@unpublished {deng2026IMAGEbag,
	title = {Bridging the Acoustic-Elastic Gap in Seismic Inversion via Robust Summary Statistics},
	year = {2026},
	month = {3},
	abstract = {Simulation-based seismic inversion provides a principled framework for uncertainty quantification, but its performance degrades when the physics used in simulation differ from those of observed data. In practice, seismic inversion is often misspecified because the true physics are only partially known or too costly to model at scale, making field observations out of distribution relative to the simulations used for training; here, we study this issue through the acoustic-elastic gap. We introduce ElasNet, a misspecification-robust probabilistic inversion framework that bridges acoustic simulations and elastic observations through physics-informed summary statistics. We use acoustically imaged common-image gathers (CIGs) as structured summaries of seismic data, compress them with an attention-based summary network, and use maximum mean discrepancy (MMD) to align acoustic and elastic feature distributions during training while learning a conditional normalizing flow for posterior estimation with only acoustic data. Experiments on 2D Compass models show that the proposed approach reduces acoustic-elastic discrepancies in inferred velocity models and improves structural consistency and uncertainty calibration. These results demonstrate that robust summary statistics and distribution matching provide a scalable pathway for simulation-based seismic inversion under realistic physics mismatch.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, IMAGE, Imaging, Inverse problems, mmd, SEG, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/deng2026IMAGEbag/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/deng2026IMAGEbag/IMAGE_Venice.pdf},
	author = {Zijun Deng and Abhinav Prakash Gahlot and Shiqin Zeng and Felix J. Herrmann}
}
@unpublished {gahlot2026IMAGEirs,
	title = {Improving reservoir state prediction in digital shadows using Radon-transformed subsurface common-offset gathers},
	year = {2026},
	month = {3},
	abstract = {Digital shadows for subsurface monitoring use simulation-based inference to map seismic and well-log observations to evolving reservoir states such as CO 2  saturation and pressure. When the simulation forward models rely on simplified or misspecified rock physics, predictions become biased and uncertainty quantification degrades. We address this by combining rock-physics-aware amortized Bayesian inference with inference in the Radon domain of common-image gathers (CIGs). Angle-dependent information from Radon-transformed subsurface offset gathers improves separation of fluid and stress effects: fluids exhibit distinct amplitude-variation-with-angle (AVA) signatures, so conditioning the digital shadow on angle gathers yields more accurate and physically consistent joint estimates of saturation and pressure than conditioning on migrated stacks or offset-domain gathers. We train a context-aware amortized posterior estimator that conditions on seismic data and rock-physics context, enabling robustness to model misspecification and structured what-if analysis at inference time without retraining. On a synthetic 2D geological carbon storage scenario, conditioning on Radon-transformed CIGs improves joint saturation and pressure prediction relative to reverse-time migrated images, while context conditioning preserves interpretability and calibration under rock physics uncertainty.},
	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},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/gahlot2026IMAGEirs/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/gahlot2026IMAGEirs/IMAGE_Abhinav.pdf},
	author = {Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@unpublished {li2026IMAGEsca,
	title = {JUDIAgent: a scientific coding agent for JUDI workflows in wave-equation imaging},
	year = {2026},
	month = {3},
	abstract = {We present JUDIAgent, a scientific coding assistant for JUDI.jl, an open-source Julia framework for wave-equation-based seismic modeling, imaging, and inversion. In seismic experimentation, the practical difficulty is not only writing executable code, but assembling a complete seismic workflow with the required physical model, acquisition geometry, modeling or migration operator, and saved outputs needed for inspection. JUDIAgent addresses this problem by retrieving JUDI examples, generating Julia code, running the code in the target environment, and checking whether the generated script contains the requested workflow pieces and outputs, such as acquisition geometry and saved figures. We demonstrate the system with validated 2D forward-modeling and reverse-time migration (RTM) examples. These case studies show that, for JUDI-based seismic scripts, domain-aware validation can turn a lightweight user request into a more complete experimental workflow by detecting missing workflow steps that are not captured by runtime correctness alone and by making the resulting scripts easier to inspect and revise.},
	keywords = {Agentic AI, devito, FWI, IMAGE, Imaging, JUDI, RTM, scientific coding agent, SEG},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2026/li2026IMAGEsca/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/li2026IMAGEsca/IMAGE_Haoyun.pdf},
	author = {Haoyun Li and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@unpublished {zeng2026IMAGEssn,
	title = {Self-attention summary networks for subsurface velocity-model building from common-image gathers},
	year = {2026},
	month = {3},
	abstract = {Common-image gathers (CIGs) contain physically meaningful information about velocity-model errors through reflector focusing and residual moveout, but in conventional imaging workflows they are typically used only as diagnostic tools. In this work, we propose a multiscale self-attention summary network that maps high-dimensional 3D CIG volumes into compact conditioning embeddings for probabilistic subsurface velocity inversion. These learned embeddings preserve offset-dependent kinematic structure and spatial coherence while reducing variability caused by background-velocity mismatch. Conditioned on these summary embeddings, a flow-matching model learns a transport from a Gaussian source distribution to the posterior distribution of plausible velocity fields. Numerical experiments show that, compared with direct conditioning on raw CIGs, the proposed summary network improves posterior velocity inference. In particular, the multiscale attention design provides greater robustness to background-model mismatch, yielding more accurate posterior reconstructions and lower predictive uncertainty.},
	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/Submitted/2026/zeng2026IMAGEssn/abstract.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Submitted/2026/zeng2026IMAGEssn/IMAGE_Shiqin.pdf},
	author = {Shiqin Zeng and Yunlin Zeng and Abhinav Prakash Gahlot and Zijun Deng and Felix J. Herrmann}
}
@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 {li2025IMAGEceo,
	title = {CO2 Enhanced Oil Recovery Through Digital Shadow Modeling},
	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, EOR, experimental design, FWI, GCS, IMAGE, Imaging, Inverse problems, permeability, reservoir simulation, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/li2025IMAGEceo},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/li2025IMAGEceo/IMAGE_Haoyun.pdf},
	author = {Haoyun Li and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {herrmann2025SEGSPEcua,
	title = {Context- and uncertainty-aware Digital Twins for Integrated Reservoir Management},
	booktitle = {SEG/SPE 2nd Integrated Reservoir Characterization, Surveillance, and Management Workshop: Advances and Challenges},
	year = {2025},
	month = {7},
	abstract = {We present a new framework for Digital Twins (DTs) that supports uncertainty-aware monitoring, optimization, and risk management of underground storage operations, with a particular focus on Geological Carbon Storage (GCS) and Enhanced Oil Recovery (EOR). The proposed DT addresses key challenges in reservoir characterization, surveillance, and management by integrating generative AI methods with simulation-based inference, enabling scalable, probabilistic decision support under uncertainty. Our approach combines sensitivity-aware amortized Bayesian inference (SA-ABI) with ensemble-based data assimilation to efficiently incorporate multimodal time-lapse observations, such as seismic and well data, into dynamic reservoir models. By embedding context-awareness into neural networks, we account for variability in rock-physics relationships linking pressure, saturation, and seismic responses, and quantify uncertainties arising from model misspecifications{\textendash}-e.g. usage of patchy versus uniform saturation models. Computational efficiency is maintained through weight sharing across the network trained on different rock-physics scenarios, enabling rapid evaluation of the impact changing the underlying rock-physics model. The DT is built on industry-strength open-source software: {\textquoteleft}JutulDarcy.jl{\textquoteleft} for fully coupled multiphase flow simulations and {\textquoteleft}JUDI.jl{\textquoteleft}, a seismic inversion package leveraging the {\textquoteleft}Devito{\textquoteleft} finite-difference framework. This computational foundation allows scalable integration of physics-based simulations and data assimilation across large reservoir models, while retaining flexibility to adapt to varying physical contexts such as the inclusion of pressure-dependence of the compressional wavespeed. The resulting DT not only delivers posterior distributions for the reservoir{\textquoteright}s state (pressure/saturation) conditioned on available observations but also enables rapid {\textquotedblleft}what-if{\textquotedblright} scenario analysis by drawing posterior samples across a range of physical contexts without retraining. This capability supports proactive storage management, optimization of injection operations, and improved assurance of storage conformance.},
	keywords = {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, SEG, SPE, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEGSPE/2025/herrmann2025SEGSPEcua},
	author = {Abhinav Prakash Gahlot and Ipsita Bhar and Haoyun Li 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 {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 {deng2025IMAGEtfi,
	title = {Time-lapse full-waveform inversion with uncertainty quantification},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, GCS, igm, IMAGE, image generative model, Imaging, Inverse problems, JRM, SEG, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/deng2025IMAGEtfi},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/deng2025IMAGEtfi/IMAGE_Venice.pdf},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {zeng2025IMAGEw2f,
	title = {Well2Flow: Reconstruction of reservoir states from sparse wells using score-based generative models},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {deep learning, diffusion-pde models, Generative models, history matching, IMAGE, reservoir simulation, score, SEG, sparse observations, Uncertainty quantification},
	doi = {10.48550/arXiv.2504.06305},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/zeng2025IMAGEw2f},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/zeng2025IMAGEw2f/IMAGE_Shiqin.pdf},
	author = {Shiqin Zeng and Haoyun Li and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {orozco2024IMAGEbeacon,
	title = {BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows - a case study in optimal monitor well placement for CO2 sequestration},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	abstract = {CO2 sequestration is a crucial engineering solution for mitigating climate change. However, the uncertain nature of reservoir properties, necessitates rigorous monitoring of CO2 plumes to prevent risks such as leakage, induced seismicity, or breaching licensed boundaries. To address this, project managers use borehole wells for direct CO2 and pressure monitoring at specific locations. Given the high costs associated with drilling, it is crucial to strategically place a limited number of wells to ensure maximally effective monitoring within budgetary constraints. Our approach for selecting well locations integrates fluid-flow solvers for forecasting plume trajectories with generative neural networks for plume inference uncertainty. Our methodology is extensible to three-dimensional domains and is developed within a Bayesian framework for optimal experimental design, ensuring scalability and mathematical optimality. We use a realistic case study to verify these claims by demonstrating our method{\textquoteright}s application in a large scale domains and optimal performance as compared to baseline well placement.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, deep learning, GCS, IMAGE, Inverse problems, OED, SEG, Uncertainty quantification},
	doi = {10.48550/arXiv.2404.00075},
	author = {Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {gahlot2024IMAGEdt,
	title = {A Digital Twin for Geological Carbon Storage with Controlled Injectivity},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	abstract = {We present an uncertainty-aware Digital Twin (DT) for geologic carbon storage (GCS), capable of handling multimodal time-lapse data and controlling CO2 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 CO2 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., CO2 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 CO2 storage operations.},
	keywords = {Bayesian inference, conditional normalizing flows, control, deep learning, digital twin, GCS, IMAGE, SEG, sequential Bayes, Uncertainty quantification},
	doi = {10.48550/arXiv.2403.19819},
	url = {https://slimgroup.github.io/IMAGE2024/GahlotLi2024SEG/paper.html},
	author = {Abhinav Prakash Gahlot and Haoyun Li and Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@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 {yu2023IMAGEmsc,
	title = {Monitoring Subsurface CO2 Plumes with Sequential Bayesian Inference},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {To monitor and predict CO2 plume dynamics during geological carbon storage, reservoir engineers usually perform two-phase flow simulations. While these simulations may provide useful insights, their usefulness is limited due to numerous complicating factors including uncertainty in the dynamics of the plume itself. To study this phenomenon, we consider stochasticity in the dynamic caused by unknown random changes in the injection rate. By conditioning the CO2 plume predictions on seismic observations, we correct the CO2 plume predictions and quantify uncertainty with machine learning.},
	keywords = {Bayesian inference, CCS, deep learning, Imaging, monitoring, SEG, Uncertainty quantification},
	url = {https://slimgroup.github.io/IMAGE2023/SequentialBayes/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/yu2023IMAGEmsc},
	author = {Ting-ying Yu and Abhinav Prakash Gahlot and Rafael Orozco and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {gahlot2023IMAGEtsm,
	title = {Time-lapse seismic monitoring of geological carbon storage with the nonlinear joint recovery model},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {During time-lapse seismic monitoring, weak 4D signal below the level of inversion or migration artifacts poses challenges. To address these, low-cost randomized non-replicated acquisitions and linear joint recovery model (JRM) have been introduced to take 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 seldomly linear, we propose a more versatile nonlinear JRM, which extends linear JRM to nonlinear forward modeling.},
	keywords = {CCS, JRM, monitoring, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/NonLinear-JRM/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/gahlot2023IMAGEtsm},
	author = {Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@conference {yang2019SEGlrr,
	title = {Low-rank representation of subsurface extended image volumes with power iterations},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {4470-4474},
	abstract = {Full subsurface extended image volumes (EIVs) contain abundant information such as subsurface image gathers used in imaging, interpretation of rock properties or velocity analysis, but are expensive in term of computational cost and storage requirement due to their large size. Yet, due to their redundant information, monochromatic EIVs exhibit a low-rank structure that allows us to get a good approximation at a computational cost proportional to the rank k , while conventional techniques require at least the number of sources. Such low-rank factorized EIVs are computed thanks to the randomized singular values decomposition (SVD) algorithm. However, recent developments on low-rank approximations of EIVs raise two major questions. First, monochromatic EIVs rely on time-harmonic wave equation solvers, which do not scale well to realistic 3D models. Second, the rank of the monochromatic EIVs increases with the frequency, which yields increasing computational costs and storage. Here, we propose a construction approach based on a time-domain finite-difference wave-equation solver with time stepping, which is combined to power iteration schemes in the randomized SVD algorithm to accelerate the decay of the singular values. We compare the performances of simultaneous iterations, block-Krylov iterations and the original randomized SVD on the computation of the EIV for a small section of the Marmousi model. Then, we show further results on the full Marmousi model to validate our approach.},
	keywords = {full extended image volumes, power iterations, randomized SVD, SEG, time domain},
	doi = {10.1190/segam2019-3215961.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/yang2019SEGlrr/yang2019SEGlrr.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/yang2019SEGlrr/yang2019SEGlrr_pres.pdf},
	author = {Mengmeng Yang and Marie Graff and Rajiv Kumar and Felix J. Herrmann}
}
@conference {kumar2018SEGlreiv,
	title = {Low-rank representation of extended image volumes{\textendash}{\textendash}applications to imaging and velocity continuation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {4418-4422},
	abstract = {In this paper, we present a cost-effective low-rank factorized form of computing the full subsurface extended image volumes without explicitly calculating the receiver wavefields. The propose approach is computationally feasible, which exploits the low-rank structure of full subsurface extended image volumes organized as a matrix, thus avoiding the customary loop over sources. Using carefully selected stylized examples, we show how conventional migration images can be extracted from the low-rank factorized form. We also present a velocity continuation procedure that uses the same low-rank form and allows us to map the extended image for one velocity model to another velocity model without recomputing all the source wavefields.},
	keywords = {image volume, low-rank, SEG, velocity continuation},
	doi = {10.1190/segam2018-2998404.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/kumar2018SEGlreiv/kumar2018SEGlreiv.html},
	author = {Rajiv Kumar and Marie Graff-Kray and Tristan van Leeuwen 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 {ghadermarzy2014SEGsti,
	title = {Seismic trace interpolation with approximate message passing},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {3621-3626},
	abstract = {Approximate message passing (AMP) is a computationally effective algorithm for recovering high dimensional signals from a few compressed measurements. In this paper we use AMP to solve the seismic trace interpolation problem. We also show that we can exploit the fast AMP algorithm to improve the recovery results of seismic trace interpolation in curvelet domain, both in terms of convergence speed and recovery performance by using AMP in Fourier domain as a preprocessor for the L1 recovery in Curvelet domain.},
	keywords = {AMP, interpolation, SEG},
	doi = {http://dx.doi.org/10.1190/segam2014-0577.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/ghadermarzy2014SEGsti/ghadermarzy2014SEGsti.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/ghadermarzy2014SEGsti/ghadermarzy2014SEGsti_pres.pdf},
	author = {Navid Ghadermarzy and Ozgur Yilmaz and Felix J. Herrmann}
}
