technology is directly applicable to acquisition and to dimensionality reduction during processing. In either case, sampling, storage, and processing costs scale with transform-domain sparsity. We illustrate this principle by means of number of case studies.},
	keywords = {Acquisition, Compressive Sensing, data acquisition, geophysical techniques, Nyquist criterion, Optimization, sampling methods, seismology, SLIM},
	doi = {10.1190/1.3506147},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2010/herrmann2010GEOPrsg/herrmann2010GEOPrsg.pdf},
	author = {Felix J. Herrmann}
}
@presentation {mansour2010SINBADrcs,
	title = {Recovering compressively sampled signals using partial support information},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/mansour2010SINBADrcs/mansour2010SINBADrcs_pres.pdf},
	author = {Hassan Mansour}
}
@presentation {wason2010SINBADssd,
	title = {Sequential source data recovery from simultaneous acquisition through transform-domain sparsity promotion - curvelet versus shearlet transform},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/wason2010SINBADssd/wason2010SINBADssd_pres.pdf},
	author = {Haneet Wason}
}
@presentation {lin2010SINBADsre,
	title = {Software release: estimation of primaries by L1 inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	author = {Tim T.Y. Lin}
}
@presentation {modzelewski2010SINBADsra,
	title = {Software releases and architecture},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/modzelewski2010SINBADsra/modzelewski2010SINBADsra_pres.pdf},
	author = {Henryk Modzelewski}
}
@presentation {yilmaz2010SINBADsac,
	title = {Sparse approximations and compressive sensing: an overview},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/yilmaz2010SINBADsac/yilmaz2010SINBADsac_pres.pdf},
	author = {Ozgur Yilmaz}
}
@presentation {lin2010SINBADsol,
	title = {Sparse optimization and the L1 norm},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/lin2010SINBADsol/lin2010SINBADsol_pres.pdf},
	author = {Tim T.Y. Lin}
}
@presentation {tu2010SINBADspm,
	title = {Sparsity promoting migration with surface-related multiples},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/tu2010SINBADspm/tu2010SINBADspm_pres.pdf},
	author = {Ning Tu}
}
@presentation {vanleeuwen2010SINBADwis,
	title = {Waveform inversion by stochastic optimization},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/vanleeuwen2010SINBADwis/vanleeuwen2010SINBADwis_pres.pdf},
	author = {Tristan van Leeuwen}
}
@conference {friedlander2009NUalssr,
	title = {Algorithms for large-scale sparse reconstruction},
	booktitle = {IEMS},
	year = {2009},
	publisher = {IEMS Colloquim Speaker},
	organization = {IEMS Colloquim Speaker},
	address = {Northwestern University},
	keywords = {minimization, SLIM},
	author = {Michael P. Friedlander}
}
@article {herrmann2009GEOPcsf,
	title = {Compressive simultaneous full-waveform simulation},
	journal = {Geophysics},
	volume = {74},
	number = {4},
	year = {2009},
	month = {07-08},
	pages = {A35-A40},
	abstract = {The fact that computational complexity of wavefield simulation is proportional to the size of the discretized model and acquisition geometry, and not to the complexity of the simulated wavefield, is a major impediment within seismic imaging. By turning simulation into a compressive sensing problem{\textendash}-where simulated data is recovered from a relatively small number of independent simultaneous sources{\textendash}-we remove this impediment by showing that compressively sampling a simulation is equivalent to compressively sampling the sources, followed by solving a reduced system. As in compressive sensing, this allows for a reduction in sampling rate and hence in simulation costs. We demonstrate this principle for the time-harmonic Helmholtz solver. The solution is computed by inverting the reduced system, followed by a recovery of the full wavefield with a sparsity promoting program. Depending on the wavefield{\textquoteright}s sparsity, this approach can lead to significant cost reductions, in particular when combined with the implicit preconditioned Helmholtz solver, which is known to converge even for decreasing mesh sizes and increasing angular frequencies. These properties make our scheme a viable alternative to explicit time-domain finite-differences.},
	keywords = {Compressive Sensing, full-waveform, Modelling, SLIM},
	doi = {10.1190/1.3115122},
	url = {http://library.seg.org/doi/abs/10.1190/1.3115122},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2009/herrmann2009GEOPcsf/herrmann2009GEOPcsf.pdf},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin}
}
@conference {friedlander2009VIETcsgpa,
	title = {Computing sparse and group-sparse approximations},
	booktitle = {VIET},
	year = {2009},
	publisher = {2009 High Performance Scientific Computing Conference},
	organization = {2009 High Performance Scientific Computing Conference},
	address = {Hanoi, Vietnam},
	keywords = {minimization, SLIM},
	author = {Michael P. Friedlander}
}
@article {herrmann2009GEOPcbm,
	title = {Curvelet-based migration preconditioning and scaling},
	journal = {Geophysics},
	volume = {74},
	year = {2009},
	month = {07-08},
	pages = {A41-A46},
	abstract = {The extremely large size of typical seismic imaging problems has been one of the major stumbling blocks for iterative techniques to attain accurate migration amplitudes. These iterative methods are important because they complement theoretical approaches that are hampered by difficulties to control problems such as finite-acquisition aperture, source-receiver frequency response, and directivity. To solve these problems, we apply preconditioning, which significantly improves convergence of least-squares migration. We discuss different levels of preconditioning that range from corrections for the order of the migration operator to corrections for spherical spreading, and position and reflector-dip dependent amplitude errors. While the first two corrections correspond to simple scalings in the Fourier and physical domain, the third correction requires phase-space (space spanned by location and dip) scaling, which we carry out with curvelets. We show that our combined preconditioner leads to a significant improvement of the convergence of least-squares {\textquoteleft}wave-equation{\textquoteright} migration on a line from the SEG AA{\textquoteright} salt model.},
	keywords = {Imaging, migration, SLIM},
	doi = {10.1190/1.3124753},
	url = {http://geophysics.geoscienceworld.org/content/74/4/A41.abstract},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2009/herrmann2009GEOPcbm/herrmann2009GEOPcbm.pdf},
	author = {Felix J. Herrmann and Cody R. Brown and Yogi A. Erlangga and Peyman P. Moghaddam}
}
@conference {vandenberg2009SLIMocf,
	title = {Optimizing costly functions with simple constraints: a limited-memory projected Quasi-Newton algorithm},
	booktitle = {SLIM},
	series = {Twelfth International Conference on Artificial Intelligence and Statistics},
	volume = {12},
	year = {2009},
	month = {04},
	abstract = {An optimization algorithm for minimizing a smooth function over a convex set is described. Each iteration of the method computes a descent direction by minimizing, over the original constraints, a diagonal-plus low-rank quadratic approximation to the function. The quadratic approximation is constructed using a limited-memory quasi-Newton update. The method is suitable for large-scale problems where evaluation of the function is substan- tially more expensive than projection onto the constraint set. Numerical experiments on one-norm regularized test problems indicate that the proposed method is competitve with state-of-the-art methods such as bound-constrained L-BFGS and orthant-wise descent. We further show that the method generalizes to a wide class of problems, and substantially improves on state-of-the-art methods for problems such as learning the structure of Gaussian graphi- cal models (involving positive-definite matrix constraints) and Markov random fields (involving second-order cone constraints).},
	keywords = {SLIM},
	url = {http://www.cs.ubc.ca/ mpf/papers/SchmidtBergFriedMurph09.pdf},
	author = {Ewout van den Berg and Mark Schmidt and Michael P. Friedlander and K. Murphy}
}
@conference {friedlander2009SCAIMspot,
	title = {Spot: A linear-operator toolbox for Matlab},
	booktitle = {SCAIM},
	year = {2009},
	publisher = {SCAIM Seminar},
	organization = {SCAIM Seminar},
	address = {University of British Columbia},
	keywords = {minimization, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2009/VandenBerg-Mon-1130.pdf},
	author = {Ewout van den Berg and Michael P. Friedlander}
}
@conference {friedlander2008SIAMasa,
	title = {Active-set approaches to basis pursuit denoising},
	booktitle = {SIAM Optimization},
	year = {2008},
	month = {05},
	publisher = {SIAM Optimization},
	organization = {SIAM Optimization},
	keywords = {SLIM},
	url = {http://www.cs.ubc.ca/ mpf/public/mpf08siopt.pdf},
	author = {Michael P. Friedlander}
}
@conference {herrmann2008SINBADacd2,
	title = {Adaptive curvelet-domain primary-multiple separation},
	booktitle = {SINBAD},
	year = {2008},
	note = {SINBAD 2008},
	publisher = {SINBAD},
	organization = {SINBAD},
	abstract = {In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by Surface-Related Multiple Elimination (SRME), amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple differently (as a function of offset, time and dip), these amplitude errors pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale (frequency band) and angle. In that case, the amplitudes can be corrected by an element-wise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to a successful estimation of the primaries, despite amplitude, sign, timing and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching, in terms of better suppression of multiple energy and high-frequency clutter and better recovery of the estimated primaries.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADacd2/herrmann2008SINBADacd2.pdf},
	author = {Felix J. Herrmann}
}
@article {herrmann2008GEOPacd,
	title = {Adaptive curvelet-domain primary-multiple separation},
	journal = {Geophysics},
	volume = {73},
	number = {3},
	year = {2008},
	month = {08},
	pages = {A17-A21},
	publisher = {SEG},
	abstract = {In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by surface-related multiple elimination (SRME), amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple in different ways (as a function of offset, time, and dip), they pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale (frequency band), and angle. With this method, the amplitudes can be corrected by an elementwise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to successful estimation of primaries, despite amplitude, sign, timing, and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching in terms of better suppression of multiple energy and high-frequency clutter and better recovery of estimated primaries. \copyright2008 Society of Exploration Geophysicists},
	keywords = {Geophysics, Processing, SLIM},
	doi = {10.1190/1.2904986},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/herrmann08GEOacd/herrmann08GEOacd.pdf},
	author = {Felix J. Herrmann and Deli Wang and D. J. Verschuur}
}
@conference {friedlander2008SINBADafl,
	title = {Algorithms for large-scale sparse reconstruction},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {Many signal processing applications seek to approximate a signal as a linear combination of only a few elementary atoms drawn from a large collection. This is known as sparse reconstruction, and the theory of compressed sensing allows us to pose it as a structured convex optimization problem. I will discuss the role of duality in revealing some unexpected and useful properties of these problems, and will show how they can lead to practical, large-scale algorithms. I will also describe some applications of these algorithms.},
	keywords = {SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/friedlander2008SINBADafl/friedlander2008SINBADafl.pdf},
	author = {Michael P. Friedlander}
}
@conference {lebed2008SINBADaoc1,
	title = {Applications of curvelets/surfacelets to seismic data processing},
	booktitle = {SINBAD},
	year = {2008},
	abstract = {In this talk we explore several applications of the curvelet and surfacelet transforms to seismic data processing. The first application is stable signal recovery in the physical domain - seismic data acquisition is often limited by physical and economic constraints, and the goal is to interpolate the data from a given subset of seismic traces. The second application is signal recovery in a transform domain - we assume that our data comes in a form of a random subset of temporal frequencies and the goal is to recover the missing frequencies from this data. Since seismic signals are generally not bandwidth limited, this in fact becomes an anti-aliasing problem. In both these problems the recovery is resolved via a robust $\ell$_1 solver that exploits the sparsity of the signals in curvelet/surfacelet domains. In the last application we explore the problem of primary-multiple separation by simple thresholding.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/lebed2008SINBADaoc1/lebed2008SINBADaoc1.pdf},
	author = {Evgeniy Lebed}
}
@conference {yarham2008SEGbgr,
	title = {Bayesian ground-roll seperation by curvelet-domain sparsity promotion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2576-2580},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The removal of coherent noise generated by surface waves in land based seismic is a prerequisite to imaging the subsurface. These surface waves, termed as ground roll, overlay important reflector information in both the t-x and f-k domains. Standard techniques of ground-roll removal commonly alter reflector information. We propose the use of the curvelet domain as a sparsifying transform in which to preform signal-separation techniques that preserves reflector information while increasing ground-roll removal. We look at how this method preforms on synthetic data for which we can build quantitative results and a real field data set.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3063878},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/yarham08SEGbgr/yarham08SEGbgr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/yarham08SEGbgr/yarham08SEGbgr_pres.pdf},
	author = {Carson Yarham and Felix J. Herrmann}
}
@conference {yarham2008SINBADbss,
	title = {Bayesian signal separation applied to ground-roll removal},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {Accurate and adaptive noise removal is a critical part in seismic processing. Recent developments in signal separation methods have allowed a more flexible and accurate framework in which to perform ground roll and reflector separation. The use of a new Bayesian separation scheme developed at the SLIM group that contains control parameters to adjust for the uniqueness of specific problems is used. The sensitivity and variation of the control parameters is examined and this method is applied to synthetic and real data and the results are compared to previous methods.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/yarham2008SINBADbss/yarham2008SINBADbss.pdf},
	author = {Carson Yarham}
}
@article {wang2008GEOPbws,
	title = {Bayesian wavefield separation by transform-domain sparsity promotion},
	journal = {Geophysics},
	volume = {73},
	number = {5},
	year = {2008},
	month = {07},
	pages = {1-6},
	abstract = {Successful removal of coherent noise sources greatly determines the quality of seismic imaging. Major advances were made in this direction, e.g., Surface-Related Multiple Elimination (SRME) and interferometric ground-roll removal. Still, moderate phase, timing, amplitude errors and clutter in the predicted signal components can be detrimental. Adopting a Bayesian approach along with the assumption of approximate curvelet-domain independence of the to-be-separated signal components, we construct an iterative algorithm that takes the predictions produced by for example SRME as input and separates these components in a robust fashion. In addition, the proposed algorithm controls the energy mismatch between the separated and predicted components. Such a control, which was lacking in earlier curvelet-domain formulations, produces improved results for primary-multiple separation on both synthetic and real data.},
	keywords = {curvelet transform, Geophysics, Optimization, Processing, SLIM},
	doi = {10.1190/1.2952571},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/wang08GEObws/wang08GEObws.pdf},
	html_version = {https://slim.gatech.edu/Publications/Public/Journals/2008/wang08TRbss/paper_html/paper.html},
	author = {Deli Wang and Rayan Saab and Ozgur Yilmaz and Felix J. Herrmann}
}
@mastersthesis {lin08THccl,
	title = {Compressed computation of large-scale wavefield extrapolation in inhomogeneous medium},
	year = {2008},
	month = {04},
	school = {University of British Columbia},
	type = {mastersmasters},
	abstract = {In this work an explicit algorithm for the extrapolation of one-way wavefields is proposed which combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3-D. By using ideas from {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright}, we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. Compressed sensing entails a new paradigm for signal recovery that provides conditions under which signals can be recovered from incomplete samplings by \emphnonlinear recovery methods that promote sparsity of the to-be-recovered signal. According to this theory, signals can successfully be recovered when the measurement basis is \emphincoherent with the representation in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with sparsity transforms that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can successfully be extrapolated in the modal domain, despite evanescent wave modes. The degree to which the wavefield can be recovered depends on the number of missing (evanescent) wave modes and on the complexity of the wavefield. A proof of principle for the {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright} method is given for inverse wavefield extrapolation in 2-D. The results show that our method is stable, has reduced dip limitations and handles evanescent waves in inverse extrapolation.},
	keywords = {BSc, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2008/lin08THccl.pdf},
	author = {Tim T.Y. Lin}
}
@conference {lin2008SINBADcwe,
	title = {Compressed wavefield extrapolation},
	booktitle = {SINBAD},
	year = {2008},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/sites/data/Papers/lin08cwe.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {herrmann2008IONcsa,
	title = {Compressive sampling: a new paradigm for seismic data acquistion and processing?},
	booktitle = {ION},
	year = {2008},
	abstract = {Seismic data processing and imaging are firmly rooted in the well-established paradigm of regular Nyquist sampling. Faced with a typical uncooperative environment, practitioners of seismic data acquisition make all efforts to comply to this theory by creating regularly-sampled seismic-data volumes that are suitable for Fourier-based processing flows. The current advent of new alternative transform domains{\textendash}- such as the sparsifying curvelet domain, where seismic data is decomposed into localized, multiscale and multidirectional plane waves{\textendash}- opens the possibility to change this paradigm by no longer combating sampling irregularity but by embracing it. During this talk, we show that as long as seismic data volumes permit a compressible representation{\textendash}-i.e., data can be represented as a superposition of relatively few number of elementary waveforms{\textendash}- Nyquist sampling is unnecessary pessimistic. So far, nothing new, we all know from the work on Fourier- or other transform-based seismic-data regularization methodologies that wavefields can be recovered accurately from sub-Nyquist samplings through some sort of optimization procedure. What is new, however, are recent insights from the field of "compressive sampling", which dictate the conditions that guarantee or, at least, in practice provide conditions that favor sparsity-promoting recovery from sub-Nyquist sampling. Random sub-sampling, or to be more precise, jitter sub-sampling creates favorable conditions for curvelet-based recovery. We explain this phenomenon by arguing that this type of sampling leads to noisy data, hence our slogan "Simply denoise: wavefield reconstruction via jittered undersampling", where we bank on separating incoherent sub-sampling noise with curvelet-domain sparsity promotion. During our presentation, we introduce you to what curvelets are, why random jitter sampling is important and why this opens a pathway towards a new paradigm of curvelet-domain seismic data processing. Our claims will be supported by examples on synthetic and field data. This is joint work with Gilles Hennenfent, PhD. student at SLIM.},
	keywords = {ION, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/ION/herrmann2008IONcsa/herrmann08ion_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2008SIAMcsm,
	title = {Compressive sampling meets seismic imaging},
	booktitle = {SIAM},
	year = {2008},
	abstract = {Compressive sensing has led to fundamental new insights in the recovery of compressible signals from sub-Nyquist samplings. It is shown how jittered subsampling can be used to create favorable recovery conditions. Applications include mitigation of incomplete acquisitions and wavefield computations. While the former is a direct adaptation of compressive sampling, the latter application represents a new way of compressing wavefield extrapolation operators. Operators are not diagonalized but are compressively sampled reducing the computational costs.},
	keywords = {Presentation, SIAM, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2008/herrmann2008SIAMcsm/herrmann2008SIAMcsm.pdf},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin}
}
@conference {kumar2008SINBADcd,
	title = {Curvelet denoising},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {The separation of signal and noise is an important issue in seismic data processing. By noise we refer to the incoherent noise which is present in the data. In our case, we showed curvelets concentrate seismic signal energy in few significant coefficients unlike noise energy that is spread all over the coefficients. The sparsity of seismic data in the curvelet domain makes curvelets an ideal choice for separating the noise from the seismic data. In our approach the denoising problem is framed as curvelet-regularized inversion problem. After initial processing, we applied the algorithm to the poststack data and compared our results with conventional wavelet denoising.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/kumar2008SINBADcd/kumar2008SINBADcd.pdf},
	author = {Vishal Kumar}
}
@conference {lebed2008SINBADaoc,
	title = {Curvelet / Surfacelet comparison},
	booktitle = {SINBAD},
	year = {2008},
	abstract = {Curvelets and Surfacelets are two transforms that aim to achieve a multiscale and a multidirectional decomposition of arbitrary N-dimensional ($N>=2$) signals. While both transforms are Fourier-based, their construction is intrinsically different. In this talk we will give and overview of the construction of the two transforms, and explore their properties such as frequency domain / spatial domain coherence, sparsity, redundancy and computational complexity.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/lebed2008SINBADaoc/lebed2008SINBADaoc.pdf},
	author = {Evgeniy Lebed}
}
@conference {moghaddam2008SEGcbm,
	title = {Curvelet-based migration preconditioning},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2211-2215},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we introduce a preconditioner for seismic imaging{\textendash}-i.e., the inversion of the linearized Born scattering operator. This preconditioner approximately corrects for the {\textquoteleft}{\textquoteleft}square root{\textquoteright}{\textquoteright} of the normal{\textendash}-i.e., the demigration-migration operator. This approach consists of three parts, namely (i) a left preconditoner, defined by a fractional time integration designed to make the migration operator zero order, and two right preconditioners that apply (ii) a scaling in the physical domain accounting for a spherical spreading, and (iii) a curvelet-domain scaling that corrects for spatial and reflector-dip dependent amplitude errors. We show that a combination of these preconditioners lead to a significant improvement of the convergence for iterative least-squares solutions to the seismic imaging problem based on reverse-time migration operators.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3059325},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/moghaddam08SEGcbm/moghaddam08SEGcbm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/moghaddam08SEGcbm/moghaddam08SEGcbm_pres.pdf},
	author = {Peyman P. Moghaddam and Cody R. Brown and Felix J. Herrmann}
}
@conference {saab2008SINBADcps,
	title = {Curvelet-based primary-multiple separation from a Bayesian perspective},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We present a novel primary-multiple separation scheme which makes use of the sparsity of both primaries and multiples in a transform domain, such as the curvelet transform, to provide estimates of each. The proposed algorithm utilizes seismic data as well as the output of a preliminary step that provides (possibly) erroneous predictions of the multiples. The algorithm separates the signal components, i.e., the primaries and multiples, by solving an optimization problem that assumes noisy input data and can be derived from a Bayesian perspective. More precisely, the optimization problem can be arrived at via an assumption of a weighted Laplacian distribution for the primary and multiple coefficients in the transform domain and of white Gaussian noise contaminating both the seismic data and the preliminary prediction of the multiples, which both serve as input to the algorithm.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/saab2008SINBADcps/saab2008SINBADcp.pdf},
	author = {Rayan Saab}
}
@article {herrmann2008GEOPcbs,
	title = {Curvelet-based seismic data processing: a multiscale and nonlinear approach},
	journal = {Geophysics},
	volume = {73},
	number = {1},
	year = {2008},
	month = {03},
	pages = {A1-A5},
	publisher = {SEG},
	abstract = {Mitigating missing data, multiples, and erroneous migration amplitudes are key factors that determine image quality. Curvelets, little {\textquoteleft}{\textquoteleft}plane waves,{\textquoteright}{\textquoteright} complete with oscillations in one direction and smoothness in the other directions, sparsify a property we leverage explicitly with sparsity promotion. With this principle, we recover seismic data with high fidelity from a small subset (20\%) of randomly selected traces. Similarly, sparsity leads to a natural decorrelation and hence to a robust curvelet-domain primary-multiple separation for North Sea data. Finally, sparsity helps to recover migration amplitudes from noisy data. With these examples, we show that exploiting the curvelet{\textquoteright}s ability to sparsify wavefrontlike features is powerful, and our results are a clear indication of the broad applicability of this transform to exploration seismology. \copyright2008 Society of Exploration Geophysicists},
	keywords = {Acquisition, curvelet transform, Processing, SLIM},
	doi = {10.1190/1.2799517},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/herrmann08GEOcbs/herrmann08GEOcbs.pdf},
	author = {Felix J. Herrmann and Deli Wang and Gilles Hennenfent and Peyman P. Moghaddam}
}
@conference {herrmann2008SEGcdm,
	title = {Curvelet-domain matched filtering},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	number = {3643-3649},
	year = {2008},
	month = {08},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Matching seismic wavefields and images lies at the heart of many pre-/post-processing steps part of seismic imaging{\textendash}- whether one is matching predicted wavefield components, such as multiples, to the actual to-be-separated wavefield components present in the data or whether one is aiming to restore migration amplitudes by scaling, using an image-to-remigrated-image matching procedure to calculate the scaling coefficients. The success of these wavefield matching procedures depends on our ability to (i) control possible overfitting, which may lead to accidental removal of energy or to inaccurate image-amplitude corrections, (ii) handle data or images with nonunique dips, and (iii) apply subsequent wavefield separations or migraton amplitude corrections stably. In this paper, we show that the curvelet transform allows us to address all these issues by imposing smoothness in phase space, by using their capability to handle conflicting dips, and by leveraging their ability to represent seismic data and images sparsely. This latter property renders curvelet-domain sparsity promotion an effective prior.},
	keywords = {Presentation, SEG, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGcdm/herrmann08SEGcdm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGcdm/herrmann08SEGcdm_pres.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam and Deli Wang}
}
@conference {herrmann2008SEGcdm3,
	title = {Curvelet-domain matched filtering},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {3643-3649},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Matching seismic wavefields lies at the heart of seismic processing whether one is adaptively subtracting multiples predictions or groundroll. In both cases, the predictions are matched to the actual to-be-separated wavefield components in the observed data. The success of these wavefield matching procedures depends on our ability to (i) control possible overfitting, which may lead to accidental removal of primary energy, (ii) handle data with nonunique dips, and (iii) apply wavefield separation after matching stably. In this paper, we show that the curvelet transform allows us to address these issues by imposing smoothness in phase space, by using their capability to handle conflicting dips, and by leveraging their ability to represent seismic data sparsely.},
	keywords = {SEG, SLIM},
	doi = {10.1190/1.3064089},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/Herrmann08SEGcdm3/Herrmann08SEGcdm3.pdf},
	author = {Felix J. Herrmann}
}
@conference {kumar2008SINBADcrd,
	title = {Curvelet-regularized deconvolution},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {The removal of source signature from seismic data is an important step in seismic data processing. The Curvelet transform provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic reflectivity). In this approach the sparseness of reflectivity in Curvelet domain is used as a prior to stabilize the inversion process. Our Curvelet-regularized deconvolution algorithm uses recently developed SPGL1 solver which does adaptive sampling of the trade-off curve. We applied the algorithm on a synthetic example and compared our results with that of Spiky deconvolution approach.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/kumar2008SINBADcrd/kumar2008SINBADcrd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/kumar08SINBADcrd/kumar08SINBADcrd_pres.pdf},
	author = {Vishal Kumar}
}
@conference {kumar2008CSEGcrs,
	title = {Curvelet-regularized seismic deconvolution},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2008},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {There is an inherent continuity along reflectors of a seismic image. We use the recently introduced multiscale and multidirectional curvelet transform to exploit this continuity along reflectors for cases in which the assumption of spiky reflectivity may not hold. We show that such type of seismic reflectivity can be represented in the curvelet-domain by a vector whose entries decay rapidly. This curvelet-domain compression of reflectivity opens new perspectives towards solving classical problems in seismic processing including the deconvolution problem. In this paper, we present a formulation that seeks curvelet-domain sparsity for non-spiky reflectivity and we compare our results with those of spiky deconvolution.},
	keywords = {Presentation, SLIM},
	url = {http://slim.gatech.edu/Publications/Public/Conferences/CSEG/2008/kumar2008CSEGcrs/kumar2008CSEGcrs.pdf},
	presentation = {http://slim.gatech.edu/Publications/Public/Conferences/CSEG/2008/kumar2008CSEGcrs/kumar2008CSEGcrs_pres.pdf},
	author = {Vishal Kumar and Felix J. Herrmann}
}
@conference {kumar2008SEGdwc,
	title = {Deconvolution with curvelet-domain sparsity},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {1996-2000},
	publisher = {SEG},
	organization = {SEG},
	abstract = {There is an inherent continuity along reflectors of a seismic image. We use the recently introduced multiscale and multidirectional curvelet transform to exploit this continuity along reflectors for cases in which the assumption of spiky reflectivity may not hold. We show that such type of seismic reflectivity can be represented in the curvelet-domain by a vector whose entries decay rapidly. This curvelet-domain compression of reflectivity opens new perspectives towards solving classical problems in seismic processing including the deconvolution problem. In this paper, we present a formulation that seeks curvelet-domain sparsity for non-spiky reflectivity and we compare our results with those of spiky deconvolution.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3059287},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/kumar08SEGdwc/kumar08SEGdwc.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/kumar08SEGdwc/kumar08SEGdwc_pres.pdf},
	author = {Vishal Kumar and Felix J. Herrmann}
}
@conference {herrmann2008SINBADfwr,
	title = {(De)-Focused wavefield reconstructions},
	booktitle = {SINBAD 2008},
	year = {2008},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADfwr/herrmann2008SINBADfwr.pdf},
	author = {Felix J. Herrmann}
}
@conference {modzelewski2008SINBADdas,
	title = {Design and specifications for SLIM{\textquoteright}s software framework},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {The SLIM group is actively developing software for seismic imaging. This talk will give a general overview of the software development during SINBAD project with focus on the final release in February 2008. The covered topics will include: 1) adopting Python for object-oriented programming, 2) including parallelism into the algorithms used in seismic imaging/modeling, 3) in-house algorithms for seismic imaging, and 4) contributions to Madagascar (RSF). The talk will serve as an introduction to the other presentations in the session "SINBAD Software releases".},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/modzelewski2008SINBADdas/modzelewski2008SINBADdas.pdf},
	author = {Henryk Modzelewski}
}
@conference {vandenberg2008IAMesr,
	title = {Exact sparse reconstruction and neighbourly polytopes},
	booktitle = {IAM},
	year = {2008},
	keywords = {IAM, Presentation, private, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Private/Conferences/IAM/2008/vandenberg2008IAMesr/vandenberg2008IAMesr.pdf},
	author = {Ewout van den Berg}
}
@techreport {vandenberg08gsv,
	title = {Group sparsity via linear-time projection},
	number = {TR-2008-09},
	year = {2008},
	month = {06},
	institution = {UBC - Department of Computer Science},
	abstract = {We present an efficient spectral projected-gradient algorithm for optimization subject to a group one-norm constraint. Our approach is based on a novel linear-time algorithm for Euclidean projection onto the one- and group one-norm constraints. Numerical experiments on large data sets suggest that the proposed method is substantially more efficient and scalable than existing methods.},
	keywords = {Optimization, SLIM},
	url = {http://www.optimization-online.org/DB_FILE/2008/07/2056.pdf},
	author = {Ewout van den Berg and Mark Schmidt and Michael P. Friedlander and K. Murphy}
}
@techreport {lebed08TRhgg,
	title = {A hitchhiker{\textquoteright}s guide to the galaxy of transform-domain sparsification},
	number = {TR-EOAS-2008-4},
	year = {2008},
	institution = {UBC Earth and Ocean Sciences Department},
	abstract = {The ability to efficiently and sparsely represent seismic data is becoming an increasingly important problem in geophysics. Over the last decade many transforms such as wavelets, curvelets, contourlets, surfacelets, shearlets, and many other types of {\textquoteright}x-lets{\textquoteright} have been developed to try to resolve this issue. In this abstract we compare the properties of four of these commonly used transforms, namely the shift-invariant wavelets, complex wavelets, curvelets and surfacelets. We also briefly explore the performance of these transforms for the problem of recovering seismic wavefields from incomplete measurements.},
	keywords = {SLIM},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2008/lebed08TRhgg/lebed08TRhgg.pdf},
	author = {Evgeniy Lebed and Felix J. Herrmann}
}
@conference {lin2008SEGiso,
	title = {Interpolating solutions of the Helmholtz equation with compressed sensing},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {01},
	pages = {2122-2126},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We present an algorithm which allows us to model wavefields with frequency-domain methods using a much smaller number of frequencies than that typically required by the classical sampling theory in order to obtain an alias-free result. The foundation of the algorithm is the recent results on the compressed sensing, which state that data can be successfully recovered from an incomplete measurement if the data is sufficiently sparse. Results from numerical experiment show that only 30\% of the total frequency spectrum is need to capture the full wavefield information when working in the hard 2D synthetic Marmousi model.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3059307},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/lin08SEGiso/lin08SEGiso.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/lin08SEGiso/lin08SEGiso.pdf},
	author = {Tim T.Y. Lin and Evgeniy Lebed and Yogi A. Erlangga and Felix J. Herrmann}
}
@conference {herrmann2008SINBADitc,
	title = {Introduction to compressive (wavefield) computation},
	booktitle = {SINBAD 2008},
	year = {2008},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADitc/herrmann2008SINBADitc.pdf},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin and Cody R. Brown}
}
@conference {erlangga2008SEGaim,
	title = {An iterative multilevel method for computing wavefields in frequency-domain seismic inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {1957-1960},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We describe an iterative multilevel method for solving linear systems representing forward modeling and back propagation of wavefields in frequency-domain seismic inversions. The workhorse of the method is the so-called multilevel Krylov method, applied to a multigrid-preconditioned linear system, and is called multigrid-multilevel Krylov (MKMG) method. Numerical experiments are presented for 2D Marmousi synthetic model for a range of frequencies. The convergence of the method is fast, and depends only mildly on frequency. The method can be considered as the first viable alternative to LU factorization, which is practically prohibitive for 3D seismic inversions.},
	keywords = {SEG, SLIM},
	doi = {10.1190/1.3059279},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/erlangga08SEGaim/erlangga08SEGaim.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/erlangga08SEGaim/erlangga08SEGaim_pres.pdf},
	author = {Yogi A. Erlangga and Felix J. Herrmann}
}
@conference {eso2008SEGira,
	title = {Iterative reconstruction algorithm for non-linear operators},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {579-583},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Iterative soft thresholding of a models wavelet coefficients can be used to obtain models that are sparse with respect to a known basis function. We generate sparse models for non-linear forward operators by applying the soft thresholding operator to the model obtained through a Gauss-Newton iteration and apply the technique in a synthetic 2.5D DC resistivity crosswell tomographic example.},
	keywords = {SEG, SLIM},
	doi = {10.1190/1.3063719},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/eso08SEGira/eso08SEGira.pdf},
	author = {R. A. Eso and S. Napier and Felix J. Herrmann and D. W. Oldenburg}
}
@conference {maysami2008SEGlcf,
	title = {Lithological constraints from seismic waveforms: application to opal-A to opal-CT transition},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2011-2015},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we present a new method for seismic waveform characterization whose aim is threefold, namely (i) extraction of detailed information on the sharpness of transitions in the subsurface from seismic waveforms, (ii) reflector modeling, based on binary-mixture and percolation theory, and (iii) establishment of well-seismic ties, through parameterizations of our waveform and critical reflector model. We test this methodology on the opal-A (Amorphous) to opal-CT (Cristobalite/Tridymite) transition imaged in a migrated section of North Sea field data West of the Shetlands.},
	keywords = {SEG, SLIM},
	doi = {10.1190/1.3059400},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/maysami08SEGlcf/maysami08SEGlcf.pdf},
	author = {Mohammad Maysami and Felix J. Herrmann}
}
@conference {hennenfent2008SINBADnii2,
	title = {New insights into one-norm solvers from the Pareto curve},
	booktitle = {SINBAD},
	year = {2008},
	abstract = {Several geophysical ill-posed inverse problems are successfully solved by promoting sparsity using one-norm regularization. The practicality of this approach depends on the effectiveness of the one-norm solver used and on its robustness under limited number of iterations. We propose an approach to understand the behavior and evaluate the performance of one-norm solvers. The technique consists of tracking on a graph the data misfit versus the one norm of successive iterates. By comparing the solution paths to the Pareto curve, we are able to assess the performance of the solvers and the quality of the solutions. Such an assessment is particularly relevant given the renewed interest in one-norm regularization.},
	keywords = {SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/hennenfent2008SINBADnii2/hennenfent2008SINBADnii2.pdf},
	author = {Gilles Hennenfent}
}
@article {hennenfent2008GEOPnii,
	title = {New insights into one-norm solvers from the Pareto curve},
	journal = {Geophysics},
	volume = {73},
	number = {4},
	year = {2008},
	month = {07},
	pages = {A23-A26},
	abstract = {Geophysical inverse problems typically involve a trade off between data misfit and some prior. Pareto curves trace the optimal trade off between these two competing aims. These curves are commonly used in problems with two-norm priors where they are plotted on a log-log scale and are known as L-curves. For other priors, such as the sparsity-promoting one norm, Pareto curves remain relatively unexplored. We show how these curves lead to new insights in one-norm regularization. First, we confirm the theoretical properties of smoothness and convexity of these curves from a stylized and a geophysical example. Second, we exploit these crucial properties to approximate the Pareto curve for a large-scale problem. Third, we show how Pareto curves provide an objective criterion to gauge how different one-norm solvers advance towards the solution.},
	keywords = {Acquisition, Geophysics, Optimization, Pareto, Processing, SLIM},
	doi = {10.1190/1.2944169},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/hennenfent08GEOnii/hennenfent08GEOnii.pdf},
	author = {Gilles Hennenfent and Ewout van den Berg and Michael P. Friedlander and Felix J. Herrmann}
}
@mastersthesis {fenelon08msc,
	title = {Nonequispaced discrete curvelet transform for seismic data reconstruction},
	year = {2008},
	month = {08},
	type = {masters},
	abstract = {Physical constraints during seismic acquisitions lead to incomplete seismic datasets. Curvelet Reconstruction with Sparsity promoting Inversion (CRSI) is one of the most efficient interpolation method available to recover complete datasets from data with missing traces. The method uses in its definition the curvelet transform which is well suited to process seismic data. However, its main shortcoming is to not be able to provide an accurate result if the data are acquired at irregular positions. This come from the curvelet transform implementation which cannot handle this type of data. In this thesis the implementation of the curvelet transform is modified to offer the possibility to CRSI to give better representation of seismic data for high quality seismic imaging.},
	keywords = {BSc, SLIM},
	pdf = {http://slim.gatech.edu/Publications/Public/Theses/2008/fenelon08msc.pdf},
	author = {Lloyd Fenelon}
}
@article {herrmann2008GJInps,
	title = {Non-parametric seismic data recovery with curvelet frames},
	journal = {Geophysical Journal International},
	volume = {173},
	year = {2008},
	month = {04},
	pages = {233-248},
	abstract = {Seismic data recovery from data with missing traces on otherwise regular acquisition grids forms a crucial step in the seismic processing flow. For instance, unsuccessful recovery leads to imaging artifacts and to erroneous predictions for the multiples, adversely affecting the performance of multiple elimination. A non-parametric transform-based recovery method is presented that exploits the compression of seismic data volumes by recently developed curvelet frames. The elements of this transform are multidimensional and directional and locally resemble wavefronts present in the data, which leads to a compressible representation for seismic data. This compression enables us to formulate a new curvelet-based seismic data recovery algorithm through sparsity-promoting inversion. The concept of sparsity-promoting inversion is in itself not new to geophysics. However, the recent insights from the field of {\textquoteleft}compressed sensing{\textquoteright} are new since they clearly identify the three main ingredients that go into a successful formulation of a recovery problem, namely a sparsifying transform, a sampling strategy that subdues coherent aliases and a sparsity-promoting program that recovers the largest entries of the curvelet-domain vector while explaining the measurements. These concepts are illustrated with a stylized experiment that stresses the importance of the degree of compression by the sparsifying transform. With these findings, a curvelet-based recovery algorithms is developed, which recovers seismic wavefields from seismic data volumes with large percentages of traces missing. During this construction, we benefit from the main three ingredients of compressive sampling, namely the curvelet compression of seismic data, the existence of a favorable sampling scheme and the formulation of a large-scale sparsity-promoting solver based on a cooling method. The recovery performs well on synthetic as well as real data and performs better by virtue of the sparsifying property of curvelets. Our results are applicable to other areas such as global seismology.},
	keywords = {Acquisition, curvelet transform, reconstruction, SLIM},
	doi = {10.1111/j.1365-246X.2007.03698.x},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalJournalInternational/2008/herrmann2008GJInps.pdf},
	author = {Felix J. Herrmann and Gilles Hennenfent}
}
@techreport {hennenfent08TRori,
	title = {One-norm regularized inversion: learning from the Pareto curve},
	number = {TR-EOAS-2008-5},
	year = {2008},
	institution = {UBC Earth and Ocean Sciences Department},
	abstract = {Geophysical inverse problems typically involve a trade off between data misfit and some prior. Pareto curves trace the optimal trade off between these two competing aims. These curves are commonly used in problems with two-norm priors where they are plotted on a log-log scale and are known as L-curves. For other priors, such as the sparsity-promoting one norm, Pareto curves remain relatively unexplored. First, we show how these curves provide an objective criterion to gauge how robust one-norm solvers are when they are limited by a maximum number of matrix-vector products that they can perform. Second, we use Pareto curves and their properties to define and compute one-norm compressibilities. We argue this notion is key to understand one-norm regularized inversion. Third, we illustrate the correlation between the one-norm compressibility and the performance of Fourier and curvelet reconstructions with sparsity promoting inversion.},
	keywords = {SLIM},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2008/hennenfent08TRori/hennenfent08TRori.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {herrmann2008SINBADpsm,
	title = {Phase-space matched filtering and migration preconditioning},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {During this talk, I will report on new phase-space regularization functionals defined in terms of splines. This spline representation reduces the dimensionality of estimating our phase-space matched filter. We will discuss how this filter can be used in migration preconditioning. This is joint work with Christiaan Stolk.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADpsm/herrmann2008SINBADpsm.pdf},
	author = {Felix J. Herrmann}
}
@conference {wang2008SINBADrri,
	title = {Recent results in curvelet-based primary-multiple separation},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We present a nonlinear curvelet-based sparsity-promoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploiting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/wang2008SINBADrri/wang2008SINBADrri.pdf},
	author = {Deli Wang and Rayan Saab and Ozgur Yilmaz and Felix J. Herrmann}
}
@manual {hennenfent08MNrap,
	title = {Repro: a Python package for automating reproducible research in scientific computing},
	year = {2008},
	month = {08},
	abstract = {Repro is a Python package for automating reproducible research in scientific computing. Repro works in combination with SCons, a next-generation build tool. The package is freely available over the Internet. Downloading and installation instructions are provided in this gui de. The repro package is documented in various ways (many comments in source code, this guide{\textendash}-written using repro itself!{\textendash}-and a reference guide). In this user{\textquoteright}s guide, we present a few pedagogical examples that uses Matlab, Python, Seismic Unix (SU), and Madagascar. We also include demo pa pers. These papers are written in LaTeX and compiled using repro. The figures they contain are automatically generated from the source codes prov ided. In that sense, the demo papers are a model of self-contained documents that are fully reproducible. The repro package is largely inspired by some parts of Madagascar, a geophysical software package for reproducible research. However, the repro package is intended for a broad audience co ming from a wide spectrum of interest areas.},
	keywords = {SLIM},
	url = {http://repro.sourceforge.net/Site/Home.html},
	author = {Gilles Hennenfent and Sean Ross-Ross}
}
@conference {moghaddam2008SINBADrtm,
	title = {Reverse-time migration amplitude recovery with curvelets},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We recover the amplitude of a seismic image by approximating the normal (demigration-migration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigenvectors. Subsequently, we propose an approximate nonlinear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time {\textquoteright}wave-equation{\textquoteright} migration code simulating the acoustic wave equation.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/moghaddam2008SINBADrtm/moghaddam2008SINBADrtm.pdf},
	author = {Peyman P. Moghaddam}
}
@mastersthesis {hennenfent08phd,
	title = {Sampling and reconstruction of seismic wavefields in the curvelet domain},
	year = {2008},
	note = {(PhD)},
	month = {05},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {Wavefield reconstruction is a crucial step in the seismic processing flow. For instance, unsuccessful interpolation leads to erroneous multiple predictions that adversely affect the performance of multiple elimination, and to imaging artifacts. We present a new non-parametric transform-based reconstruction method that exploits the compression of seismic data by the recently developed curvelet transform. The elements of this transform, called curvelets, are multi-dimensional, multi-scale, and multi-directional. They locally resemble wavefronts present in the data, which leads to a compressible representation for seismic data. This compression enables us to formulate a new curvelet-based seismic data recovery algorithm through sparsity-promoting inversion (CRSI). The concept of sparsity-promoting inversion is in itself not new to geophysics. However, the recent insights from the field of {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright} are new since they clearly identify the three main ingredients that go into a successful formulation of a reconstruction problem, namely a sparsifying transform, a sub-Nyquist sampling strategy that subdues coherent aliases in the sparsifying domain, and a data-consistent sparsity-promoting program. After a brief overview of the curvelet transform and our seismic-oriented extension to the fast discrete curvelet transform, we detail the CRSI formulation and illustrate its performance on synthetic and real datasets. Then, we introduce a sub-Nyquist sampling scheme, termed jittered undersampling, and show that, for the same amount of data acquired, jittered data are best interpolated using CRSI compared to regular or random undersampled data. We also discuss the large-scale one-norm solver involved in CRSI. Finally, we extend CRSI formulation to other geophysical applications and present results on multiple removal and migration-amplitude recovery.},
	keywords = {curvelet transform, reconstruction, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2008/hennenfent08phd.pdf},
	author = {Gilles Hennenfent}
}
@conference {johnson2008SINBADsdi,
	title = {Seismic data interpolation with symmetry},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {Due to the physics of reciprocity seismic data sets are symmetric in the source and receiver coordinates. Often seismic data sets are incomplete and the missing data must be interpolated. Typically, missing traces do not occur symmetrically. The purpose of this project is to extend the current formulation for solving the seismic interpolation problems in such a way that they enforce reciprocity. The method decomposes the seismic data volume into symmetric and antisymmetric parts. This decomposition leads to an augmented system of equations for the L1-solver that promotes sparsity in the curvelet domain. Interpolation is carried out on the entire system during which the asymmetric component of the volume is forced to zero, while the symmetric part of the data volume is matched to the measured data.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/johnson2008SINBADsdi/johnson2008SINBADsdi.pdf},
	author = {James Johnson and Gilles Hennenfent}
}
@conference {ross2008SINBADsit,
	title = {Seismic inversion through operator overloading},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {Geophysical processing is dominated by many different out of core memory software environments (OOCE). Such environments include Madagascar and SU and are designed to handle data that can not be operated on in memory. Each base operation is created as a main program that reads data from disk and writes the result to disk. The main programs can also be chained together on stdin/out pipes using a shell only writing data to disk at the end. To be efficient, the algorithm using an OOCE must chain together the longest pipe to avoid disk I/O, as a result it is very difficult to use iterative techniques. The algorithms are written in shell scripts can be difficult to read and understand. SLIMpy is a software library that contains definitions of coordinate free vectors and linear operators. It allows the user to design and run algorithms with any out of core package, in a Matlab style interface while maintaining optimal efficiency and speed. SLIMpy looks at each main program of each OOCE as a Matrix vector operation or vector reduction/transformation operation. It uses operator overloading to generate an abstract syntax tree (AST) which can be optimized in many ways before executing its commands. The AST also provides a pathway for embarrassingly parallel applications by splitting the tree over different nodes and processors. SLIMpy provides an interface to these OOCE that allows for optimal construction of commands and allows for iterative techniques. It smoothes the transition from other languages such as Matlab and allows the algorithm designer to write readable and reusable code. SLIMpy also adds to OOCE by allowing for easy parallelization.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/ross2008SINBADsit/ross2008SINBADsit.pdf},
	author = {Sean Ross-Ross}
}
@conference {herrmann2008SEGgbu,
	title = {Seismic noise: the good, the bad, \& the ugly},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2008},
	keywords = {Presentation, SEG, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGgbu/herrmann08SEGgbu.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2008SEGswi,
	title = {Seismic wavefield inversion with curvelet-domain sparsity promotion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2497-2501},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Inverting seismic wavefields lies at the heart of seismic data processing and imaging{\textendash}- whether one is applying {\textquoteleft}{\textquoteleft}a poor man{\textquoteright}s inverse{\textquoteright}{\textquoteright} by correlating wavefields during imaging or whether one inverts wavefields as part of a focal transform interferrometric deconvolution or as part of computing the {\textquoteright}data verse{\textquoteright}. The success of these wavefield inversions depends on the stability of the inverse with respect to data imperfections such as finite aperture, bandwidth limitation, and missing data. In this paper, we show how curvelet domain sparsity promotion can be used as a suitable prior to invert seismic wavefields. Examples include, seismic data regularization with the focused curvelet-based recovery by sparsity-promoting inversion (fCRSI), which involves the inversion of the primary-wavefield operator, the prediction of multiples by inverting the adjoint of the primary operator, and finally the inversion of the data itself {\textendash}- the so-called {\textquoteright}data inverse{\textquoteright}. In all cases, curvelet-domain sparsity leads to a stable inversion.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3063862},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGswi/herrmann08SEGswi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGswi/herrmann08SEGswi_pres.pdf},
	author = {Felix J. Herrmann and Deli Wang}
}
@article {hennenfent2008GEOPsdw,
	title = {Simply denoise: wavefield reconstruction via jittered undersampling},
	journal = {Geophysics},
	volume = {73},
	number = {3},
	year = {2008},
	month = {05},
	pages = {V19-V28},
	publisher = {SEG},
	abstract = {In this paper, we present a new discrete undersampling scheme designed to favor wavefield reconstruction by sparsity-promoting inversion with transform elements that are localized in the Fourier domain. Our work is motivated by empirical observations in the seismic community, corroborated by recent results from compressive sampling, which indicate favorable (wavefield) reconstructions from random as opposed to regular undersampling. As predicted by theory, random undersampling renders coherent aliases into harmless incoherent random noise, effectively turning the interpolation problem into a much simpler denoising problem. A practical requirement of wavefield reconstruction with localized sparsifying transforms is the control on the maximum gap size. Unfortunately, random undersampling does not provide such a control and the main purpose of this paper is to introduce a sampling scheme, coined jittered undersampling, that shares the benefits of random sampling, while offering control on the maximum gap size. Our contribution of jittered sub-Nyquist sampling proves to be key in the formulation of a versatile wavefield sparsity-promoting recovery scheme that follows the principles of compressive sampling. After studying the behavior of the jittered undersampling scheme in the Fourier domain, its performance is studied for curvelet recovery by sparsity-promoting inversion (CRSI). Our findings on synthetic and real seismic data indicate an improvement of several decibels over recovery from regularly-undersampled data for the same amount of data collected.},
	keywords = {Acquisition, Compressive Sensing, Geophysics, Optimization, Processing, sampling, SLIM},
	doi = {10.1190/1.2841038},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/hennenfent08GEOsdw/hennenfent08GEOsdw.pdf},
	html_version = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/hennenfent08GEOsdw/paper_html/paper.html},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {hennenfent2008SINBADsdw2,
	title = {Simply denoise: wavefield reconstruction via jittered undersampling},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We present a new discrete undersampling scheme designed to favor wavefield reconstruction by sparsity-promoting inversion with transform elements that are localized in the Fourier domain. Our work is motivated by empirical observations in the seismic community, corroborated by recent results from compressive sampling, which indicate favorable (wavefield) reconstructions from random as opposed to regular undersampling. As predicted by theory, random undersampling renders coherent aliases into harmless incoherent random noise, effectively turning the interpolation problem into a much simpler denoising problem. A practical requirement of wavefield reconstruction with localized sparsifying transforms is the control on the maximum gap size. Unfortunately, random undersampling does not provide such a control and the main purpose of this paper is to introduce a sampling scheme, coined jittered undersampling, that shares the benefits of random sampling, while offering control on the maximum gap size. Our contribution of jittered sub-Nyquist sampling proofs to be key in the formulation of a versatile wavefield sparsity-promoting recovery scheme that follows the principles of compressive sampling. After studying the behavior of the jittered-undersampling scheme in the Fourier domain, its performance is studied for curvelet recovery by sparsity-promoting inversion (CRSI). Our findings on synthetic and real seismic data indicate an improvement of several decibels over recovery from regularly-undersampled data for the same amount of data collected.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/hennenfent2008SINBADsdw2/hennenfent2008SINBADsdw2.pdf},
	author = {Gilles Hennenfent}
}
@conference {herrmann2008SINBADs2c,
	title = {SINBAD 2008 Consortium meeting},
	booktitle = {SINBAD 2008},
	year = {2008},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADs2c/herrmann2008SINBADs2c.pdf},
	author = {Felix J. Herrmann}
}
@conference {vandenberg2008SINBADsat,
	title = {Sparco: A testing framework for sparse reconstruction},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {Sparco is a framework for testing and benchmarking algorithms for sparse reconstruction. It includes a large collection of sparse reconstruction problems drawn from the imaging, compressed sensing, and geophysics literature. Sparco is also a framework for implementing new test problems and can be used as a tool for reproducible research. We describe the software environment, and demonstrate its usefulness for testing and comparing solvers for sparse reconstruction.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/vandenberg2008SINBADsat/vandenberg2008SINBADsat.pdf},
	author = {Ewout van den Berg}
}
@article {herrmann2008ACHAsac,
	title = {Sparsity- and continuity-promoting seismic image recovery with curvelet frames},
	journal = {Applied and Computational Harmonic Analysis},
	volume = {24},
	number = {2},
	year = {2008},
	month = {03},
	pages = {150-173},
	abstract = {A nonlinear singularity-preserving solution to seismic image recovery with sparseness and continuity constraints is proposed. We observe that curvelets, as a directional frame expansion, lead to sparsity of seismic images and exhibit invariance under the normal operator of the linearized imaging problem. Based on this observation we derive a method for stable recovery of the migration amplitudes from noisy data. The method corrects the amplitudes during a post-processing step after migration, such that the main additional cost is one ap- plication of the normal operator, i.e. a modeling followed by a migration. Asymptotically this normal operator corresponds to a pseudodifferential operator, for which a convenient diagonal approximation in the curvelet domain is derived, including a bound for its error and a method for the estimation of the diagonal from a compound operator consisting of discrete implementations for the scattering operator and its adjoint the migration operator. The solution is formulated as a nonlinear optimization problem where sparsity in the curvelet domain as well as continuity along the imaged reflectors are jointly promoted. To enhance sparsity, the $ell_1$-norm on the curvelet coefficients is minimized, while continuity is promoted by minimizing an anisotropic diffusion norm on the image. The performance of the recovery scheme is evaluated with a time-reversed {\textquoteright}wave-equation{\textquoteright} migration code on synthetic datasets, including the complex SEG/EAGE AA salt model.},
	keywords = {curvelet transform, Imaging, Processing, SLIM},
	doi = {10.1016/j.acha.2007.06.007},
	url = {https://slim.gatech.edu/Publications/Public/Journals/ACHA/2008/herrmann2008ACHAsac/herrmann2008ACHAsac.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam and Christiaan C. Stolk}
}
@conference {yilmaz2008SINBADsse,
	title = {Stable sparse expansions via non-convex optimization},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We present theoretical results pertaining to the ability of p-(quasi)norm minimization to recover sparse and compressible signals from incomplete and noisy measurements. In particular, we extend the results of Candes, Romberg and Tao for 1-norm to the p $\ll$ 1 case. Our results indicate that depending on the restricted isometry constants and the noise level, p-norm minimization with certain values of p $\ll$ 1 provides better theoretical guarantees in terms of stability and robustness compared to 1-norm minimization. This is especially true when the restricted isometry constants are relatively large, or equivalently, when the data is significantly undersampled.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/yilmaz2008SINBADsse/yilmaz2008SINBADsse.pdf},
	author = {Ozgur Yilmaz}
}
@conference {yan2008SINBADwru,
	title = {Wavefield reconstruction using simultaneous denoising interpolation vs. denoising after interpolation},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {This report represents and compares two methods of wavefield reconstruction from noisy seismic data with missing traces. The two methods are (i) First interpolate incomplete noisy data to get complete noisy data and then denoise, and (ii) Interpolate and denoise the incomplete noisy data simultaneously. A sample test of synthetic data will be presented. The results of tests show that denoising after interpolation is better than simultaneous denoising and interpolation if the parameter of the denoising problem is chosen appropriately.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/yan2008SINBADwru/yan2008SINBADwru.pdf},
	author = {Jiupeng Yan}
}
@article {lin2007GEOPcwe,
	title = {Compressed wavefield extrapolation},
	journal = {Geophysics},
	volume = {72},
	number = {5},
	year = {2007},
	month = {08},
	pages = {SM77-SM93},
	publisher = {SEG},
	abstract = {An explicit algorithm for the extrapolation of one-way wavefields is proposed that combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3D. By using ideas from compressed sensing, we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. Compressed sensing entails a new paradigm for signal recovery that provides conditions under which signals can be recovered from incomplete samplings by nonlinear recovery methods that promote sparsity of the to-be-recovered signal. According to this theory, signals can be successfully recovered when the measurement basis is incoherent with the representa-tion in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can be successfully extrapolated in the modal domain, despite evanescent wave modes. The degree to which the wavefield can be recovered depends on the number of missing (evanescent) wavemodes and on the complexity of the wavefield. A proof of principle for the compressed sensing method is given for inverse wavefield extrapolation in 2D, together with a pathway to 3D during which the multiscale and multiangular properties of curvelets, in relation to the Helmholz operator, are exploited. The results show that our method is stable, has reduced dip limitations, and handles evanescent waves in inverse extrapolation. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Modelling, SLIM, wave propagation},
	doi = {10.1190/1.2750716},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2007/lin07cwe/lin07cwe.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {herrmann2007SINBADcwe,
	title = {Compressed wavefield extrapolation},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {An explicit algorithm for the extrapolation of one-way wavefields is proposed which combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3-D. By using ideas from {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright}, we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. According to compressed sensing theory, signals can successfully be recovered from an incomplete set of measurements when the measurement basis is incoherent with the representation in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can successfully be extrapolated in the modal domain via a computationally cheaper operation. A proof of principle for the {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright} method is given for wavefield extrapolation in 2-D. The results show that our method is stable and produces identical results compared to the direct application of the full extrapolation operator. This is joint work with Tim Lin.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007SINBADcwe/herrmann2007SINBADcwe.pdf},
	author = {Felix J. Herrmann}
}
@conference {lin2007SEGcwe1,
	title = {Compressed wavefield extrapolation with curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {1997-2001},
	abstract = {An explicit algorithm for the extrapolation of one-way wavefields is proposed which combines recent developments in information theory and theoretical signal processing with the physics of wave propagation. Because of excessive memory requirements, explicit formulations for wave propagation have proven to be a challenge in 3-D. By using ideas from {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright}, we are able to formulate the (inverse) wavefield extrapolation problem on small subsets of the data volume, thereby reducing the size of the operators. According to compressed sensing theory, signals can successfully be recovered from an imcomplete set of measurements when the measurement basis is incoherent with the representation in which the wavefield is sparse. In this new approach, the eigenfunctions of the Helmholtz operator are recognized as a basis that is incoherent with curvelets that are known to compress seismic wavefields. By casting the wavefield extrapolation problem in this framework, wavefields can successfully be extrapolated in the modal domain via a computationally cheaper operation. A proof of principle for the {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright} method is given for wavefield extrapolation in 2-D. The results show that our method is stable and produces identical results compared to the direct application of the full extrapolation operator. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792882},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/lin07SEGcwe1/lin07SEGcwe1.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/lin07SEGcwe1/lin07SEGcwe1_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {herrmann2007PIMScsm,
	title = {Compressive sampling meets seismic imaging},
	booktitle = {PIMS},
	year = {2007},
	keywords = {PIMS, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/PIMS/2007/herrmann07PIMScsm/herrmann07PIMScsm_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007AMScsi,
	title = {Compressive seismic imaging},
	booktitle = {AMS Von Neumann},
	year = {2007},
	abstract = {Seismic imaging involves the solution of an inverse-scattering problem during which the energy of (extremely) large data volumes is collapsed onto the Earth{\textquoteright}s reflectors. We show how the ideas from "compressive sampling" can alleviate this task by exploiting the curvelet transform{\textquoteright}s "wavefront-set detection" capability and "invariance" property under wave propagation. First, a wavelet-vaguellete technique is reviewed, where seismic amplitudes are recovered from complete data by diagonalizing the Gramm matrix of the linearized scattering problem. Next, we show how the recovery of seismic wavefields from incomplete data can be cast into a compressive sampling problem, followed by a proposal to compress wavefield extrapolation operators via compressive sampling in the modal domain. During the latter approach, we explicitly exploit the mutual incoherence between the eigenfunctions of the Helmholtz operator and the curvelet frame elements that compress the extrapolated wavefield. This is joint work with Gilles Hennenfent, Peyman Moghaddam, Tim Lin, Chris Stolk and Deli Wang.},
	keywords = {AMS Von Neumann, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/vonNeuman/2007/herrmann07AMScsi/herrmann07AMScsi_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {yarham2007EAGEcai,
	title = {Curvelet applications in surface wave removal},
	booktitle = {EAGE Workshop on Curvelets, contourlets, seislets, {\textellipsis} in seismic data processing - where are we and where are we going?},
	year = {2007},
	month = {06},
	abstract = {Ground roll removal of seismic signals can be a challenging prospect. Dealing with undersampleing causing aliased waves amplitudes orders of magnitude higher than reflector signals and low frequency loss of information due to band ...},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/yarham07EAGEcai/yarham07EAGEcai.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7590},
	author = {Carson Yarham and Gilles Hennenfent and Felix J. Herrmann}
}
@conference {hennenfent2007EAGEcrw,
	title = {Curvelet reconstruction with sparsity-promoting inversion: successes and challenges},
	booktitle = {EAGE Workshop on Curvelets, contourlets, seislets, {\textellipsis} in seismic data processing - where are we and where are we going?},
	year = {2007},
	month = {06},
	abstract = {In this overview of the recent Curvelet Reconstruction with Sparsity-promoting Inversion (CRSI) method, we present our latest 2-D and 3-D interpolation results on both synthetic and real datasets. We compare these results to interpolated data using other existing methods. Finally, we discuss the challenges related to sparsity-promoting solvers for the large-scale problems the industry faces.},
	keywords = {EAGE, Presentation, SLIM, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEcrw/hennenfent07EAGEcrw.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEcrw/hennenfent07EAGEcrw_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7553},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {saab2007SEGcbp,
	title = {Curvelet-based primary-multiple separation from a Bayesian perspective},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2510-2514},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this abstract, we present a novel primary-multiple separation scheme which makes use of the sparsity of both primaries and multiples in a transform domain, such as the curvelet transform, to provide estimates of each. The proposed algorithm utilizes seismic data as well as the output of a preliminary step that provides (possibly) erroneous predictions of the multiples. The algorithm separates the signal components, i.e., the primaries and multiples, by solving an optimization problem that assumes noisy input data and can be derived from a Bayesian perspective. More precisely, the optimization problem can be arrived at via an assumption of a weighted Laplacian distribution for the primary and multiple coefficients in the transform domain and of white Gaussian noise contaminating both the seismic data and the preliminary prediction of the multiples, which both serve as input to the algorithm. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792988},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/saab07SEGcbp/saab07SEGcbp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/saab07SEGcbp/saab07SEGcbp_pres.pdf},
	author = {Rayan Saab and Deli Wang and Ozgur Yilmaz and Felix J. Herrmann}
}
@article {friedlander2007TASdtd,
	title = {Discussion: the Dantzig selector: statistical estimation when p is much larger than n},
	journal = {The Annals of Statistics},
	volume = {35},
	number = {6},
	year = {2007},
	month = {03},
	pages = {2385-2391},
	keywords = {dantzig, SLIM, statistics},
	doi = {10.1214/009053607000000479},
	author = {Michael P. Friedlander and M. A. Saunders}
}
@article {friedlander2007SJOero,
	title = {Exact regularization of convex programs},
	journal = {SIAM Journal on Optimization},
	volume = {18},
	number = {4},
	year = {2007},
	month = {05},
	pages = {1326-1350},
	abstract = {The regularization of a convex program is exact if all solutions of the regularized problem are also solutions of the original problem for all values of the regularization parameter below some positive threshold. For a general convex program, we show that the regularization is exact if and only if a certain selection problem has a Lagrange multiplier. Moreover, the regularization parameter threshold is inversely related to the Lagrange multiplier. We use this result to generalize an exact regularization result of Ferris and Mangasarian [Appl. Math. Optim., 23(1991), pp. 266{\textendash}273] involving a linearized selection problem. We also use it to derive necessary and sufficient conditions for exact penalization, similar to those obtained by Bertsekas [Math. Programming, 9(1975), pp. 87{\textendash}99] and by Bertsekas, Nedi , Ozdaglar [Convex Analysis and Optimization, Athena Scientific, Belmont, MA, 2003]. When the regularization is not exact, we derive error bounds on the distance from the regularized solution to the original solution set. We also show that existence of a {\textquoteleft}{\textquoteleft}weak sharp minimum{\textquoteright}{\textquoteright} is in some sense close to being necessary for exact regularization. We illustrate the main result with numerical experiments on the l1 regularization of benchmark (degenerate) linear programs and semidefinite/second-order cone programs. The experiments demonstrate the usefulness of l1 regularization in finding sparse solutions.},
	keywords = {Optimization, SLIM},
	doi = {10.1137/060675320},
	author = {Michael P. Friedlander and P. Tseng}
}
@conference {herrmann2007SINBADfrw,
	title = {Focused recovery with the curvelet transform},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {Incomplete data represents a major challenge for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles this challenge in a two-step approach. During the first step, the recently developed curvelet-based recovery by sparsity-promoting inversion (CRSI) is applied to the data, followed by a prediction of the primaries. During the second high-resolution step, the estimated primaries are used to improve the frequency content of the recovered data by combining the focal transform, defined in terms of the estimated primaries, with the curvelet transform. This focused curvelet transform leads to an improved recovery, which can subsequently be used as input for a second stage of multiple prediction and primary-multiple separation. This is joint work with Deli Wang and Gilles Hennenfent.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/sites/data/Papers/Herrmann2007SINBADfoc.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007SLIMfsd,
	title = {From seismic data to the composition of rocks: an interdisciplinary and multiscale approach to exploration seismology},
	booktitle = {Berkhout{\textquoteright}s valedictory address: the conceptual approach of understanding},
	year = {2007},
	abstract = {In this essay, a nonlinear and multidisciplinary approach is presented that takes seismic data to the composition of rocks. The presented work has deep roots in the {\textquoteleft}gedachtengoed{\textquoteright} (philosophy) of Delphi spearheaded by Guus Berkhout. Central themes are multiscale, object-orientation and a multidisciplinary approach.},
	keywords = {SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Misc/herrmann07SLIMfsd/herrmann07SLIMfsd.pdf},
	author = {Felix J. Herrmann}
}
@conference {vandenberg2007SINBADipo1,
	title = {In pursuit of a root},
	booktitle = {2007 Von Neumann Symposium},
	year = {2007},
	keywords = {minimization, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/vandenberg2007SINBADipo1/vandenberg2007SINBADipo1.pdf},
	author = {Ewout van den Berg and Michael P. Friedlander}
}
@conference {hennenfent2007EAGEisf,
	title = {Irregular sampling: from aliasing to noise},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Seismic data is often irregularly and/or sparsely sampled along spatial coordinates. We show that these acquisition geometries are not necessarily a source of adversity in order to accurately reconstruct adequately-sampled data. We use two examples to illustrate that it may actually be better than equivalent regularly subsampled data. This comment was already made in earlier works by other authors. We explain this behavior by two key observations. Firstly, a noise-free underdetermined problem can be seen as a noisy well-determined problem. Secondly, regularly subsampling creates strong coherent acquisition noise (aliasing) difficult to remove unlike the noise created by irregularly subsampling that is typically weaker and Gaussian-like.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEisf/hennenfent07EAGEisf.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEisf/hennenfent07EAGEisf_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/hennenfent07EAGEisf/hennenfent07EAGEisf_WS.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {hennenfent2007SINBADjdn,
	title = {Just denoise: nonlinear recovery from randomly sampled data},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {In this talk, we turn the interpolation problem of coarsely-sampled data into a denoising problem. From this point of view, we illustrate the benefit of random sampling at sub-Nyquist rate over regular sampling at the same rate. We show that, using nonlinear sparsity-promoting optimization, coarse random sampling may actually lead to significantly better wavefield reconstruction than equivalent regularly sampled data.},
	keywords = {Presentation, SINBAD, SLIM},
	author = {Gilles Hennenfent}
}
@conference {herrmann2007EAGEjda,
	title = {Just diagonalize: a curvelet-based approach to seismic amplitude recovery},
	booktitle = {EAGE Workshop on Curvelets, contourlets, seislets, {\textellipsis} in seismic data processing - where are we and where are we going?},
	year = {2007},
	month = {06},
	abstract = {In his presentation we present a nonlinear curvelet-based sparsity-promoting formulation for the recovery of seismic amplitudes. We show that the curvelet{\textquoteright}s wavefront detection capability and invariance under wave propagation lead to a formulation of this recovery problem that is stable under noise and missing data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007EAGEjda/herrmann2007EAGEjda_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007EAGEjda/herrmann2007EAGEjda_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7555},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {moghaddam2007CSEGmar,
	title = {Migration amplitude recovery using curvelets},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2007},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {In this paper, we recover the amplitude of a seismic image by approximating the normal operator and subsequently inverting it. Normal operator (migration followed by modeling) is an example of pseudo-differential. curvelets are proven to be invariant under the action of pseudo-differential operators under certain conditions. Subsequently, curvelets are forming as eigen-vectors for such an operator. We propose a seismic amplitude recovery method that employs an eigen-value decomposition for normal operator using curvelets as eigen-vectors and to be estimated eigenvalues. A post-stack reverse-time, wave-equation migration is used for evaluation of the proposed method.},
	keywords = {SLIM},
	url = {http://cseg.ca/assets/files/resources/abstracts/2007/168S0131.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann and Christiaan C. Stolk}
}
@conference {herrmann2007SEGmpf,
	title = {Multiple prediction from incomplete data with the focused curvelet transform},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2505-2600},
	abstract = {Incomplete data represents a major challenge for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles this challenge in a two-step approach. During the first step, the recenly developed curvelet-based recovery by sparsity-promoting inversion (CRSI) is applied to the data, followed by a prediction of the primaries. During the second high-resolution step, the estimated primaries are used to improve the frequency content of the recovered data by combining the focal transform, defined in terms of the estimated primaries, with the curvelet transform. This focused curvelet transform leads to an improved recovery, which can subsequently be used as input for a second stage of multiple prediction and primary-multiple separation.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792987},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGmpf/herrmann07SEGmpf.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGmpf/herrmann07SEGmpf_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {verschuur2007SEGmmp,
	title = {Multiterm multiple prediction using separated reflections and diffractions combined with curvelet-based subtraction},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2535-2539},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The surface-related multiple elimination (SRME) method has proven to be successful on a large number of data cases. Most of the applications are still 2D, as the full 3D implementation is still expensive and under development. However, the earth is a 3D medium, such that 3D effects are difficult to avoid. Most of the 3D effects come from diffractive structures, whereas the specular reflections normally have less of a 3D behavior. By separating the seismic data in a specular reflecting and a diffractive part, multiple prediction can be carried out with these different subsets of the input data, resulting in several categories of predicted multiples. Because each category of predicted multiples can be subtracted from the input data with different adaptation filters, a more flexible SRME procedure is obtained. Based on some initial results from a Gulf of Mexico dataset, the potential of this approach is investigated. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792993},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/verschuur07SEGmmp/verschuur07SEGmmp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/verschuur07SEGmmp/verschuur07SEGmmp_pres.pdf},
	author = {D. J. Verschuur and Deli Wang and Felix J. Herrmann}
}
@article {herrmann2007GJInlp,
	title = {Non-linear primary-multiple separation with directional curvelet frames},
	journal = {Geophysical Journal International},
	volume = {170},
	number = {2},
	year = {2007},
	month = {08},
	pages = {781-799},
	abstract = {Predictive multiple suppression methods consist of two main steps: a prediction step, during which multiples are predicted from seismic data, and a primary-multiple separation step, during which the predicted multiples are {\textquoteright}matched{\textquoteright} with the true multiples in the data and subsequently removed. This second separation step, which we will call the estimation step, is crucial in practice: an incorrect separation will cause residual multiple energy in the result or may lead to a distortion of the primaries, or both. To reduce these adverse effects, a new transformed-domain method is proposed where primaries and multiples are separated rather than matched. This separation is carried out on the basis of differences in the multiscale and multidirectional characteristics of these two signal components. Our method uses the curvelet transform, which maps multidimensional data volumes into almost orthogonal localized multidimensional prototype waveforms that vary in directional and spatio-temporal content. Primaries-only and multiples-only signal components are recovered from the total data volume by a non-linear optimization scheme that is stable under noisy input data. During the optimization, the two signal components are separated by enhancing sparseness (through weighted l1-norms) in the transformed domain subject to fitting the observed data as the sum of the separated components to within a user-defined tolerance level. Whenever, during the optimization, the estimates for the primaries in the transformed domain correlate with the predictions for the multiples, the recovery of the coefficients for the estimated primaries will be suppressed while for regions where the correlation is small the method seeks the sparsest set of coefficients that represent the estimation for the primaries. Our algorithm does not seek a matched filter and as such it differs fundamentally from traditional adaptive subtraction methods. The method derives its stability from the sparseness obtained by a non-parametric (i.e. not depending on a parametrized physical model) multiscale and multidirectional overcomplete signal representation. This sparsity serves as prior information and allows for a Bayesian interpretation of our method during which the log-likelihood function is minimized while the two signal components are assumed to be given by a superposition of prototype waveforms, drawn independently from a probability function that is weighted by the predicted primaries and multiples. In this paper, the predictions are based on the data-driven surface-related multiple elimination method. Synthetic and field data examples show a clean separation leading to a considerable improvement in multiple suppression compared to the conventional method of adaptive matched filtering. This improved separation translates into an improved stack.},
	keywords = {Processing, signal separation, SLIM},
	doi = {10.1111/j.1365-246X.2007.03360.x},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalJournalInternational/2007/herrmann2007GJInlp/herrmann2007GJInlp.pdf},
	author = {Felix J. Herrmann and Urs Boeniger and D. J. Verschuur}
}
@conference {yarham2007EAGEnsw,
	title = {Nonlinear surface wave prediction and separation},
	booktitle = {EAGE 2007},
	year = {2007},
	abstract = {Removal of surface waves is an integral step in seismic processing. There are many standard techniques for removal of this type of coherent noise, such as f-k filtering, but these methods are not always effective. One of the common problems with removal of surface waves is that they tend to be aliased in the frequency domain. This can make removal difficult and affect the frequency content of the reflector signals, as this signals will not be completely separated. As seen in (Hennenfent, G. and F. Herrmann, 2006, Application of stable signal recovery to seismic interpolation) interpolation can be used effectively to resample the seismic record thus dealiasing the surface waves. This separates the signals in the frequency domain allowing for a more precise and complete removal. The use of this technique with curvelet based surface wave predictions and an iterative L1 separation scheme can be used to remove surface waves from shot records more completely that with standard techniques.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/yarham2007EAGEnsw/yarham2007EAGEnsw.pdf},
	author = {Carson Yarham}
}
@conference {sastry2007SINBADnor,
	title = {Norm-one recovery from irregular sampled data},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {Seismic traces are sampled irregularly and insufficiently due to practical and economical limitations. The use of such data in seismic imaging results in image artifacts and poor spatial resolution. Therefore, before being used, the measurements are to be interpolated onto a regular grid. One of the methods achieving this objective is based on the Fourier reconstruction, which deals with the under-determined system of equations. The recent pursuit techniques (namely, basis pursuit, matching pursuit etc) admit certain promising features such as faster and simpler implementation even in large scale settings. The presentation discusses the application of the pursuit algorithms to the Fourier-based interpolation problem for the signals that have sparse Fourier spectra. In particular, the objective of the presentation includes: 1). studying the performance of the algorithm if, and how far, the measurement coordinates can be shifted from uniform distribution on the continuous interval. 2). studying what could be the allowable misplacement in the measurement coordinates that does not alter the quality of the reconstruction process},
	keywords = {Presentation, SINBAD, SLIM},
	author = {Challa S. Sastry}
}
@conference {herrmann2007COIPpti,
	title = {Phase transitions in explorations seismology: statistical mechanics meets information theory},
	booktitle = {COIP},
	year = {2007},
	abstract = {In this paper, two different applications of phase transitions to exploration seismology will be discussed. The first application concerns a phase diagram ruling the recovery conditions for seismic data volumes from incomplete and noisy data while the second phase transition describes the behavior of bi-compositional mixtures as a function of the volume fraction. In both cases, the phase transitions are the result of randomness in large system of equations in combination with nonlinearity. The seismic recovery problem from incomplete data involves the inversion of a rectangular matrix. Recent results from the field of "compressive sensing" provide the conditions for a successful recovery of functions that are sparse in some basis (wavelet) or frame (curvelet) representation, by means of a sparsity ($\ell_1$-norm) promoting nonlinear program. The conditions for a successful recovery depend on a certain randomness of the matrix and on two parameters that express the matrix{\textquoteright} aspect ratio and the ratio of the number of nonzero entries in the coefficient vector for the sparse signal representation over the number of measurements. It appears that the ensemble average for the success rate for the recovery of the sparse transformed data vector by a nonlinear sparsity promoting program, can be described by a phase transition, demarcating the regions for the two ratios for which recovery of the sparse entries is likely to be successful or likely to fail. Consistent with other phase transition phenomena, the larger the system the sharper the transition. The randomness in this example is related to the construction of the matrix, which for the recovery of spike trains corresponds to the randomly restricted Fourier matrix. It is shown, that these ideas can be extended to the curvelet recovery by sparsity-promoting inversion (CRSI). The second application of phase transitions in exploration seismology concerns the upscaling problem. To counter the intrinsic smoothing of singularities by conventional equivalent medium upscaling theory, a percolation-based nonlinear switch model is proposed. In this model, the transport properties of bi-compositional mixture models for rocks undergo a sudden change in the macroscopic transport properties as soon as the volume fraction of the stronger material reaches a critical point. At this critical point, the stronger material forms a connected cluster, which leads to the creation of a cusp-like singularity in the elastic moduli, which in turn give rise to specular reflections. In this model, the reflectivity is no longer explicitly due to singularities in the rocks composition. Instead, singularities are created whenever the volume fraction exceeds the critical point. We will show that this concept can be used for a singularity-preserved lithological upscaling.},
	keywords = {Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/COIP/2007/herrmann07COIPpti/herrmann07COIPpti_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2007SEGrsn,
	title = {Random sampling: new insights into the reconstruction of coarsely sampled wavefields},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2007},
	pages = {2575-2579},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we turn the interpolation problem of coarsely-sampled data into a denoising problem. From this point of view, we illustrate the benefit of random sampling at sub-Nyquist rate over regular sampling at the same rate. We show that, using nonlinear sparsity-promoting optimization, coarse random sampling may actually lead to significantly better wavefield reconstruction than equivalent regularly sampled data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2793002},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/hennenfent07SEGrsn/hennenfent07SEGrsn.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/hennenfent07SEGrsn/hennenfent07SEGrsn_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {herrmann2007EAGErdi,
	title = {Recent developments in curvelet-based seismic processing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Combinations of parsimonious signal representations with nonlinear sparsity promoting programs hold the key to the next-generation of seismic data processing algorithms, since they allow for a formulation that is stable w.r.t. noise \& incomplete data do not require prior information on the velocity or locations and dips of the events},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGErdi/herrmann07EAGErdi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGErdi/herrmann07EAGErdi_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7548},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007SINBADrdi2,
	title = {Recent developments in primary-multiple separation},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {In this talk, we present a novel primary-multiple separation scheme which makes use of the sparsity of both primaries and multiples in a transform domain, such as the curvelet transform, to provide estimates of each. The proposed algorithm utilizes seismic data as well as the output of a preliminary step that provides (possibly) erroneous predictions of the multiples. The algorithm separates the signal components, i.e., the primaries and multiples, by solving an optimization problem that assumes noisy input data and can be derived from a Bayesian perspective. More precisely, the optimization problem can be arrived at via an assumption of a weighted Laplacian distribution for the primary and multiple coefficients in the transform domain and of white Gaussian noise contaminating both the seismic data and the preliminary prediction of the multiples, which both serve as input to the algorithm. Time permitted, we will also briefly discuss a propasal for adaptive curvelet-domain matched filtering. This is joint work with Deli Wang, Rayan Saaba, {\o}zgur Yilmaz and Eric Verschuur.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann07SINBADrdi2/herrmann07SINBADrdi2_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2007SINBADrii,
	title = {Recent insights in $\ell_1$ solvers},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {During this talk, an overview is given on our work on norm-one solvers as part of the DNOISE project. Gilles will explain the ins and outs of our iterative thresholding solver based on log cooling while Felix will present the work of Michael Friedlander "A Newton root-finding algorithms for large-scale basis pursuit denoise". Both approaches involve the solution of the basis pursuit problem that seeks a minimum one-norm solution of an underdetermined least-squares problem. Basis pursuit denoise (BPDN) fits the least-squares problem only approximately, and a single parameter determines a curve that traces the trade-off between the least-squares fit and the one-norm of the solution. In the work of Friedlander, it is shown show that the function that describes this curve is convex and continuously differentiable over all points of interest. They describe an efficient procedure for evaluating this function and its derivatives. As a result, they can compute arbitrary points on this curve. Their method is suitable for large-scale problems. Only matrix-vector operations are required. This is joint work with Ewout van der Berg and Michael P. Friedlander},
	keywords = {Presentation, SINBAD, SLIM},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {wang2007SEGrri,
	title = {Recent results in curvelet-based primary-multiple separation: application to real data},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2500-2504},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792986},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/wang07SEGrri/wang07SEGrri.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/wang07SEGrri/wang07SEGrri_pres.pdf},
	author = {Deli Wang and Rayan Saab and Ozgur Yilmaz and Felix J. Herrmann}
}
@conference {moghaddam2007SEGrsi,
	title = {Robust seismic-images amplitude recovery using curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2225-2229},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we recover the amplitude of a seismic image by approximating the normal (demigration-migration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigen-vectors. Subsequently, we propose an approximate non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time {\textquoteleft}wave-equation{\textquoteright} migration code simulating the acoustic wave equation on the SEG-AA salt model. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792928},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/moghaddam07SEGrsi/moghaddam07SEGrsi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/moghaddam07SEGrsi/moghaddam07SEGrsi_pres.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann and Christiaan C. Stolk}
}
@conference {moghaddam2007EAGEsar,
	title = {Seismic amplitude recovery with curvelets},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {A non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness and continuity constraints is proposed. The applied formalism explores curvelets as a directional frame that, by their sparsity on the image, and their invariance under the imaging operators, allows for a stable recovery of the amplitudes. Our method is based on the estimation of the normal operator in the form of an {\textquoteright}eigenvalue{\textquoteright} decompsoition with curvelets as the {\textquoteright}eigenvectors{\textquoteright}. Subsequently, we propose an inversion method that derives from estimation of the normal operator and is formulated as a convex optimization problem. Sparsity in the curvelet domain as well as continuity along the reflectors in the image domain are promoted as part of this optimization. Our method is tested with a reverse-time {\textquoteright}wave-equation{\textquoteright} migration code simulating the acoustic wave equation.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/moghaddam07EAGEsar/moghaddam07EAGEsar.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=6935},
	author = {Peyman P. Moghaddam and Felix J. Herrmann and Christiaan C. Stolk}
}
@conference {herrmann2007SEGsdp,
	title = {Seismic data processing with curvelets: a multiscale and nonlinear approach},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2220-2224},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic processing flow, consisting of the following steps: seismic data regularization and the restoration of migration amplitudes. We show that the curvelet{\textquoteright}s wavefront detection capability and invariance under the migration-demigration operator lead to a formulation that is stable under noise and missing data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792927},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGsdp/herrmann07SEGsdp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGsdp/herrmann07SEGsdp_pres.pdf},
	author = {Felix J. Herrmann and Deli Wang and Gilles Hennenfent and Peyman P. Moghaddam}
}
@conference {herrmann2007SINBADsia2,
	title = {Seismic image amplitude recovery},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {In this talk, we recover the amplitude of a seismic image by approximating the normal (demigration-migration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigen-vectors. Subsequently, we propose an approximate nonlinear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time {\textquoteright}wave-equation{\textquoteright} migration code simulating the acoustic wave equation on the SEG-AA salt model. This is joint work with Peyman Moghaddam and Chris Stolk (University of Twente)},
	keywords = {Presentation, SINBAD, SLIM},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007EAGEsia,
	title = {Seismic imaging and processing with curvelets},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three problems in seismic processing and imaging namely, seismic data regularization from data with large percentages of traces missing; seismic amplitude recovery for sub-salt images obtained by reverse-time migration and primary-multiple separation, given an inaccurate multiple prediction. We argue why these nonlinear formulations are beneficial.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsia/herrmann07EAGEsia.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsia/herrmann07EAGEsia_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7075},
	author = {Felix J. Herrmann and Gilles Hennenfent and Peyman P. Moghaddam}
}
@conference {herrmann2007AIPsit,
	title = {Seismic inversion through operator overloading},
	booktitle = {AIP},
	year = {2007},
	abstract = {Inverse problems in (exploration) seismology are known for their large to very large scale. For instance, certain sparsity-promoting inversion techniques involve vectors that easily exceed 230 unknowns while seismic imaging involves the construction and application of matrix-free discretized operators where single matrix-vector evaluations may require hours, days or even weeks on large compute clusters. For these reasons, software development in this field has remained the domain of highly technical codes programmed in low-level languages with little eye for easy development, code reuse and integration with (nonlinear) programs that solve inverse problems. Following ideas from the Symes{\textquoteright} Rice Vector Library and Bartlett{\textquoteright}s C++ object-oriented interface, Thyra, and Reduction/Transformation operators (both part of the Trilinos software package), we developed a software-development environment based on overloading. This environment provides a pathway from in-core prototype development to out-of-core and MPI {\textquoteright}production{\textquoteright} code with a high level of code reuse. This code reuse is accomplished by integrating the out-of-core and MPI functionality into the dynamic object-oriented programming language Python. This integration is implemented through operator overloading and allows for the development of a coordinate-free solver framework that (i) promotes code reuse; (ii) analyses the statements in an abstract syntax tree and (iii) generates executable statements. In the current implementation, we developed an interface to generate executable statements for the out-of-core unix-pipe based (seismic) processing package RSF-Madagascar (rsf.sf.net). The modular design allows for interfaces to other seismic processing packages and to in-core Python packages such as numpy. So far, the implementation overloads linear operators and elementwise reduction/transformation operators. We are planning extensions towards nonlinear operators and integration with existing (parallel) solver frameworks such as Trilinos.},
	keywords = {AIP, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AIP/2007/herrmann07AIPsit/herrmann07AIPsit.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007SEGsnt,
	title = {Seismic noise: the good, the bad and the ugly},
	booktitle = {SEG Summer Research Workshop: Seismic Noise: Origins Preventions, Mitigation, Utilization},
	year = {2007},
	note = {Presented at SEG Summer Research Workshop: Seismic Noise: Origins, Prevention, Mitigation, Utilization},
	abstract = {In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three problems related to seismic noise, namely the {\textquoteright}good{\textquoteright}, corresponding to noise generated by random sampling; the {\textquoteright}bad{\textquoteright}, corresponding to coherent noise for which (inaccurate) predictions exist and the {\textquoteright}ugly{\textquoteright} for which no predictions exist. We will show that the compressive capabilities of curvelets on seismic data and images can be used to tackle these three categories of noise-related problems.},
	keywords = {SEG, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGsnt/herrmann07SEGsnt.pdf},
	author = {Felix J. Herrmann and D. Wilkinson}
}
@conference {maysami2007EAGEsrc,
	title = {Seismic reflector characterization by a multiscale detection-estimation method},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Seismic transitions of the subsurface are typically considered as zero-order singularities (step functions). According to this model, the conventional deconvolution problem aims at recovering the seismic reflectivity as a sparse spike train. However, recent multiscale analysis on sedimentary records revealed the existence of accumulations of varying order singularities in the subsurface, which give rise to fractional-order discontinuities. This observation not only calls for a richer class of seismic reflection waveforms, but it also requires a different methodology to detect and characterize these reflection events. For instance, the assumptions underlying conventional deconvolution no longer hold. Because of the bandwidth limitation of seismic data, multiscale analysis methods based on the decay rate of wavelet coefficients may yield ambiguous results. We avoid this problem by formulating the estimation of the singularity orders by a parametric nonlinear inversion method.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/maysami07EAGEsrc/maysami07EAGEsrc.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/maysami07EAGEsrc/maysami07EAGEsrc_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7081},
	author = {Mohammad Maysami and Felix J. Herrmann}
}
@conference {herrmann2007CYBERsmc,
	title = {Seismology meets compressive sampling},
	booktitle = {Cyber},
	year = {2007},
	note = {Presented at the joint NSF-IPAM meeting. Los Angeles. October, 2007},
	abstract = {Presented at Cyber-Enabled Discovery and Innovation: Knowledge Extraction as a success story lecture. See for more detail https://www.ipam.ucla.edu/programs/cdi2007/},
	keywords = {Cyber, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Cyber/2007/herrmann07CYBERsmc/herrmann07CYBERsmc.pdf},
	author = {Felix J. Herrmann}
}
@conference {challa2007EAGEsrf,
	title = {Signal reconstruction from incomplete and misplaced measurements},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Constrained by practical and economical considerations, one often uses seismic data with missing traces. The use of such data results in image artifacts and poor spatial resolution. Sometimes due to practical limitations, measurements may be available on a perturbed grid, instead of on the designated grid. Due to algorithmic requirements, when such measurements are viewed as those on the designated grid, the recovery procedures may result in additional artifacts. This paper interpolates incomplete data onto regular grid via the Fourier domain, using a recently developed greedy algorithm. The basic objective is to study experimentally as to what could be the size of the perturbation in measurement coordinates that allows for the measurements on the perturbed grid to be considered as on the designated grid for faithful recovery. Our experimental work shows that for compressible signals, a uniformly distributed perturbation can be offset with slightly more number of measurements.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/challa07EAGEsrf/challa07EAGEsrf.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=6917},
	author = {Challa S. Sastry and Gilles Hennenfent and Felix J. Herrmann}
}
@manual {rossross07MNsda,
	title = {SLIMpy development and programming interface for seismic processing},
	year = {2007},
	abstract = {Inverse problems in (exploration) seismology are known for their large to very large scale. For instance, certain sparsity-promoting inversion techniques involve vectors that easily exceed unknowns while seismic imaging involves the construction and application of matrix-free discretized operators where single matrix-vector evaluations may require hours, days or even weeks on large compute clusters. For these reasons, software development in this field has remained the domain of highly technical codes programmed in low-level languages with little eye for easy development, code reuse and integration with (nonlinear) programs that solve inverse problems.Following ideas from the Symes{\textquoteright} Rice Vector Library and Bartlett{\textquoteright}s C++ object-oriented interface, Thyra, and Reduction/Transformation operators (both part of the Trilinos software package), we developed a software-development environment based on overloading. This environment provides a pathway from in-core prototype development to out-of-core and MPI {\textquoteright}production{\textquoteright} code with a high level of code reuse. This code reuse is accomplished by integrating the out-of-core and MPI functionality into the dynamic object-oriented programming language Python. This integration is implemented through operator overloading and allows for the development of a coordinate-free solver framework that (i) promotes code reuse; (ii) analyses the statements in an abstract syntax tree and (iii) generates executable statements. In the current implementation, we developed an interface to generate executable statements for the out-of-core unix-pipe based (seismic) processing package RSF-Madagascar (rsf.sf.net). The modular design allows for interfaces to other seismic processing packages and to in-core Python packages such as numpy. So far, the implementation overloads linear operators and element-wise reduction/transformation operators. We are planning extensions towards nonlinear operators and integration with existing (parallel) solver frameworks such as Trilinos.},
	keywords = {SLIM, software},
	url = {https://slim.gatech.edu/Software/SLIM/SLIMpy/},
	author = {Sean Ross-Ross and Henryk Modzelewski and Cody R. Brown and Felix J. Herrmann}
}
@manual {vandenberg07MNsat,
	title = {SPARCO: a toolbox for testing sparse reconstruction algorithms},
	year = {2007},
	month = {10},
	abstract = {Sparco is a suite of problems for testing and benchmarking algorithms for sparse signal reconstruction. It is also an environment for creating new test problems, and a suite of standard linear operators is provided from which new problems can be assembled. Sparco is implement ed entirely in Matlab and is self contained. (A few optional test problems are based on the CurveLab toolbox, which can be installed separately.) At the core of the sparse recovery problem is the linear system $Ax+r=b$, where $A$ is an $m$-by-$n$ linear operator and the $m$-vector $b$ is the observed signal. The goal is to find a sparse $n$-vector $x$ such that $r$ is small in norm.},
	keywords = {SLIM},
	url = {http://www.cs.ubc.ca/labs/scl/sparco/},
	author = {Ewout van den Berg and Michael P. Friedlander}
}
@conference {moghaddam2007CSEGsac,
	title = {Sparsity and continuity enhancing seismic imaging},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2007},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {A non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness and continuity constraints is proposed. The applied formalism explores curvelets as a directional frame that, by their sparsity on the image, and their invariance under the imaging operators, allows for a stable recovery of the amplitudes. Our method is based on the estimation of the normal operator in the form of an {\textquoteright}eigenvalue{\textquoteright} decompsoition with curvelets as the eigenvectors{\textquoteright}. Subsequently, we propose an inversion method that derives from estimation of the normal operator and is formulated as a convex optimization problem. Sparsity in the curvelet domain as well as continuity along the reflectors in the image domain are promoted as part of this optimization. Our method is tested with a reverse-time {\textquoteright}wave-equation{\textquoteright} migration code simulating the acoustic wave equation.},
	keywords = {SLIM},
	url = {http://cseg.ca/assets/files/resources/abstracts/2007/091S0130.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann and Christiaan C. Stolk}
}
@conference {herrmann2007AIPssd,
	title = {Stable seismic data recovery},
	booktitle = {AIP},
	year = {2007},
	abstract = {In this talk, directional frames, known as curvelets, are used to recover seismic data and images from noisy and incomplete data. Sparsity and invariance properties of curvelets are exploited to formulate the recovery by a {\textquoteleft}1-norm promoting program. It is shown that our data recovery approach is closely linked to the recent theory of {\textquoteleft}{\textquoteleft}compressive sensing{\textquoteright}{\textquoteright} and can be seen as a first step towards a nonlinear sampling theory for wavefields. The second problem that will be discussed concerns the recovery of the amplitudes of seismic images in clutter. There, the invariance of curvelets is used to approximately invert the Gramm operator of seismic imaging. In the high-frequency limit, this Gramm matrix corresponds to a pseudo-differential operator, which is near diagonal in the curvelet domain.In this talk, directional frames, known as curvelets, are used to recover seismic data and images from noisy and incomplete data. Sparsity and invariance properties of curvelets are exploited to formulate the recovery by a l1-norm promoting program. It is shown that our data recovery approach is closely linked to the recent theory of {\textquoteleft}{\textquoteleft}compressive sensing{\textquoteright}{\textquoteright} and can be seen as a first step towards a nonlinear sampling theory for wavefields. The second problem that will be discussed concerns the recovery of the amplitudes of seismic images in clutter. There, the invariance of curvelets is used to approximately invert the Gramm operator of seismic imaging. In the high-frequency limit, this Gramm matrix corresponds to a pseudo-differential operator, which is near diagonal in the curvelet domain.},
	keywords = {AIP, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AIP/2007/herrmann07AIPssd/herrmann07AIPssd.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007EAGEsrm,
	title = {Surface related multiple prediction from incomplete data},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Incomplete data, unknown source-receiver signatures and free-surface reflectivity represent challenges for a successful prediction and subsequent removal of multiples. In this paper, a new method will be represented that tackles these challenges by combining what we know about wavefield (de-)focussing, by weighted convolutions/correlations, and recently developed curvelet-based recovery by sparsity-promoting inversion (CRSI). With this combination, we are able to leverage recent insights from wave physics towards a nonlinear formulation for the multiple-prediction problem that works for incomplete data and without detailed knowledge on the surface effects.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsrm/herrmann07EAGEsrm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsrm/herrmann07EAGEsrm_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=6496},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2006SEGaos,
	title = {Application of stable signal recovery to seismic data interpolation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2006},
	pages = {2797-2801},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We propose a method for seismic data interpolation based on 1) the reformulation of the problem as a stable signal recovery problem and 2) the fact that seismic data is sparsely represented by curvelets. This method does not require information on the seismic velocities. Most importantly, this formulation potentially leads to an explicit recovery condition. We also propose a large-scale problem solver for the 1-regularization minimization involved in the recovery and successfully illustrate the performance of our algorithm on 2D synthetic and real examples. \copyright2006 Society of Exploration Geophysicists},
	keywords = {amplitude, continuity, curvelets, fast transform, interpolation, iterative thresholding, Presentation, regularization minimization, SEG, seismic data, SLIM},
	doi = {10.1190/1.2370105},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2006/hennenfent06SEGaos/hennenfent06SEGaos.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2006/hennenfent06SEGaos/hennenfent06SEGaos_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {lin2006SINBADci,
	title = {Compressed imaging},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {In 1998 Grimbergen et. al. introduced a new method for computing wavefield propagation which improved on the previously employed local explicit operator method in that it exhibited no dip limitation, accurately handled laterally varying background ground velocity models, and is unconditionally stable. These desirable properties are mainly attributed to bringing the propagation problem into an eigenvector basis that diagonalizes the propagation operators. This modal-transform method, however, requires at each depth-level the solution of a large-scale sparse eigenvalue problem to compute the square-root of the Helmholtz operator. By using recent results from compressed sensing, we hope to reduce these computational costs that typically involve the synthesizes of the imaging operators and the cost of matrix-vector products. To reduce these costs, we compress the extrapolation operators by using only a fraction of the positive eigenvalues and temporal frequencies. This reduction not only leads to smaller matrices but also to reduced synthesis costs. These reductions go at the expense of solving a recovery problem from incomplete data. During the presentation, we show that wavefields can accurately be extrapolated with a compressed operators and competitive costs.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/lin2006SINBADci/lin2006SINBADci.pdf},
	author = {Tim T.Y. Lin}
}
@conference {modzelewski2006SINBADdas,
	title = {Design and specifications for SLIM{\textquoteright}s software framework},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/modzelewski2006SINBADdas/modzelewski2006SINBADdas.pdf},
	author = {Henryk Modzelewski}
}
@conference {moghaddam2006SINBADioa,
	title = {Imaging operator approximation using curvelets},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {In this presentation, the normal (demigation-migration) operator is studied in terms of a pseudo-differential operator. The invariance of curvelets under this operator and their sparsity on the seismic images is used to precondition the migration operator. A brief overview will be given on some of the theory from micro-local analysis which proofs that curvelets remain approximately invariant under the operator. The proper setting for which a diagonal approximation in the curvelet domain is accurate is discussed together with different methods that estimate this diagonal from of-the-shelf migration operators. This is joint work with Chris Stolk.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/moghaddam2006SINBADioa/moghaddam2006SINBADioa.pdf},
	author = {Peyman P. Moghaddam}
}
@conference {thomson2006SINBADlss,
	title = {Large-scale seismic data recovery by the parallel windowed curvelet transform},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {We propose using overlapping, tapered windows to process seismic data in parallel. This method consists of numerically tight linear operators and adjoints that are suitable for use in iterative algorithms. This method is also highly scalable and makes parallelprocessing of large seismic data sets feasible. We use this scheme to define the Parallel Windowed Fast Discrete Curvelet Transform (PWFDCT), which we have applied to a seismic data interpolation algorithm. Some preliminary results will be shown. Henryk Modzeleweski: Design and specifications for SLIMPy{\textquoteright}s software framework The SLIM group is actively developing software for seismic imaging. This talk will give a general overview of the software development philosophy adopted by SLIM. The covered topics will include: 1) adopting Python for object-oriented programming, 2) including parallelism into the algorithms used in seismic imaging/modeling, 3) in-house algorithms for seismic imaging, and 4) contributions to Madagascar (RSF). The talk will serve as an introduction to the other presentations in the session "SINBAD Software releases".},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/thomson2006SINBADlss/thomson2006SINBADlss.pdf},
	author = {Darren Thomson}
}
@conference {herrmann2006SINBADmpf,
	title = {Multiple prediction from incomplete data},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADmpf/herrmann2006SINBADmpf.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2006SINBADtnf,
	title = {The Nonuniform Fast Discrete Curvelet Transform (NFDCT)},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {The authors present an extension of the fast discrete curvelet transform (FDCT) to nonuniformly sampled data. This extension not only restores curvelet compression rates for nonuniformly sampled data but also removes noise and maps the data to a regular grid.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/hennenfent2006SINBADtnf/hennenfent2006SINBADtnf.pdf},
	author = {Gilles Hennenfent}
}
@conference {herrmann2006SINBADom,
	title = {Opening meeting},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADom/herrmann2006SINBADom.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2006SINBADpms,
	title = {Primary-multiple separation by curvelet frames},
	booktitle = {SINBAD 2006},
	volume = {170},
	year = {2006},
	pages = {781-799},
	publisher = {Geophysical Journal International},
	organization = {Geophysical Journal International},
	abstract = {Predictive multiple suppression methods consist of two main steps: a prediction step, during which multiples are predicted from seismic data, and a primary-multiple separation step, during which the predicted multiples are {\textquoteright}matched{\textquoteright} with the true multiples in the data and subsequently removed. The last step is crucial in practice: an incorrect separation will cause residual multiple energy in the result or may lead to a distortion of the primaries, or both. To reduce these adverse effects, a new transformed-domain method is proposed where primaries and multiples are separated rather than matched. This separation is carried out on the basis of differences in the multiscale and multidirectional characteristics of these two signal components. Our method uses the curvelet transform, which maps multidimensional data volumes into almost orthogonal localized multidimensional prototype waveforms that vary in directional and spatio-temporal content. Primaries-only and multiples-only signal components are recovered from the total data volume by a nonlinear optimization scheme that is stable under noisy input data. During the optimization, the two signal components are separated by enhancing sparseness (through weighted l1-norms) in the transformed domain subject to fitting the observed data as the sum of the separated components to within a user-defined tolerance level. Whenever the prediction for the two signal components in the transformed domain correlate, the recovery is suppressed while for regions where the correlation is small the method seeks the sparsest set of coefficients that represent each signal component. Our algorithm does not seek a matched filter and as such it differs fundamentally from traditional adaptive subtraction methods. The method derives its stability from the sparseness obtained by a non-parametric multiscale and multidirectional overcomplete signal representation. This sparsity serves as prior information and allows for a Bayesian interpretation of our method during which the log-likelihood function is minimized while the two signal components are assumed to be given by a superposition of prototype waveforms, drawn independently from a probability function that is weighted by the predicted primaries and multiples. In this paper, the predictions are based on the data-driven surface-related multiple elimination (SRME) method. Synthetic and field data examples show a clean separation leading to a considerable improvement in multiple suppression compared to the conventional method of adaptive matched filtering. This improved separation translates into an improved stack.},
	keywords = {Presentation, SINBAD, SLIM},
	doi = {10.1111/j.1365-246X.2007.03360.x},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADpms/herrmann2006SINBADpms.pdf},
	author = {Felix J. Herrmann and Urs Boeniger and D. J. Verschuur}
}
@conference {herrmann2006SINBADapo1,
	title = {A primer on sparsity transforms: curvelets and wave atoms},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an overview will be given on the different sparsity transforms that are used at SLIM. Emphasis will be on two directional and multiscale wavelet transforms, namely the curvelet and the recently introduced wave-atom transforms. The main properties of these transforms will be listed and their performance on seismic data will be discussed.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/hennenfent2006SINBADapo/hennenfent2006SINBADapo.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2006SINBADapo,
	title = {A primer on sparsity transforms: curvelets and wave atoms},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an introduction will be given on the method of stable recovery from noisy and incomplete data. Strong recovery conditions that guarantee the recovery for arbitrary acquisition geometries will be reviewed and numerical recovery examples will be presented.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/hennenfent2006SINBADapo/hennenfent2006SINBADapo.pdf},
	author = {Gilles Hennenfent}
}
@conference {herrmann2006SINBADapow,
	title = {A primer on weak conditions for stable recovery},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an introduction will be given on the method of stable recovery from noisy and incomplete data. Weak recovery conditions that guarantee the recovery for typical acquisition geometries will be reviewed and numerical recovery examples will be presented. The advantage of these weak conditions is that they are less pessimistic and {\textquoteleft}verifiable{\textquoteright} or very large-scale acquisition geometries.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADapow/herrmann2006SINBADapow.pdf},
	author = {Felix J. Herrmann}
}
@conference {thomson2006SINBADppe,
	title = {(P)SLIMPy: parallel extension},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {The parallel extensions to the SLIMpy environment enable pipe-based processing of large data sets in an MPI-based parallel environment. Parallel processing can be done by straightforward slicing of data, or by using an overlapping domain decomposition that requires communication between different processors. The principal aim of the parallel extensions is to leave abstract numerical algorithms (ANA{\textquoteright}s) and applications programmed for use in SLIMpy untouched when moving to parallel processing. The object-oriented functionality of Python makes this possible.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/thomson2006SINBADppe/thomson2006SINBADppe.pdf},
	author = {Darren Thomson}
}
@conference {maysami2006SINBADrro,
	title = {Recent results on seismic deconvolution},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {One of the important steps in seismic imaging is to provide suitable information about boundaries. Sharp variation of physical properties at a layer boundary cause reflection the wavefield. In previous work done by C. M. Dupuis, seismic signal characterization is divided into two steps: detection and estimation. In the detection phase, the goal is to find all singularities in a seismic section regardless of their order and then to categorize the data to different events by windowing each singularity. In the estimation step, we determine the order of singularity more precisely by using a rough estimate based on the detection phase. Traditionally, a redundant dictionary method is employed for the detection part. However, we attempt to instead use a new L1-solver developed by D.L. Donoho: the Stagewise Orthogonal Matching Pursuit (StOMP). It approximates the solution to inverse problems while promoting the sparsity in the solution vector. This algorithm will allow us to experimentally confirm the recent analysis by S. Mallat on spiky deconvolution limits, which imposes a required minimum distance between spikes. This required minimum distance between different spikes is dependent on the number of spikes as well as the width of the chosen source wavelet used in convolution with the train. These results allow for the design of more robust and accurate detection schemes for seismic signal characterization.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/maysami06SINBADrro/maysami06SINBADrro_pres.pdf},
	author = {Mohammad Maysami}
}
@conference {sastry2007SINBADrfu,
	title = {Recovery from unstructured data},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/sastry2007SINBADrfu/sastry2007SINBADrfu.pdf},
	author = {Challa S. Sastry and Gilles Hennenfent and Felix J. Herrmann}
}
@conference {hennenfent2006SINBADros,
	title = {Recovery of seismic data: practical considerations},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {We propose a method for seismic data interpolation based on 1) the reformulation of the problem as a stable signal recovery problem and 2) the fact that seismic data is sparsely represented by curvelets. This method does not require information on the seismic velocities. Most importantly, this formulation potentially leads to an explicit recovery condition. We also propose a large-scale problem solver for the l1-regularization minimization involved in the recovery and successfully illustrate the performance of our algorithm on 2D synthetic and real examples.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/hennenfent2006SINBADros/hennenfent2006SINBADros.pdf},
	author = {Gilles Hennenfent}
}
@conference {moghaddam2006SINBADsac,
	title = {Sparsity- and continuity-promoting norms for seismic images},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation, the importance of sparsity and continuity enhancing energy norms is emphasized for seismic imaging and inversion. The continuity promoting energy norm is justified by the apparent smoothness of reflectors in the direction along and the oscillatory behavior across the interfaces. This energy norm is called anisotropic diffusion and will be defined mathematically. Denoising examples will be given during which seismic images are recovered from the noise by a joint norm-one and continuity promoting minimization.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/moghaddam2006SINBADsac/moghaddam2006SINBADsac.pdf},
	author = {Peyman P. Moghaddam}
}
@conference {herrmann2006SINBADsac,
	title = {Sparsity- and continuity-promoting seismic image recovery with curvelets},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {A nonlinear singularity-preserving solution to seismic image recovery with sparseness and continuity constraints is proposed. The method explicitly explores the curvelet transform as a directional frame expansion that, by virtue of its sparsity on seismic images and its invariance under the Hessian of the linearized imaging problem, allows for a stable recovery of the migration amplitudes from noisy data. The method corresponds to a preconditioning that corrects the amplitudes during a post-processing step. The solution is formulated as a nonlinear optimization problem where sparsity in the curvelet domain as well as continuity along the imaged reflectors are jointly promoted. To enhance sparsity, the l1-norm on the curvelet coefficients is minimized while continuity is promoted by minimizing an anisotropic diffusion norm on the image. The performance of the recovery scheme is evaluated with {\textquoteright}wave-equation{\textquoteright} migration code on a synthetic dataset. This is joint work with Peyman Moghaddam.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADsac/herrmann2006SINBADsac.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2006SINBADsra,
	title = {Stable recovery and separation of seismic data},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an overview will be given on how seismic data regularization and separation problems can be cast into the framework of stable signal recovery. It is shown that the successful solution of these two problems depends on the existence of signal expansions that are compressible. Preliminary examples will be shown.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADsra/herrmann2006SINBADsra.pdf},
	author = {Felix J. Herrmann}
}
@conference {beyreuther2005SEGcot,
	title = {Computation of time-lapse differences with 3-D directional frames},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {24},
	year = {2005},
	pages = {2488-2491},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We present an alternative method of extracting production related differences from time-lapse seismic data sets. Our method is not based on the actual subtraction of the two data sets, risking the enhancement of noise and introduction of artifacts due to local phase rotation and slightly misaligned events. Rather, it mutes events of the monitor survey with respect to the baseline survey based on the magnitudes of coefficients in a sparse and local atomic decomposition. Our technique is demonstrated to be an effective tool for enhancing the time-lapse signal from surveys which have been cross-equalized. \copyright2005 Society of Exploration Geophysicists},
	keywords = {SEG, SLIM},
	doi = {10.1190/1.2148227},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2005/Beyreuther05SEGcot/Beyreuther05SEGcot.pdf},
	author = {Moritz Beyreuther and Jamin Cristall and Felix J. Herrmann}
}
@conference {herrmann2005CSEGnld,
	title = {Non-linear data continuation with redundant frames},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2005},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {We propose an efficient iterative data interpolation method using continuity along reflectors in seismic images via curvelet and discrete cosine transforms. The curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). The advantage of using curvelets is that these frames are sparse for high-frequency caustic-free solutions of the wave-equation. Since we are dealing with less than ideal data (e.g. bandwidth-limited), we compliment the curvelet frames with the discrete cosine transform. The latter is motivated by the successful data continuation with the discrete Fourier transform. By choosing generic basis functions we circumvent the necessity to make parametric assumptions (e.g., through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. Synthetic and real data examples demonstrate that our algorithm provides interpolated traces that accurately reproduce the wavelet shape as well as the AVO behavior along events in shot gathers.},
	keywords = {Presentation, SLIM},
	url = {http://www.cseg.ca/assets/files/resources/abstracts/2005/101S0201-Herrmann_F_Non_Linear_Data_Continuation.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Herrmann05CSEGnld/Herrmann05CSEGnld_pres.pdf},
	author = {Felix J. Herrmann and Gilles Hennenfent}
}
@conference {herrmann2005CSEGnlr,
	title = {Non-linear regularization in seismic imaging},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2005},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {Two complementary solution strategies to the least-squares imaging problem with sparseness \& continuity continuity constraints are proposed. The applied formalism explores the sparseness of curvelets coefficients of the reflectivity and their invariance under the demigration-migration operator. We achieve the solution by jointly minimizing a weighted l1-norm on the curvelet coefficients and an anisotropic difussion or total variation norm on the imaged reflectivity model. The l1-norm exploits the sparsenss of the reflectivity in the curvelet domain whereas the anisotropic norm enhances the continuity along the reflections while removing artifacts residing in between reflectors. While the two optimization methods (convex versus non-convex) share the same type of regularization, they differ in flexibility how to handle additional constraints on the coefficients of the imaged reflectivity and in computational expense. A brief sketch of the theory is provided along with a number of synthetic examples.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Herrmann05CSEGnlr/Herrmann05CSEGnlr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Herrmann05CSEGnlr/Herrmann05CSEGnlr_pres.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2005EAGEosf,
	title = {Optimization strategies for sparseness- and continuity-enhanced imaging: theory},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {Two complementary solution strategies to the least-squares migration problem with sparseness- and continuity constraints are proposed. The applied formalism explores the sparseness of curvelets on the reflectivity and their invariance under the demigration-migration operator. Sparseness is enhanced by (approximately) minimizing a (weighted) l1-norm on the curvelet coefficients. Continuity along imaged reflectors is brought out by minimizing the anisotropic diffusion or total variation norm which penalizes variations along and in between reflectors. A brief sketch of the theory is provided as well as a number of synthetic examples. Technical details on the implementation of the optimization strategies are deferred to an accompanying paper: implementation.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGEosf/Herrmann05EAGEosf.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1343},
	author = {Felix J. Herrmann and Peyman P. Moghaddam and R. Kirlin}
}
@conference {herrmann2005EAGErcd,
	title = {Robust curvelet-domain data continuation with sparseness constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {A robust data interpolation method using curvelets frames is presented. The advantage of this method is that curvelets arguably provide an optimal sparse representation for solutions of wave equations with smooth coefficients. As such curvelets frames circum- vent {\textendash} besides the assumption of caustic-free data {\textendash} the necessity to make parametric assumptions (e.g. through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. A brief sketch of the theory is provided as well as a number of examples on synthetic and real data.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGErcd/Herrmann05EAGErcd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGErcd/Herrmann05EAGErcd.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1112},
	author = {Felix J. Herrmann and Gilles Hennenfent}
}
@conference {herrmann2005EAGErcd1,
	title = {Robust curvelet-domain primary-multiple separation with sparseness constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {A non-linear primary-multiple separation method using curvelets frames is presented. The advantage of this method is that curvelets arguably provide an optimal sparse representation for both primaries and multiples. As such curvelets frames are ideal candidates to separate primaries from multiples given inaccurate predictions for these two data components. The method derives its robustness regarding the presence of noise; errors in the prediction and missing data from the curvelet frame{\textquoteright}s ability (i) to represent both signal components with a limited number of multi-scale and directional basis functions; (ii) to separate the components on the basis of differences in location, orientation and scales and (iii) to minimize correlations between the coefficients of the two components. A brief sketch of the theory is provided as well as a number of examples on synthetic and real data.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGErcd1/Herrmann05EAGErcd1.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1384},
	author = {Felix J. Herrmann and D. J. Verschuur}
}
@article {herrmann2005ICAEsdb,
	title = {Seismic deconvolution by atomic decomposition: a parametric approach with sparseness constraints},
	journal = {Integrated Computer-Aided Engineering},
	volume = {12},
	number = {1},
	year = {2005},
	month = {01},
	pages = {69-90},
	publisher = {IOS Press},
	address = {Amsterdam, The Netherlands},
	abstract = {In this paper an alternative approach to the blind seismic deconvolution problem is presented that aims for two goals namely recovering the location and relative strength of seismic reflectors, possibly with super-localization, as well as obtaining detailed parametric characterizations for the reflectors. We hope to accomplish these goals by decomposing seismic data into a redundant dictionary of parameterized waveforms designed to closely match the properties of reflection events associated with sedimentary records. In particular, our method allows for highly intermittent non-Gaussian records yielding a reflectivity that can no longer be described by a stationary random process or by a spike train. Instead, we propose a reflector parameterization that not only recovers the reflector{\textquoteright}s location and relative strength but which also captures reflector attributes such as its local scaling, sharpness and instantaneous phase-delay. The first set of parameters delineates the stratigraphy whereas the second provides information on the lithology. As a consequence of the redundant parameterization, finding the matching waveforms from the dictionary involves the solution of an ill-posed problem. Two complementary sparseness-imposing methods Matching and Basis Pursuit are compared for our dictionary and applied to seismic data.},
	keywords = {deconvolution, Modelling, Processing, SLIM},
	issn = {1069-2509},
	url = {http://dl.acm.org/citation.cfm?id=1238980.1238986},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/IntegratedComputerAidedEngineering/2005/herrmann2005ICAEsdb/herrmann2005ICAEsdb.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2005EAGEsdr,
	title = {Seismic deconvolution revisited with curvelet frames},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {We propose an efficient iterative curvelet-regularized deconvolution algorithm that exploits continuity along reflectors in seismic images. Curvelets are a new multiscale transform that provides sparse representations for images (such as seismic images) that comprise smooth objects separated by piece-wise smooth discontinuities. Our technique combines conjugate gradient-based convolution operator inversion with noise regularization that is performed using non-linear curvelet coefficient shrinkage (thresholding). The shrinkage operation leverages the sparsity of curvelets representations. Simulations demonstrate that our algorithm provides improved resolution compared to the traditional Wiener-based deconvolution approach.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Hennenfent05EAGEsdr/Hennenfent05EAGEsdr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Hennenfent05EAGEsdr/Hennenfent05EAGEsdr_poster.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1383},
	author = {Gilles Hennenfent and R. Neelamani and Felix J. Herrmann}
}
@conference {hennenfent2005SEGscd,
	title = {Sparseness-constrained data continuation with frames: applications to missing traces and aliased signals in 2/3-D},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2005},
	pages = {2162-2165},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We present a robust iterative sparseness-constrained interpolation algorithm using 2-/3-D curvelet frames and Fourier-like transforms that exploits continuity along reflectors in seismic data. By choosing generic transforms, we circumvent the necessity to make parametric assumptions (e.g. through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. Simulation and real data examples for data with moderately sized gaps demonstrate that our algorithm provides interpolated traces that accurately reproduce the wavelet shape as well as the AVO behavior. Our method also shows good results for de-aliasing judged by the behavior of the ($f-k$)-spectrum before and after regularization. \copyright2005 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2148142},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2005/Hennenfent05SEGscd/Hennenfent05SEGscd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2005/Hennenfent05SEGscd/Hennenfent05SEGscd_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {hennenfent2005CSEGscs,
	title = {Sparseness-constrained seismic deconvolution with curvelets},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2005},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the convolution operator inversion. The Curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). Our iterative Curvelet-regularized deconvolution algorithm combines conjugate gradient-based inversion with noise regularization performed using non-linear Curvelet coefficient thresholding. The thresholding operation enhances the sparsity of Curvelet representations. We show on a synthetic example that our algorithm provides improved resolution and continuity along reflectors as well as reduced ringing effect compared to the iterative Wiener-based deconvolution approach.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Hennenfent05CSEGscs/Hennenfent05CSEGscs.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Hennenfent05CSEGscs/Hennenfent05CSEGscs_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann and R. Neelamani}
}
@conference {beyreuther2004EAGEcdo,
	title = {Curvelet denoising of 4-D seismic},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2004},
	month = {06},
	abstract = {With burgeoning world demand and a limited rate of discovery of new reserves, there is increasing impetus upon the industry to optimize recovery from already existing fields. 4D, or time-lapse, seismic imaging is an emerging technology that holds great promise to better monitor and optimise reservoir production. The basic idea behind 4D seismic is that when multiple 3D surveys are acquired at separate calendar times over a producing field, the reservoir geology will not change from survey to survey but the state of the reservoir fluids will change. Thus, taking the difference between two 3D surveys should remove the static geologic contribution to the data and isolate the time- varying fluid flow component. However, a major challenge in 4D seismic is that acquisition and processing differences between 3D surveys often overshadow the changes caused by fluid flow. This problem is compounded when 4D effects are sought to be derived from vintage 3D data sets that were not originally acquired with 4D in mind. The goal of this study is to remove the acquisition and imaging artefacts from a 4D seismic difference cube using Curveket processing techniques.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Beyreuther04EAGEcdo/beyreuther2004EAGEcdo_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Beyreuther04EAGEcdo/Beyreuther04EAGEcdo_pres.pdf},
	url2 = {https://circle.ubc.ca/bitstream/handle/2429/453/EAGE4D2004.pdf?sequence=1},
	author = {Moritz Beyreuther and Felix J. Herrmann and Jamin Cristall}
}
@conference {herrmann2004CSEGcia1,
	title = {Curvelet imaging and processing: adaptive multiple elimination},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {Predictive multiple suppression methods consist of two main steps: a prediction step, in which multiples are predicted from the seismic data, and a subtraction step, in which the predicted multiples are matched with the true multiples in the data. The last step appears crucial in practice: an incorrect adaptive subtraction method will cause multiples to be sub-optimally subtracted or primaries being distorted, or both. Therefore, we propose a new domain for separation of primaries and multiples via the Curvelet transform. This transform maps the data into almost orthogonal localized events with a directional and spatial-temporal component. The multiples are suppressed by thresholding the input data at those Curvelet components where the predicted multiples have large amplitudes. In this way the more traditional filtering of predicted multiples to fit the input data is avoided. An initial field data example shows a considerable improvement in multiple suppression.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia1/Herrmann04CSEGcia1_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia1/Herrmann04CSEGcia1.pdf},
	author = {Felix J. Herrmann and D. J. Verschuur}
}
@conference {herrmann2004CSEGcia,
	title = {Curvelet imaging and processing: an overview},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {In this paper an overview is given on the application of directional basis functions, known under the name Curvelets/Contourlets, to various aspects of seismic processing and imaging. Key conceps in the approach are the use of (i) directional basis functions that localize in both domains (e.g. space and angle); (ii) non-linear estimation, which corresponds to localized muting on the coefficients, possibly supplemented by constrained optimization (iii) invariance of the basis functions under the imaging operators. We will discuss applications that include multiple and ground roll removal; sparseness-constrained least-squares migration and the computation of 4-D difference cubes.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia/Herrmann04CSEGcia_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia/Herrmann04CSEGcia.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2004CSEGcia2,
	title = {Curvelet imaging and processing: sparseness-constrained least-squares migration},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signal-to-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is, like-wise to the (de)-migration operators, almost diagonalized by the Curvelet transform.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia2/Herrmann04CSEGcia2_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia2/Herrmann04CSEGcia2.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {cristall2004CSEGcpa,
	title = {Curvelet processing and imaging: 4-D adaptive subtraction},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {With burgeoning world demand and a limited rate of discovery of new reserves, there is increasing impetus upon the industry to optimize recovery from already existing fields. 4D, or time-lapse, seismic imaging holds great promise to better monitor and optimise reservoir production. The basic idea behind 4D seismic is that when multiple 3D surveys are acquired at separate calendar times over a producing field, the reservoir geology will not change from survey to survey but the state of the reservoir fluids will change. Thus, taking the difference between two 3D surveys should remove the static geologic contribution to the data and isolate the time-varying fluid flow component. However, a major challenge in 4D seismic is that acquisition and processing differences between 3D surveys often overshadow the changes caused by fluid flow. This problem is compounded when 4D effects are sought to be derived from legacy 3D data sets that were not originally acquired with 4D in mind. The goal of this study is to remove the acquisition and imaging artefacts from a 4D seismic difference cube using Curvelet processing techniques.},
	keywords = {SLIM},
	url = {http://www.cseg.ca/assets/files/resources/abstracts/2004/059S0201-Cristall_J_Curvelet_4D.pdf},
	author = {Jamin Cristall and Moritz Beyreuther and Felix J. Herrmann}
}
@conference {yarham2004CSEGcpa,
	title = {Curvelet processing and imaging: adaptive ground roll removal},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {In this paper we present examples of ground roll attenuation for synthetic and real data gathers by using Contourlet and Curvelet transforms. These non-separable wavelet transforms are locoalized both (x,t)- and (k,f)-domains and allow for adaptive seperation of signal and ground roll. Both linear and non-linear filtering are discussed using the unique properties of these basis that allow for simultaneous localization in the both domains. Eventhough, the linear filtering techniques are encouraging the true added value of these basis-function techniques becomes apparent when we use these decompositions to adaptively substract modeled ground roll from data using a non-linear thesholding procedure. We show real and synthetic examples and the results suggest that these directional-selective basis functions provide a usefull tool for the removal of coherent noise such as ground roll.},
	keywords = {CSEG, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Yarham04CSEGcpa/yarham04csegcpa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Yarham04CSEGcpa/Yarham04CSEGcpa_pres.pdf},
	author = {Carson Yarham and Daniel Trad and Felix J. Herrmann}
}
@conference {herrmann2004SEGcbn,
	title = {Curvelet-based non-linear adaptive subtraction with sparseness constraints},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {23},
	year = {2004},
	pages = {1977-1980},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper an overview is given on the application of directional basis functions, known under the name Curvelets/Contourlets, to various aspects of seismic processing and imaging, which involve adaptive subtraction. Key concepts in the approach are the use of directional basis functions that localize in both domains (e.g. space and angle); non-linear estimation, which corresponds to localized muting on the coefficients, possibly supplemented by constrained optimization. We will discuss applications that include multiple, ground-roll removal and migration denoising. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1851181},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcbn/Herrmann04SEGcbn.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcbn/Herrmann04SEGcbn_pres.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2004EAGEcdl,
	title = {Curvelet-domain least-squares migration with sparseness constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2004},
	month = {06},
	abstract = {A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signal-to-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is like-wise the (de)-migration operators, almost diagonalized by the Curvelet transform.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Herrmann04EAGEcdl/Herrmann04EAGEcdl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Herrmann04EAGEcdl/Herrmann04EAGEcdl_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=2073},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2004SEGcdm,
	title = {Curvelet-domain multiple elimination with sparseness constraints},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {23},
	year = {2004},
	pages = {1333-1336},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Predictive multiple suppression methods consist of two main steps: a prediction step, in which multiples are predicted from the seismic data, and a subtraction step, in which the predicted multiples are matched with the true multiples in the data. The last step appears crucial in practice: an incorrect adaptive subtraction method will cause multiples to be sub-optimally subtracted or primaries being distorted, or both. Therefore, we propose a new domain for separation of primaries and multiples via the Curvelet transform. This transform maps the data into almost orthogonal localized events with a directional and spatial-temporal component. The multiples are suppressed by thresholding the input data at those Curvelet components where the predicted multiples have large amplitudes. In this way the more traditional filtering of predicted multiples to fit the input data is avoided. An initial field data example shows a considerable improvement in multiple suppression. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1851110},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcdm/Herrmann04SEGcdm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcdm/Herrmann04SEGcdm_pres.pdf},
	author = {Felix J. Herrmann and D. J. Verschuur}
}
@conference {herrmann2004EAGEcdp,
	title = {Curvelet-domain preconditioned {\textquoteright}wave-equation{\textquoteright} depth-migration with sparseness and illumination constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2004},
	month = {06},
	abstract = {A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signal- to-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is like-wise the (de)-migration operators, almost diagonalized by the Curvelet transform.},
	keywords = {SLIM},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2004/herrmann2004EAGEcdp/herrmann2004EAGEcdp.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {moghaddam2004SEGmpw,
	title = {Migration preconditioning with curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {23},
	year = {2004},
	pages = {2204-2207},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, the property of Curvelet transforms for preconditioning the migration and normal operators is investigated. These operators belong to the class of Fourier integral operators and pseudo-differential operator, respectively. The effect of this pre-conditioner is shown in term of improvement of sparsity, convergence rate, number of iteration for the Krylov-subspace solver and clustering of singular(eigen) values. The migration operator, which we employed in this work is the common-offset Kirchoff-Born migration. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1845213},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Moghaddam04SEGmpw/Moghaddam04SEGmpw_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Moghaddam04SEGmpw/Moghaddam04SEGmpw_pres.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann}
}
@article {bernabe2004JGRpas,
	title = {Permeability and storativity of binary mixtures of high-and low-porosity materials},
	journal = {Journal of Geophysical Research: Solid Earth},
	volume = {109},
	year = {2004},
	month = {12},
	pages = {B12207},
	abstract = {As a first step toward determining the mixing laws for the transport properties of rocks, we prepared binary mixtures of high- and low-permeability materials by isostatically hot-pressing mixtures of fine powders of calcite and quartz. The resulting rocks were marbles containing varying concentrations of dispersed quartz grains. Pores were present throughout the rock, but the largest ones were preferentially associated with the quartz particles, leading us to characterize the material as being composed of two phases, one with high permeability and the second with low permeability. We measured the permeability and storativity of these materials using the oscillating flow technique, while systematically varying the effective pressure and the period and amplitude of the input fluid oscillation. Control measurements performed using the steady state flow and pulse decay techniques agreed well with the oscillating flow tests. The hydraulic properties of the marbles were highly sensitive to the volume fraction of the high-permeability phase (directly related to the quartz content). Below a critical quartz content, slightly less than 20 wt \%, the high-permeability volume elements were disconnected, and the overall permeability was low. Above the critical quartz content the high-permeability volume elements formed throughgoing paths, and permeability increased sharply. We numerically simulated fluid flow through binary materials and found that permeability approximately obeys a percolation-based mixing law, consistent with the measured permeability of the calcite-quartz aggregates.},
	keywords = {Modeling, permeability, porosity, SLIM},
	doi = {10.1029/2004JB003111},
	url = {https://slim.gatech.edu/Publications/Public/Journals/JournalOfGeophysicalResearch/2004/bernabe04JGRpas/bernabe04JGRpas.pdf},
	url2 = {http://onlinelibrary.wiley.com/doi/10.1029/2004JB003111/abstract},
	author = {Y. Bernab{\'e} and U. Mok and B. Evans and Felix J. Herrmann}
}
@article {herrmann2004GJIssa,
	title = {Seismic singularities at upper-mantle phase transitions: a site percolation model},
	journal = {Geophysical Journal International},
	volume = {159},
	number = {3},
	year = {2004},
	month = {12},
	pages = {949-960},
	abstract = {Mineralogical phase transitions are usually invoked to account for the sharpness of globally observed upper-mantle seismic discontinuities. We propose a percolation-based model for the elastic properties of the phase mixture in the coexistence regions associated with these transitions. The major consequence of the model is that the elastic moduli (but not the density) display a singularity at the percolation threshold of the high-pressure phase. This model not only explains the sharp but continuous change in seismic velocities across the phase transition, but also predicts its abruptness and scale invariance, which are characterized by a non-integral scale exponent. Using the receiver-function approach and new, powerful signal-processing techniques, we quantitatively determine the singularity exponent from recordings of converted seismic waves at two Australian stations (CAN and WRAB). Using the estimated values, we construct velocity{\textendash}depth profiles across the singularities and verify that the calculated converted waveforms match the observations under CAN. Finally, we point out a series of additional predictions that may provide new insights into the physics and fine structure of the upper-mantle transition zone.},
	keywords = {Modelling, percolation, SLIM},
	doi = {10.1111/j.1365-246X.2004.02464.x},
	author = {Felix J. Herrmann and Y. Bernab{\'e}}
}
@conference {herrmann2004EAGEsop,
	title = {Separation of primaries and multiples by non-linear estimation in the curvelet domain},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2004},
	month = {06},
	abstract = {Predictive multiple suppression methods consist of two main steps: a prediction step, in which multiples are predicted from the seismic data, and a subtraction step, in which the predicted multiples are matched with the true multiples in the data. The last step appears crucial in practice: an incorrect adaptive subtraction method will cause multiples to be sub-optimally subtracted or primaries being distorted, or both. Therefore, we propose a new domain for separation of primaries and multiples via the Curvelet transform. This transform maps the data into almost orthogonal localized events with a directional and spatial-temporal component. The multiples are suppressed by thresholding the input data at those Curvelet components where the predicted multiples have large amplitudes. In this way the more traditional filtering of predicted multiples to fit the input data is avoided. An initial field data example shows a considerable improvement in multiple suppression.},
	keywords = {SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/herrmann2004EAGEsop/herrmann2004EAGEsop.pdf},
	author = {Felix J. Herrmann and D. J. Verschuur}
}
@conference {hennenfent2004SEGtta,
	title = {Three-term amplitude-versus-offset (AVO) inversion revisited by curvelet and wavelet transforms},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2004},
	pages = {211-214},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We present a new method to stabilize the three-term AVO inversion using Curvelet and Wavelet transforms. Curvelets are basis functions that effectively represent otherwise smooth objects having discontinuities along smooth curves. The applied formalism explores them to make the most of the continuity along reflectors in seismic images. Combined with Wavelets, Curvelets are used to denoise the data by penalizing high frequencies and small contributions in the AVO-cube. This approach is based on the idea that rapid amplitude changes along the ray-parameter axis are most likely due to noise. The AVO-inverse problem is linearized, formulated and solved for all (x, z) at once. Using densities and velocities of the Marmousi model to define the fluctuations in the elastic properties, the performance of the proposed method is studied and compared with the smoothing along the ray-parameter direction only. We show that our method better approximates the true data after the denoising step, especially when noise level increases. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1851201},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Hennenfent04SEGtta/Hennenfent04SEGtta.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Hennenfent04SEGtta/Hennenfent04SEGtta_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {herrmann2003SPIEmsa,
	title = {Multifractional splines: application to seismic imaging},
	booktitle = {Proceedings of SPIE Technical Conference on Wavelets: Applications in Signal and Image Processing X},
	volume = {5207},
	year = {2003},
	pages = {240-258},
	publisher = {SPIE},
	organization = {SPIE},
	abstract = {Seismic imaging commits itself to locating singularities in the elastic properties of the Earth{\textquoteright}s subsurface. Using the high-frequency ray-Born approximation for scattering from non-intersecting smooth interfaces, seismic data can be represented by a generalized Radon transform mapping the singularities in the medium to seismic data. Even though seismic data are bandwidth limited, signatures of the singularities in the medium carry through this transform and its inverse and this mapping property presents us with the possibility to develop new imaging techniques that preserve and characterize the singularities from incomplete, bandwidth-limited and noisy data. In this paper we propose a non-adaptive Curvelet/Contourlet technique to image and preserve the singularities and a data-adaptive Matching Pursuit method to characterize these imaged singularities by Multi-fractional Splines. This first technique borrows from the ideas within the Wavelet-Vaguelette/Quasi-SVD approach. We use the almost diagonalization of the scattering operator to approximately compensate for (i) the coloring of the noise and hence facilitate estimation; (ii) the normal operator itself. Results of applying these techniques to seismic imaging are encouraging although many open fundamental questions remain.},
	keywords = {Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SPIE/2003/herrmann2003SPIEmsa/herrmann2003SPIEmsa.pdf},
	author = {Felix J. Herrmann},
	editor = {Michael A. Unser and Akram Aldroubi and Andrew F. Laine}
}
@conference {herrmann2003SEGoiw,
	title = {"Optimal" imaging with curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {22},
	year = {2003},
	pages = {997-1000},
	abstract = {In this paper we present a non-linear edge-preserving solution to linear inverse scattering problems based on optimal basis-function decompositions. Optimality of the basis functions allow us to (i) reduce the dimensionality of the inverse problem; (ii) devise non-linear thresholding operators that approximate minimax (minimize the maximal mean square error given the worst possible prior) and that significantly improve the signal-to-noise ratio on the image. We present a reformulation of the standard generalized least-squares formulation of the seismic inversion problem into a formulation based on thresholding, where the singular values, vectors and linear estimators are replaced by quasi-singular values, basis-functions and thresholding. To limit the computational burden we use a Monte-Carlo sampling method to compute the quasi-singular values. With the proposed method, we aim to significantly improve the signal-to-noise ratio (SNR) on the model space and hence the resolution of the seismic image. While classical Tikhonov-regularized methods only gain the square-root of the SNR on the data for the SNR on the model our method scales almost linearly. This significant improvement of the SNR allows us to discern events at high frequencies which would normally be in the noise.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1818117},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2003/Herrmann03SEGoiw/Herrmann03SEGoiw_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2001EAGEsas,
	title = {Scaling and seismic reflectivity: implications of scaling on AVO},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2001},
	month = {06},
	abstract = {AVO analysis of seismic data is based on the assumption that transitions in the earth consist of jump discontinuities only. Generalization of these transitions to more realistic transitions shows a drastic change in observed AVO behavior, especially for the large angles currently attained by increasing cable lengths. We propose a simple ities. After renormalization, the inverted fluctuations regain their relative magnitudes which, due to the scaling, may have been significantly distorted.},
	keywords = {SLIM},
	author = {Felix J. Herrmann}
}
