@conference {herrmann20205IMAGEapg,
	title = {Attention Please: Getting the Most from CIGs with Learned Summary Statistics},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {attention, Bayesian inference, CIG, deep learning, diffusion models, FWI, IMAGE, Imaging, Inverse problems, learned summary statistics, RTM, SEG, uncertainty calibration, Uncertainty quantification, velocity model building, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/herrmann20205IMAGEapg},
	author = {Shiqin Zeng and Rafael Orozco and Yunlin Zeng and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@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 {herrmann2025AIDT4EScad,
	title = {Context-aware Digital Twin for Underground Storage},
	booktitle = {Artificial Intelligence and Digital Twins for Earth Systems 2025, Austin, TX},
	year = {2025},
	month = {9},
	keywords = {AIDT4ES, Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, GCS, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/AIDT4ES/2025/herrmann2025AIDT4EScad},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ipsita Bhar and Huseyin Tuna Erdinc and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {herrmann2025IMSIcad,
	title = {Context-Aware Digital Twin for Underground Storage Operations and Decision Making},
	booktitle = {Data Assimilation and Inverse Problems for Digital Twins Workshop 205, IMSI, Chicago},
	year = {2025},
	month = {10},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, decision making, digital twin, experimental design, GCS, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/IMSI/2025/herrmann2025IMSIcad},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ipsita Bhar and Huseyin Tuna Erdinc and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {cirakman2025IMAGEesp,
	title = {Efficient and scalable posterior surrogate for seismic inversion via wavelet score-based generative models},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Bayesian inference, deep learning, generative model, IMAGE, Imaging, Inverse problems, power-scaling, RTM, score, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/cirakman2025IMAGEesp},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/cirakman2025IMAGEesp/IMAGE_Ege.pdf},
	author = {Ege Cirakman and Huseyin Tuna Erdinc 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 {zeng2025IMAGEfwi,
	title = {Full-waveform variational inference with common-image gathers},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Bayesian inference, CIG, deep learning, diffusion models, FWI, IMAGE, Imaging, Inverse problems, MVA, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/zeng2025IMAGEfwi/IMAGE_Yunlin.pdf},
	author = {Yunlin Zeng and Huseyin Tuna Erdinc and Rafael Orozco and Felix J. Herrmann}
}
@conference {orozco2025SIAMisu,
	title = {Industry-Scale Uncertainty-Aware Full Waveform Inference with Generative Models},
	booktitle = {SIAM Conference on Computational Science and Engineering (CSE25)},
	year = {2025},
	note = {(SIAM CSE25, Dallas)},
	month = {03},
	abstract = {In this talk, we address the challenge of Bayesian sampling for ill-posed inverse problems involving expensive PDE forward operators. To tackle this, we propose the use of generative models and discuss three key challenges along with our solutions. First, we address the constraints of GPU memory by introducing patch-based training and memory-efficient invertible networks, enabling the handling of large-scale data. Second, we demonstrate that these models can be effectively trained using only prior data available from acoustic observations and borehole well data, making the approach practical for real-world applications. Third, we explore various strategies to enhance the reliability of posterior samples by incorporating physics-based constraints through the wave operator. We conclude by presenting experiments that showcase the capabilities of these methods in field-data Full Waveform Inversion (FWI) workflows, scaling up to industry-scale problems with large 2D (512x7000 degrees of freedom) and 3D models.},
	keywords = {Bayesian sampling, FWI, Generative models, Inverse problems, Normalizing flows, SIAM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMCSE/2025/orozco2025SIAMisu},
	author = {Rafael Orozco and Huseyin Tuna Erdinc and Mathias Louboutin 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 {erdinc2025IMAGEpbi,
	title = {Power-scaled Bayesian inference with score-based generative models},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Bayesian inference, deep learning, generative model, IMAGE, Imaging, Inverse problems, power-scaling, RTM, score, SEG, Summary Statistics, Uncertainty quantification, WISE},
	doi = {10.48550/arXiv.2504.10807},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/erdinc2025IMAGEpbi},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/erdinc2025IMAGEpbi/IMAGE_Tuna.pdf},
	author = {Huseyin Tuna Erdinc and Yunlin Zeng and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {park2025IMAGErdn,
	title = {A reduced-order derivative-informed neural operator for subsurface fluid-flow},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {deep learning, derivatve-informed, Fisher-information, FNO, GCS, IMAGE, Inverse problems, permeability, reduced-order modeling, reservoir simulation, SEG, Uncertainty quantification},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/park2025IMAGErdn/IMAGE_Jayjay.pdf},
	author = {Jeongjin Park and Grant Bruer and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {herrmann2025EAGEssf,
	title = {SAGE {\textemdash} Subsurface foundational model with AI-driven Geostatistical Extraction},
	booktitle = {EAGE Workshop},
	year = {2025},
	month = {6},
	abstract = {In this talk, we introduce a novel diffusion-based generative modeling approach for synthesizing high-fidelity subsurface velocity models. Once trained, this foundational model serves as a prior or a generative tool, enabling the sampling of velocity models that closely match the statistical distribution of geologically relevant Earth models. To achieve this, we leverage geostatistical information derived from curated datasets provided by the UK National Data Repository. Our training set comprises check-shot-corrected migrated images intersected by well logs. Numerical experiments on multiple synthetic datasets, including the Compass model and Synthoseis, demonstrate the model{\textquoteright}s ability to generate realistic subsurface velocity models. This capability offers a data-driven prior for full-waveform inversion and facilitates the generation of training samples for amortized neural networks to improve full-waveform inference.},
	keywords = {Bayesian inference, deep learning, EAGE, foundation models, full waveform inference, generative model, Imaging, Inverse problems, RTM, score, Summary Statistics, UK-NDR, Uncertainty quantification},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2025/herrmann2025EAGEssf/EAGE_WS22_Foundation_Herrmann.mp4},
	author = {Huseyin Tuna Erdinc and Thales Souza 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}
}
@conference {gahlot2025IMAGEsrp,
	title = {Sensitivity-aware rock physics enhanced digital shadow for underground-energy storage monitoring},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, experimental design, GCS, IMAGE, Imaging, Inverse problems, permeability, reservoir simulation, rock physics, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE},
	doi = {10.48550/arXiv.2504.14405},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/gahlot2025IMAGEsrp},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/gahlot2025IMAGEsrp/IMAGE_Abhinav.pdf},
	author = {Abhinav Prakash Gahlot and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@conference {herrmann2025SciMLtfm,
	title = {Towards foundation models for subsurface priors and posteriors},
	booktitle = {Workshop on Scientific Machine Learning 2025, Austin, TX},
	year = {2025},
	month = {9},
	keywords = {Bayesian inference, deep learning, foundation models, full waveform inference, generative model, Imaging, Inverse problems, RTM, SciML, score, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SciML/2025/herrmann2025SciMLtfm},
	author = {Huseyin Tuna Erdinc and Thales Souza and Shiqin Zeng and Rafael Orozco 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}
}
@conference {herrmann2024PURDUEicon,
	title = {Digital Twins in the era of generative AI {\textemdash} Application to Geological CO2 Storage},
	booktitle = {ICON Seminar in IoT},
	year = {2024},
	month = {9},
	abstract = {As a society, we are faced with important challenges to combat climate change. Geological Carbon Storage, during which gigatonnes of super-critical CO2 are stored underground, is arguably the only scalable net-negative CO2-emission technology that is available. Recent advances in generative AI offer unique opportunities{\textemdash}especially in the context of Digital Twins for subsurface CO2-storage monitoring, decision making, and control{\textemdash}to help scale this technology, optimize its operations, lower its costs, and reduce its risks, so assurances can be made whether storage projects proceed as expected and whether CO2 remains underground. During this talk, it is shown how techniques from Simulation-Based Inference and Ensemble Bayesian Filtering can be extended to establish probabilistic baselines and assimilate multimodal data for problems challenged by large degrees of freedom, nonlinear multiphysics, and computationally expensive to evaluate simulations. Key concepts that will be reviewed include neural Wave-Based Inference with Amortized Uncertainty Quantification and physics-based Summary Statistics, Ensemble Bayesian Filtering with Conditional Neural Networks, and learned multiphysics inversion with Differentiable Programming.},
	keywords = {Bayesian inference, conditional normalizing flows, differential programming, digital twin, Ensemble Bayesian filtering, GCS, multiphyics, sequential Bayes, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/PURDUEicon/2024/herrmann2024PURDUEicon},
	url2 = {https://www.youtube.com/watch?v=GQT9CLA-DlU},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {gahlot2024MINESGIGACO2dtg,
	title = {DT4GCS {\textemdash} Digital Twin for Geological CO2 Storage and Control},
	booktitle = {Geophysical Research for Gigatonnes CO2 Storage, Colorado School of Mines},
	year = {2024},
	month = {7},
	keywords = {Bayesian inference, conditional normalizing flows, control, deep learning, digital twin, GCS, sequential Bayes, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/MINESGIGACO2/2024/gahlot2024MINESGIGACO2dtg},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/MINESGIGACO2/2024/gahlot2024MINESGIGACO2dtg/DTCCUS24.docx},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {erdinc2024IMAGEggm,
	title = {Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	abstract = {Diffusion generative models are powerful frameworks for learning high-dimensional distributions and synthesizing high-fidelity images. However, their efficacy in training predominantly hinges on the availability of complete, high-quality training datasets, a condition that often proves unattainable, particularly in the domain of subsurface velocity-model generation. In this work, we propose to synthesize proxy subsurface velocities from incomplete well and imaged seismic observations by introducing additional corruptions to the observations during the training phase. In this context, proxy velocity models refer to random realizations of subsurface velocities that are close in distribution to the actual subsurface velocities. These proxy models can be used as priors to train neural networks with simulation-based inference. Our approach facilitates the generation of these proxy velocity samples by utilizing available datasets composed merely of seismic images and 5 (for now) wells per seismic image.After training, our foundation generative model permits the generation of velocity samples derived from unseen RTMs without the need of having access to wells.},
	keywords = {deep learning, diffusion, generative modeling, geostatistics, IMAGE, kriging, SEG, velocity model building},
	url = {https://slimgroup.github.io/IMAGE2024/erdinc2024SEG/abstract.html},
	author = {Huseyin Tuna Erdinc and Rafael Orozco and Felix J. Herrmann}
}
@conference {herrmann2023CCUSdgs,
	title = {Derisking geological storage with simulation-based seismic monitoring design and machine learning},
	booktitle = {Carbon, Capture, Utilization, and Storage},
	year = {2023},
	note = {(CCUS, Houston)},
	month = {04},
	keywords = {CAM, CCS, CCUS, classification, explainability, JRM, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CCUS/2023/herrmann2023CCUSdgs/CCUS2023.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/CCUS/2023/herrmann2023CCUSdgs},
	author = {Felix J. Herrmann and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin}
}
@conference {erdinc2023IMAGEecl,
	title = {Enhancing CO2 Leakage Detectability via Dataset Augmentation},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Previous work 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 show that by augmenting the training set with various coarse receiver samplings and corresponding seismic images, we can improve the leakage detection capabilities and accuracy while increasing the robustness with respect to low-cost coarse receiver samplings, e.g. ocean bottom nodes (OBNs).},
	keywords = {CCS, classifier, deep learning, leakage detection, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/DetectabilityWithVision/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/erdinc2023IMAGEecl},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@conference {gahlot2023NIPSWSifp,
	title = {Inference of CO2 flow patterns {\textendash} a feasibility study},
	booktitle = {Neural Information Processing Systems (NeurIPS)},
	year = {2023},
	note = {(NeurIPS 2023 Workshop - Tackling Climate Change with Machine Learning (Spotlight))},
	month = {10},
	abstract = {As the global deployment of carbon capture and sequestration (CCS) technology intensifies in the fight against climate change, it becomes increasingly imperative to establish robust monitoring and detection mechanisms for potential underground CO$_2$ leakage, particularly through pre-existing or induced faults in the storage reservoir{\textquoteright}s seals. While techniques such as history matching and time-lapse seismic monitoring of CO$_2$ storage have been used successfully in tracking the evolution of CO$_2$ plumes in the subsurface, these methods lack principled approaches to characterize uncertainties related to the CO$_2$ plumes{\textquoteright} behavior. Inclusion of systematic assessment of uncertainties is essential for risk mitigation for the following reasons: (i) CO$_2$ plume-induced changes are small and seismic data is noisy; (ii) changes between regular and irregular (e.g., caused by leakage) flow patterns are small; and (iii) the reservoir properties that control the flow are strongly heterogeneous and typically only available as distributions. To arrive at a formulation capable of inferring flow patterns for regular and irregular flow from well and seismic data, the performance of conditional normalizing flow will be analyzed on a series of carefully designed numerical experiments. While the inferences presented are preliminary in the context of an early CO$_2$ leakage detection system, the results do indicate that inferences with conditional normalizing flows can produce high-fidelity estimates for CO$_2$ plumes with or without leakage. We are also confident that the inferred uncertainty is reasonable because it correlates well with the observed errors. This uncertainty stems from noise in the seismic data and from the lack of precise knowledge of the reservoir{\textquoteright}s fluid flow properties.},
	keywords = {Amortized Variational Inference, Bayesian inference, CCS, conditional normalizing flows, geological carbon storage, monitoring, NIPS, Summary Statistics},
	doi = {10.48550/arXiv.2311.00290},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2023/gahlot2023NIPSWSifp/paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2023/gahlot2023NIPSWSifp/poster.pdf},
	author = {Abhinav Prakash Gahlot and Huseyin Tuna Erdinc and Rafael Orozco and Ziyi Yin and Felix J. Herrmann}
}
@conference {herrmann2022EAGEcvm,
	title = {Capturing velocity-model uncertainty and two-phase flow with Fourier Neural Operators},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2022},
	note = {(EAGE, Madrid)},
	month = {06},
	pages = {AI in Geoscience and Geophysics: Current Trends and Future Prospects (Dedicated Session)},
	keywords = {CCS, EAGE, Fourier neural operators, JRM, seismic imaging, Uncertainty quantification},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/herrmann2022EAGEcvm/index.html},
	author = {Ali Siahkoohi and Thomas J. Grady II and Abhinav Prakash Gahlot and Huseyin Tuna Erdinc and Felix J. Herrmann}
}
@conference {erdinc2022AAAIdcc,
	title = {De-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images},
	booktitle = {AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges},
	year = {2022},
	note = {(AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges, Arlington)},
	month = {11},
	abstract = {With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO$_2$ leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO$_2$ storage has shown promising results in its ability to monitor the growth of the CO$_2$ plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO$_2$ concentration, additional developments are required to efficiently interpret the seismic images for leakage. In this work, we introduce a binary classification of time-lapse seismic images to delineate CO$_2$ plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO$_2$ plumes by leveraging Class Activation Mapping (CAM) methods.},
	keywords = {AAAI, CAM, CCS, classification, explainability, JRM, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/AAAI/2022/erdinc2022AAAIdcc/erdinc2022AAAIdcc.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AAAI/2022/erdinc2022AAAIdcc/index.html},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {sharan2018IEEEIUSspoa,
	title = {Sparsity-promoting photoacoustic imaging with source estimation},
	booktitle = {2018 IEEE International Ultrasonics Symposium (IUS)},
	year = {2018},
	note = {(IEEE IUS, Kobe)},
	month = {10},
	pages = {206-212},
	abstract = {Photoacoustics has emerged as a high-contrast imaging modality that provides optical absorption maps inside of tissues, therefore complementing morphological information of conventional ultrasound. The laser-generated photoacoustic waves are usually envelope-detected, thus disregarding the specific waveforms generated by each photoabsorber. Here we propose a sparsity-promoting image reconstruction method that allows the estimation of each photoabsorber{\textquoteright}s source-time function. Preliminary studies showed the ability to reconstruct the optical absorption map of an in silico vessel phantom. By using a sparsity-promoting imaging method, absorption maps and source-time functions can still be recovered even in situations where the number of transducers is decreased by a factor of six. Moreover, the recovery is able to attain higher resolution than conventional beamforming methods. Because our method recovers the source-time function of the absorbers, it could potentially also be used to distinguish different types of photoabsorbers, or the degree of aggregation of exogenous agents, under the assumption that these would generate different source-time functions at the moment of laser irradiation.},
	keywords = {Acceleration, estimation, IEEE, Image reconstruction, Phantoms, photoacoustic imaging, source-time function, sparsity promoting inversion, Transducers, wave equation},
	doi = {10.1109/ULTSYM.2018.8580037},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/IEEEIUS/2018/sharan2018IEEEIUSspoa/sharan2018IEEEIUSspoa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IEEEIUS/2018/sharan2018IEEEIUSspoa/sharan2018IEEEIUSspoa_pres.pdf},
	author = {Shashin Sharan and Rajiv Kumar and Diego S. Dumani and Mathias Louboutin and Rongrong Wang and Stanislav Emelianov and Felix J. Herrmann}
}
@conference {esser2015SEGasd,
	title = {Automatic salt delineation {\textendash}- Wavefield Reconstruction Inversion with convex constraints},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2015},
	note = {(SEG, New Orleans)},
	month = {10},
	pages = {1337-1343},
	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 = {convex constraints, Full-waveform inversion, salt, SEG, total-variation norm},
	doi = {10.1190/segam2015-5877995.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2015/esser2015SEGasd/esser2015SEGasd.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2015/esser2015SEGasd/esser2015SEGasd_pres.pdf},
	author = {Ernie Esser and Llu{\'\i}s Guasch and Tristan van Leeuwen and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@conference {esser2015SEGWSasf,
	title = {Automatic salt flooding with hinged FWI},
	booktitle = {SEG Workshop on Subsalt model buiding long to short wavelengths; New Orleans},
	year = {2015},
	note = {(SEG, New Orleans)},
	month = {10},
	keywords = {SEG, waveform inversion, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2015/esser2015SEGWSasf/esser2015SEGWSasf_pres.pdf},
	author = {Ernie Esser and Llu{\'\i}s Guasch and Felix J. Herrmann}
}
@conference {herrmann2015EAGEfom,
	title = {Fast "online" migration with Compressive Sensing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	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 = {EAGE, LSRTM},
	doi = {10.3997/2214-4609.201412942},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/herrmann2015EAGEfom/herrmann2015EAGEfom.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/herrmann2015EAGEfom/herrmann2015EAGEfom_pres.pdf},
	author = {Felix J. Herrmann and Ning Tu and Ernie Esser}
}
@conference {esser2015EAGElcs,
	title = {A lifted $\ell_1/\ell_2$ constraint for sparse blind deconvolution},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {We propose a modification to a sparsity constraint based on the ratio of $\ell_1$ and $\ell_2$ norms for solving blind seismic deconvolution problems in which the data consist of linear convolutions of different sparse reflectivities with the same source wavelet. We also extend the approach to the Estimation of Primaries by Sparse Inversion (EPSI) model, which includes surface related multiples. Minimizing the ratio of $\ell_1$ and $\ell_2$ 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 $\ell_1/\ell_2$ penalty. Lifted versions of $\ell_1/\ell_2$ constraints have also been proposed but are challenging to implement. Inspired by the lifting approach, we propose to split the sparse signals 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 without smoothing the $\ell_1$ or $\ell_2$ 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. Numerical experiments demonstrate robustness to the initialization as well as to noise in the data.},
	keywords = {blind deconvolution, EAGE, EPSI},
	doi = {10.3997/2214-4609.201413420},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/esser2015EAGElcs/esser2015EAGElcs.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/esser2015EAGElcs/esser2015EAGElcs_poster.pdf},
	author = {Ernie Esser and Tim T.Y. Lin and Rongrong Wang and Felix J. Herrmann}
}
@conference {kumar2015EAGEmcu,
	title = {Matrix completion on unstructured grids : 2-D seismic data regularization and interpolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {Seismic data interpolation via rank-minimization techniques has been recently introduced in the seismic community. All the existing rank-minimization techniques assume the recording locations to be on a regular grid, e.g., sampled periodically, but seismic data are typically irregularly sampled along spatial axes. Other than the irregularity of the sampled grid, we often have missing data. In this paper, we study the effect of grid irregularity to conduct matrix completion on a regular grid for unstructured data. We propose an improvement of existing rank-minimization techniques to do regularization. We also demonstrate that we can perform seismic data regularization and interpolation simultaneously. We illustrate the advantages of the modification using a real seismic line from the Gulf of Suez to obtain high quality results for regularization and interpolation, a key application in exploration geophysics.},
	keywords = {EAGE, interpolation, matrix completion, NFFT, regularization},
	doi = {10.3997/2214-4609.201413448},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/kumar2015EAGEmcu/kumar2015EAGEmcu.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/kumar2015EAGEmcu/kumar2015EAGEmcu_poster.pdf},
	author = {Rajiv Kumar and Oscar Lopez and Ernie Esser and Felix J. Herrmann}
}
@conference {kumar2015IIPFWIrmb,
	title = {Rank minimization based seismic data processing and inversion},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {We present the low-rank extensions of full-waveform inversion to mitigate the local-minima issues. The purpose formulation is computationally tractable and does not involve any extra computational cost compared to the adjoint-state method.},
	keywords = {FWI, inversion, Processing, rank minimization},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/kumar2015IIPFWIrmb/kumar2015IIPFWIrmb_pres.pdf},
	author = {Rajiv Kumar and Curt Da Silva and Oscar Lopez and Aleksandr Y. Aravkin and Hassan Mansour and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@conference {esser2015CAMSAPrsa,
	title = {Resolving scaling ambiguities with the $\ell_1/\ell_2$ norm in a blind deconvolution problem with feedback},
	booktitle = {IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing},
	year = {2015},
	note = {(IEEE CAMSAP Workshop, Canc{\'u}n, Mexico)},
	month = {12},
	pages = {365-368},
	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 $\ell_1$ constraints. However, when paired with an $\ell_1/\ell_2$ 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 = {$\ell_1/\ell_2$, blind deconvolution, CAMSAP, lifting, method of multipliers},
	doi = {10.1109/CAMSAP.2015.7383812},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CAMSAP/2015/esser2015CAMSAPrsa/esser2015CAMSAPrsa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CAMSAP/2015/esser2015CAMSAPrsa/esser2015CAMSAPrsa_poster.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/CAMSAP/2015/esser2015CAMSAPrsa/esser2015CAMSAPrsa_ext.pdf},
	author = {Ernie Esser and Rongrong Wang and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {witte2015IIPFWIspl,
	title = {Sparsity-promoting least-square migration with linearized Bregman and compressive sensing},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	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 = {Bregman, compressed sensing, FWI, migration, sparsity promotion, time domain},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/witte2015IIPFWIspl/witte2015IIPFWIspl_pres.pdf},
	author = {Philipp A. Witte and Ning Tu and Ernie Esser and Mengmeng Yang and Mathias Louboutin and Felix J. Herrmann}
}
@conference {oghenekohwo2015IIPFWItlf,
	title = {Time-lapse FWI with distributed compressed sensing},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {In this talk, I will discuss our most recent application of full-waveform inversion (FWI) to time-lapse seismic data inversion. Specifically, I will illustrate how a joint recovery model (JRM) can be used in a joint inversion framework, leveraging ideas from distributed compressed sensing (DCS). The key idea involves exploiting the common information in the baseline and monitor gradients during the inversion.},
	keywords = {distributed compressed sensing, FWI, time lapse},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/oghenekohwo2015IIPFWItlf/oghenekohwo2015IIPFWItlf_pres.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@conference {oghenekohwo2015EAGEuci,
	title = {Using common information in compressive time-lapse full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {The use of time-lapse seismic data to monitor changes in the subsurface has become standard practice in industry. In addition, full-waveform inversion has also been extended to time-lapse seismic to obtain useful time-lapse information. The computational cost of this method are becoming more pronounced as the volume of data increases. Therefore, it is necessary to develop fast inversion algorithms that can also give improved time-lapse results. Rather than following existing joint inversion algorithms, we are motivated by a joint recovery model which exploits the common information among the baseline and monitor data. We propose a joint inversion framework, leveraging ideas from distributed compressive sensing and the modified Gauss-Newton method for full-waveform inversion, by using the shared information in the time-lapse data. Our results on a realistic synthetic example highlight the benefits of our joint inversion approach over a parallel inversion method that does not exploit the shared information. Preliminary results also indicate that our formulation can address time-lapse data with inconsistent acquisition geometries.},
	keywords = {EAGE, FWI, time-lapse},
	doi = {10.3997/2214-4609.201413086},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/oghenekohwo2015EAGEuci/oghenekohwo2015EAGEuci.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/oghenekohwo2015EAGEuci/oghenekohwo2015EAGEuci_poster.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@conference {herrmann2015IIPFWIwcc,
	title = {Wavefield Reconstruction Inversion with convex constraints},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {During this talk, we discuss how to exploit the special structure of Wavefield Reconstruction Inversion (WRI) to include convex bound and total-variation constraints in a computationally feasible manner. The resulting method shows promising results on challenging models that include high-velocity high-contrast inclusions such as salt or basalt. This is joint work with Ernie Esser who passed away and who is dearly missed.},
	keywords = {convex, FWI, regularization},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/herrmann2015IIPFWIwcc/herrmann2015IIPFWIwcc_pres.pdf},
	author = {Felix J. Herrmann and Ernie Esser and Llu{\'\i}s Guasch}
}
@conference {esser2014EAGEacp,
	title = {Application of a convex phase retrieval method to blind seismic deconvolution},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {A classical strategy for blind seismic deconvolution is to first estimate the autocorrelation of the unknown source wavelet from the data and then recover the wavelet by assuming it has minimum phase. However, computing the minimum phase wavelet directly from the amplitude spectrum can be sensitive to even extremely small errors, especially in the coefficients close to zero. Since the minimum phase requirement follows from an assumption that the wavelet should be as impulsive as possible, we propose to directly estimate an impulsive wavelet by minimizing a weighted l2 penalty subject to a constraint on its amplitude spectrum. This nonconvex model has the form of a phase retrieval problem, in this case recovering a signal given only estimates of the magnitudes of its Fourier coefficients. Following recent work on convex relaxations of phase retrieval problems, we propose a convex semidefinite program for computing an impulsive minimum phase wavelet whose amplitude spectrum is close to a given estimate, and we show that this can be robustly solved by a Douglas Rachford splitting method for convex optimization.},
	keywords = {blind deconvolution, convex phase retrieval, EAGE, source wavelet estimation},
	doi = {10.3997/2214-4609.20141590},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/esser2014EAGEacp/esser2014EAGEacp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/esser2014EAGEacp/esser2014EAGEacp_poster.pdf},
	author = {Ernie Esser and Felix J. Herrmann}
}
@conference {esser2014SIAMISsdc,
	title = {Solving DC programs that promote group 1-sparsity},
	booktitle = {SIAM Conference on Imaging Science},
	year = {2014},
	note = {(SIAM Conference on Imaging Science)},
	month = {05},
	abstract = {Many interesting applications require solving nonconvex problems that would be convex if not for a group 1-sparsity constraint. Splitting methods that are effective for convex problems can still work well in this setting. We propose several nonconvex penalties that can be used to promote group 1-sparsity in the framework of difference of convex or primal dual hybrid gradient (PDHG) methods. Applications to nonlocal inpainting, linear unmixing and phase unwrapping are demonstrated.},
	keywords = {difference of convex, group 1-sparsity, nonconvex PDHG, operator splitting, phase unwrapping},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2014/esser2014SIAMISsdc_pres.pdf},
	author = {Ernie Esser}
}
@conference {kumar2014EAGErank,
	title = {SVD-free low-rank matrix factorization : wavefield reconstruction via jittered subsampling and reciprocity},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {Recently computationally efficient rank optimization techniques have been studied extensively to develop a new mathematical tool for the seismic data interpolation. So far, matrix completion problems have been discussed where sources are subsample according to a discrete uniform distribution. In this paper, we studied the effect of two different subsampling techniques on seismic data interpolation using rank-regularized formulations, namely jittered subsampling over uniform random subsampling. The other objective of this paper is to combine the fact of source-receiver reciprocity with the rank-minimization techniques to enhance the accuracy of missing-trace interpolation. We illustrate the advantages of jittered subsampling and reciprocity using a seismic line from Gulf of Suez to obtain high quality results for interpolation, a key application in exploration geophysics.},
	keywords = {EAGE, interpolation, low-rank, reciprocity},
	doi = {10.3997/2214-4609.20141394},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGErank/kumar2014EAGErank.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGErank/kumar2014EAGErank_pres.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Ernie Esser and Hassan Mansour and Felix J. Herrmann}
}
@conference {oghenekohwo2014EAGEtls,
	title = {Time-lapse seismic without repetition: reaping the benefits from randomized sampling and joint recovery},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In the current paradigm of 4-D seismic, guaranteeing repeatability in acquisition and processing of the baseline and monitor surveys ranks highest amongst the technical challenges one faces in detecting time-lapse signals. By using recent insights from the field of compressive sensing, we show that the condition of survey repeatability can be relaxed as long as we carry out a sparsitypromoting program that exploits shared information between the baseline and monitor surveys. By inverting for the baseline and monitor survey as the common "background", we are able to compute high-fidelity 4-D differences from carefully selected synthetic surveys that have different sets of source/receivers missing. This synthetic example is proof of concept of an exciting new approach to randomized 4-D acquisition where time-lapse signal can be computed as long as the survey details, such as source/receiver locations are known afterwards.},
	keywords = {4-D seismic, EAGE, joint recovery, time-lapse},
	doi = {10.3997/2214-4609.20141478},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/oghenekohwo2014EAGEtls/oghenekohwo2014EAGEtls.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/oghenekohwo2014EAGEtls/oghenekohwo2014EAGEtls_pres.pdf},
	author = {Felix Oghenekohwo and Ernie Esser and Felix J. Herrmann}
}
@conference {herrmann2014SEGWS,
	title = {Wavefield Reconstruction Inversion (WRI) {\textendash} a new take on wave-equation based inversion},
	booktitle = {SEG Workshop on Full Waveform Inversion - Elastic Approaches and Issues with Anisotropy, Nonshallow Inversion, Poor Starting Model; Denver},
	year = {2014},
	note = {(SEG Workshop, Denver)},
	month = {10},
	keywords = {FWI, SEG, workshop, WRI},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/herrmann2014SEGWSwri/herrmann2014SEGWSwri_pres.pdf},
	author = {Felix J. Herrmann and Ernie Esser and Tristan van Leeuwen and Bas Peters}
}
@conference {herrmann2009IAPcsisa,
	title = {Compressive seismic imaging with simultaneous acquisition},
	booktitle = {IAP},
	year = {2009},
	note = {Presented at the IAP meeting, Vienna},
	abstract = {The shear size of seismic data volumes forms one of the major impediments for the inversion of seismic data. Turning forward modeling and inversion into a compressive sensing (CS) problem - where simulated data are recovered from a relatively small number of independent sources - can effectively mitigate this high-cost impediment. Our key contribution lies in the design of a sub-sampling operator that commutes with the time-harmonic Helmholtz system. As in compressive sensing, this leads to a reduction of simulation cost. This reduction is commensurate with the transform-domain sparsity of the solution., implying that computational costs are no longer determined by the size of the discretization but by transform-domain sparsity of the solution of the CS problem that recovers the data. The combination of this sub-sampling strategy with our recent work on preconditioned implicit solvers for the time-harmonic Helmholtz equation provides a viable alternative to full-waveform inversion schemes based on explicit time-domain finite-difference methods.},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AIP/2009/herrmann2009IAPcsisa/Herrmann09AIP1.pdf},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin}
}
@conference {herrmann2009EAGEcsa,
	title = {Compressive sensing applied to full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2009},
	month = {06},
	abstract = {With the recent resurgence of full-waveform inversion, the computational cost of solving forward modeling problems has become{\textendash}-aside from issues with non-uniqueness{\textendash}-one of the major impediments withstanding successful application of this technology to industry-size data volumes. To overcome this impediment, we argue that further improvements in this area will depend on a problem formulation with a computational complexity that is no longer strictly determined by the size of the discretization but by transform-domain sparsity of its solution. In this new paradigm, we bring computational costs in par with our ability to compress seismic data and images. This premise is related to two recent developments. First, there is the new field of compressive sensing (CS in short throughout the paper, Cand{\textquoteleft}es et al., 2006; Donoho, 2006){\textendash}-where the argument is made, and rigorously proven, that compressible signals can be recovered from severely sub-Nyquist sampling by solving a sparsity promoting program. Second, there is in the seismic community the recent resurgence of simultaneous-source acquisition (Beasley, 2008; Krohn and Neelamani, 2008; Herrmann et al., 2009; Berkhout, 2008; Neelamani et al., 2008), and continuing efforts to reduce the cost of seismic modeling, imaging, and inversion through phase encoding of simultaneous sources (Morton and Ober, 1998; Romero et al., 2000; Krohn and Neelamani, 2008; Herrmann et al., 2009), removal of subsets of angular frequencies (Sirgue and Pratt, 2004; Mulder and Plessix, 2004; Lin et al., 2008) or plane waves (Vigh and Starr, 2008). By using CS principles, we remove sub-sampling interferences asocciated with these approaches through a combination of exploiting transform-domain sparsity, properties of certain sub-sampling schemes, and the existence of sparsity promoting solvers.},
	keywords = {EAGE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/Herrmann09EAGEcsa/Herrmann09EAGEcsa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/Herrmann09EAGEcsa/Herrmann09EAGEcsa_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=23961},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin}
}
@conference {lin2009SEGcsf,
	title = {Compressive simultaneous full-waveform simulation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {2577-2581},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The fact that the 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 a significant cost reduction, 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-difference.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255381},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/Lin09SEGcsf/Lin09SEGcsf.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/Lin09SEGcsf/Lin09SEGcsf_pres.pdf},
	author = {Tim T.Y. Lin and Yogi A. Erlangga and Felix J. Herrmann}
}
@conference {herrmann2009SAMPTAcws,
	title = {Compressive-wavefield simulations},
	booktitle = {SAMPTA},
	year = {2009},
	publisher = {SAMPTA},
	organization = {SAMPTA},
	abstract = {Full-waveform inversion{\textquoteright}s high demand on computational resources forms, along with the non-uniqueness problem, the major impediment withstanding its widespread use on industrial-size datasets. Turning modeling and inversion into a compressive sensing problem{\textendash}-where simulated data are recovered from a relatively small number of independent simultaneous sources{\textendash}-can effectively mitigate this high-cost impediment. The key is in showing that we can design a sub-sampling operator that commutes with the time-harmonic Helmholtz system. As in compressive sensing, this leads to a reduction in simulation cost. Moreover, this reduction is commensurate with the transform-domain sparsity of the solution, implying that computational costs are no longer determined by the size of the discretization but by transform-domain sparsity of the solution of the CS problem which forms our data. The combination of this sub-sampling strategy with our recent work on implicit solvers for the Helmholtz equation provides a viable alternative to full-waveform inversion schemes based on explicit finite-difference methods.},
	keywords = {SAMPTA},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SAMPTA/2009/Herrmann09SAMPTAcws/Herrmann09SAMPTAcws.pdf},
	author = {Felix J. Herrmann and Yogi A. Erlangga and Tim T.Y. Lin}
}
@conference {erlangga2009EAGEmwi,
	title = {Migration with implicit solvers for the time-harmonic Helmholtz equation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2009},
	month = {06},
	abstract = {From the measured seismic data, the location and the amplitude of reflectors can be determined via a migration algorithm. Classically, following Claerbout{\textquoteright}s imaging principle [2], a reflector is located at the position where the source{\textquoteright}s forward-propagated wavefield correlates with the backward-propagated wavefield of the receiver data. Lailly and Tarantola later showed that this imaging principle is an instance of inverse problems, with the associated migration operator formulated via a least-squares functional; see [6, 12, 13]. Furthermore, they showed that the migrated image is associated with the gradient of this functional with respect to the image. If the solution of the least-squares functional is done iteratively, the correlation-based image coincides up to a constant with the first iteration of a gradient method. In practice, this migration is done either in the time domain or in the frequency domain. In the frequency-domain migration, the main bottleneck thus far, which renders its full implementation to large scale problems, is the lack of efficient solvers for computing wavefields. Robust direct methods easily run into excessive memory requirements as the size of the problem increases. On the other hand, iterative methods, which are less demanding in terms of memory, suffered from lack of convergence. During the past years, however, progress has been made in the development of an efficient iterative method [4, 3] for the frequency-domain wavefield computations. In this paper, we will show the significance of this method (called MKMG) in the context of the frequency-domain migration, where multi-shot-frequency wavefields (of order of 10,000 related wavefields) need to be computed.},
	keywords = {EAGE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/Erlangga09EAGEmwi/Erlangga09EAGEmwi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/Erlangga09EAGEmwi/Erlangga09EAGEmwi_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=23955},
	author = {Yogi A. Erlangga and Felix J. Herrmann}
}
@conference {lin2009DELPHIrwi,
	title = {Randomized wavefield inversion},
	booktitle = {DELPHI},
	year = {2009},
	note = {Presented at the DELPHI meeting. The Hague},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Delphi/2009/lin2009DELPHIrwi/lin2009DELPHIrwi.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann and Yogi A. Erlangga}
}
@conference {erlangga2009SEGswi,
	title = {Seismic waveform inversion with Gauss-Newton-Krylov method},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {2357-2361},
	publisher = {SEG},
	organization = {SEG},
	abstract = {This abstract discusses an implicit implementation of the Gauss-Newton method, used for the frequency-domain full-waveform inversion, where the inverse of the Hessian for the update is never formed explicitly. Instead, the inverse of the Hessian is computed approximately by a conjugate gradient (CG) method, which only requires the action of the Hessian on the CG search direction. This procedure avoids an excessive computer storage, usually needed for storing the Hessian, at the expense of extra computational work in CG. An effective preconditioner for the Hessian is important to improve the convergence of CG, and hence to reduce the overall computational work.},
	keywords = {SEG},
	doi = {10.1190/1.3255332},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/erlangga09SEGswi/erlangga09SEGswi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/erlangga09SEGswi/erlangga09SEGswi_pres.pdf},
	author = {Yogi A. Erlangga and 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 {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}
}
