@conference {herrmann2025IMSIafi,
	title = {Amortized Full-Waveform Inference w/ Learned Summary Statistics},
	booktitle = {Reduced Order and Surrogate Modeling for Digital Twins, IMSI, Chicago},
	year = {2025},
	month = {11},
	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/IMSI/2025/herrmann2025IMSIafi},
	author = {Rafael Orozco and Ziyi Yin and Shiqin Zeng 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 {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 {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 {herrmann2025SIAMrdt,
	title = {Robust Digital Twin for Geological Carbon Storage},
	booktitle = {SIAM Conference on Computational Science and Engineering (CSE25)},
	year = {2025},
	note = {(SIAM CSE25, Dallas)},
	month = {03},
	abstract = {While recent work has shown that conditional neural networks can be used to effectuate prior-to-posterior mappings during ensemble Kalman filtering, their accuracy relies on the quality of the training ensemble, consisting of the simulated time-advanced nonlinear state and possibly nonlinear observations. Its precision also depends on the generative neural networks{\textquoteright} ability to generalize. In this work, we will report progress on how to make our Digital Twin more robust with respect to distribution shifts and modeling errors, e.g. due to erroneous choices for the physics within the Forecast Step. To this end, we will follow a two-pronged approach consisting of augmenting the Forecast step, e.g. by including different models for the rock physics that map the Digital Twin{\textquoteright}s state to seismic properties, and by adding more physics during the prior-to-posterior mapping. In its current implementation, training of our Digital Twin{\textquoteright}s neural networks relies on relative low-fidelity amortization of the Forecast Ensemble, which is susceptible to distribution shifts. By conducting non-amortized inference informed by the physics and low-fidelity prior-to-posterior mapping, we propose an approach that is more robust than the current implementation of the Digital Twin.},
	keywords = {Bayesian inference, conditional normalizing flows, CSE25, digital twin, rock physics, sequential Bayes, SIAM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMCSE/2025/herrmann2025SIAMrdt},
	author = {Felix J. Herrmann and Abhinav Prakash Gahlot and Rafael Orozco and Ziyi Yin and Grant Bruer}
}
@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 {deng2025ML4SEISMICtfi,
	title = {Time-lapse full-waveform inversion with uncertainty quantification},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Reliable management of underground energy storage systems, such as underground compressed Hydrogene or Air storage, rely on semi-continuous monitoring and rigorous quantification of subsurface uncertainty. Time-lapse seismic imaging provides valuable insights into fluid migration and storage integrity. However, traditional inversion techniques{\textemdash}particularly full-waveform inversion (FWI){\textemdash}are inherently ill-posed and deterministic, limiting their applicability for uncertainty-aware decision-making. The Joint Recovery Method (JRM) improves consistency across multiple surveys by jointly reconstructing shared structures, but does not capture uncertainty. We introduce the Probabilistic Joint Recovery Method (πJRM), which embeds uncertainty estimation within a joint inversion framework. πJRM leverages a shared generative model to infer wave-equation{\textendash}based posterior distributions for each monitoring survey, enabling probabilistic interpretation of time-lapse changes. Synthetic experiments demonstrate that πJRM accurately recovers dynamic reservoir evolution and provides uncertainty-aware reconstructions, offering a principled foundation for safe, efficient management of subsurface energy storage operations.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, GCS, igm, image generative model, Imaging, Inverse problems, jrm pjrm, ML4SEISMIC, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/deng2025ML4SEISMICtfi},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {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 {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 {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 {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 {herrmann2024CSMdt,
	title = {DT4GCS {\textendash}- Digital Twin for Geological CO2 Storage and Control},
	booktitle = {Gigatonnes CO2 Storage Workshop},
	year = {2024},
	note = {(Geophysical Research for Gigatonnes CO2 Storage Workshop, Golden)},
	month = {7},
	abstract = {Our industry is experiencing significant changes due to AI and the challenges of the energy transition. While some view these changes as threats, recent advances in AI offer unique opportunities, especially in the context of Digital Twins for subsurface monitoring and control. IBM defines {\textquotedblleft}A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.{\textquotedblright} During this talk, we will explore these concepts and their significance in addressing the challenges of monitoring and control of geological CO2 storage projects. This talk also aims to illustrate how Digital Twins can serve as a platform to integrate the seemingly disparate and siloed fields of geophysics and reservoir engineering.},
	keywords = {Bayesian inference, CCS, deep learning, digital twin, Imaging, monitoring, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CO2STORAGE/2024/herrmann2024CSMdt/herrmann2024CSMdt.pdf},
	author = {Felix J. Herrmann and Abhinav Prakash Gahlot and Rafael Orozco and Ziyi Yin and Haoyun Li}
}
@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 {zeng2024IMAGEefw,
	title = {Enhancing Full-Waveform Variational Inference through Stochastic Resampling and Data Augmentation},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, IMAGE, Imaging, Inverse problems, MVA, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slimgroup.github.io/IMAGE2024/Yunlin_Zeng2024SEG/paper.html},
	author = {Yunlin Zeng and Rafael Orozco and Ziyi Yin 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 {orozco2024IPMSnwi,
	title = {Neural wave-based imaging with amortized uncertainty quantification},
	booktitle = {Inverse Problems: Modelling and Simulation},
	year = {2024},
	month = {5},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, Imaging, Inverse problems, IPMS, MVA, RTM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/IPMS/2024/orozco2024IPMSnwi},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/IPMS/2024/orozco2024IPMSnwi/herrmann2024.pdf},
	author = {Rafael Orozco and Ziyi Yin and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@conference {orozco2024EAGEnfb,
	title = {Normalizing Flows for Bayesian Experimental Design in Imaging Applications},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2024},
	month = {6},
	abstract = {Neural density estimators, such as invertible normalizing flows, are capable of estimating the Bayesian posterior distribution in a variety of imaging problems, including medical MRI and seismic imaging/monitoring. So far, few works explore the possibility to make explicit use of probabilistic information contained within the full Bayesian solution of these inverse problems. During our talk, we investigate how a simple modification to the training objective of conditional normalizing flows allows for Bayesian experimental design without modifying the normalizing flow{\textquoteright}s neural architecture itself. By establishing a key relationship between the expected information gain (EIG) and the maximum-likelihood, attained during the training of normalizing flows, we show that optimal experimental design can be achieved. During our talk, we first verify, on a stylized problem, that our method indeed maximizes the expected information gain, followed by demonstrating the advocacy of our methodology on large-scale medical and seismic problems.},
	keywords = {Bayesian inference, deep learning, expected information gain, Normalizing flows, optimal experimental design, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2024/orozco2024EAGEnfb},
	author = {Rafael Orozco and Abhinav Prakash Gahlot and Peng Chen and Mathias Louboutin and Felix J. Herrmann}
}
@conference {orozco2024SIAMboed,
	title = {Normalizing Flows for Bayesian Experimental Design in Imaging Applications},
	booktitle = {SIAM Conference on Uncertainty Quantification},
	year = {2024},
	note = {(SIAM UQ, Trieste)},
	month = {03},
	abstract = {Neural density estimators such as normalizing flows have shown promise for estimation of the Bayesian posterior in a variety of imaging problems. Few works have explored how to practically exploit the probabilistic information contained in the full Bayesian solution of the inverse problem. Here we explore a simple modification to conditional normalizing flow training that enables Bayesian experimental design without modifying existing architectures. Based on a relationship between the expected information gain and maximum-likelihood training of normalizing flows, we show that experimental design can be achieved with the same training objective. We first verify that our method maximizes the expected information gain using a stylized problem. Then, we demonstrate our method can solve imaging problems in large scale medical and seismic applications.},
	keywords = {expected information gain, Normalizing flows, optimal experimental design, SIAM},
	doi = {10.48550/arXiv.2402.18337},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMUQ/2024/orozco2024SIAMUQboed},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMUQ/2024/orozco2024SIAMUQboed/orozco2024SIAMUQboed.pdf},
	author = {Rafael Orozco and Abhinav Prakash Gahlot and Peng Chen and Mathias Louboutin and Felix J. Herrmann}
}
@conference {herrmann2024SIAMUQdt,
	title = {An Uncertainty-Aware Digital Twin for Geological Carbon Storage},
	booktitle = {SIAM Conference on Uncertainty Quantification},
	year = {2024},
	note = {(SIAM UQ, Trieste)},
	month = {03},
	abstract = {Arguably, Geological Carbon Storage constitutes the only truly scalable net-negative carbon emission technology. To mitigate its risks and optimize its operations, an uncertainty-aware Digital Twin is being developed. To leverage existing fluid-flow and seismic simulation and imaging capabilities, the envisioned twin combines techniques from sequential and simulation-based Bayesian inference to train its deep generative neural networks to draw samples from the posterior of the Digital Twin{\textquoteright}s state. Because these samples are conditioned on observed time-lapse field data, these twins are capable of capturing the dynamics of CO2 plumes and their uncertainty.},
	keywords = {Bayesian inference, CCS, deep learning, digital twin, GCS, Imaging, monitoring, SIAM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMUQ/2024/digital-twin},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Grant Bruer and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {yin2024IMAGEwiser,
	title = {WISER: full-Waveform variational Inference via Subsurface Extensions with Refinements},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	abstract = {We introduce a cost-effective Bayesian inference method for full-waveform inversion (FWI) to quantify uncertainty in migration-velocity models and its impact on imaging. Our method targets inverse uncertainty due to null-space of the wave modeling operators and observational noise, and forward uncertainty where the uncertainty in velocity models is propagated to uncertainty in amplitude and positioning of imaged reflectivities. This is achieved by integrating generative artificial intelligence (genAI) with physics-informed common-image gathers (CIGs), which greatly reduces reliance on accurate initial FWI-velocity models. In addition, we illustrate the capability of fine-tuning the generative AI networks with frugal physics-based refinements to improve the inference accuracy.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, IMAGE, Imaging, Inverse problems, MVA, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE, WISER},
	url = {https://slimgroup.github.io/IMAGE2024/yin2024SEG/paper.html},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@conference {orozco2022SPIEadjoint,
	title = {Adjoint operators enable fast and amortized machine learning based Bayesian uncertainty quantification},
	booktitle = {SPIE Medical Imaging Conference},
	year = {2023},
	note = {(SPIE, San Diego)},
	month = {02},
	abstract = {Machine learning algorithms such as Normalizing Flows, VAEs, and GANs are powerful tools in Bayesian uncertainty quantification (UQ) of inverse problems. Unfortunately, when using these algorithms medical imaging practitioners are faced with the challenging task of manually defining neural networks that can handle complicated inputs such as acoustic data. This task needs to be replicated for different receiver types or configurations since these change the dimensionality of the input. We propose to first transform the data using the adjoint operator {\textemdash}ex: time reversal in photoacoustic imaging (PAI) or back-projection in computer tomography (CT) imaging {\textemdash} then continue posterior inference using the adjoint data as an input now that it has been standardized to the size of the unknown model. This adjoint preprocessing technique has been used in previous works but with minimal discussion on if it is biased. In this work, we prove that conditioning on adjoint data is unbiased for a certain class of inverse problems. We then demonstrate with two medical imaging examples (PAI and CT) that adjoints enable two things: Firstly, adjoints partially undo the physics of the forward operator resulting in faster convergence of an ML based Bayesian UQ technique. Secondly, the algorithm is now robust to changes in the observed data caused by differing transducer subsampling in PAI and number of angles in CT. Our adjoint-based Bayesian inference method results in point estimates that are faster to compute than traditional baselines and show higher SSIM metrics, while also providing validated UQ.},
	keywords = {Amortized Inference, Bayesian inference, deep learning, Inverse problems, machine learning, medical imaging, Normalizing flows, SPIE, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SPIE/2023/orozco2022SPIEadjoint/SPIE_2022_adjoint.html},
	author = {Rafael Orozco and Ali Siahkoohi and Gabrio Rizzuti and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {orozco2023IMAGEabf,
	title = {Amortized Bayesian Full Waveform Inversion and Experimental Design with Normalizing Flows},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Probabilistic approaches to Full-Waveform Inversion (FWI), such as Bayesian ones, traditionally require expensive computations involving many wave-equation solves. To reduce the computational burden at test time, we propose to amortize the computational cost with offline training. After training, we aim to efficiently generate probabilistic FWI solutions with uncertainty. This aim is achieved by exploiting the ability of networks (i.e Normalizing Flows) to learn distributions, such as the Bayesian posterior. The posterior uncertainty is used during training to optimize the receiver sampling.},
	keywords = {bayesian, experimental design, FWI, Normalizing flows, SEG, Uncertainty quantification},
	url = {https://slimgroup.github.io/IMAGE2023/BayesianFWI/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/orozco2023IMAGEabf},
	author = {Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@conference {orozco2023MIDLanf,
	title = {Amortized normalizing flows for transcranial ultrasound with uncertainty quantification},
	booktitle = {Medical Imaging with Deep Learning},
	year = {2023},
	note = {(MIDL, Nashville)},
	month = {07},
	abstract = {We present a novel approach to transcranial ultrasound computed tomography that utilizes normalizing flows to improve the speed of imaging and provide Bayesian uncertainty quantification. Our method combines physics-informed methods and data-driven methods to accelerate the reconstruction of the final image. We make use of a physics-informed summary statistic to incorporate the known ultrasound physics with the goal of compressing large incoming observations. This compression enables efficient training of the normalizing flow and standardizes the size of the data regardless of imaging configurations. The combinations of these methods results in fast uncertainty-aware image reconstruction that generalizes to a variety of transducer configurations. We evaluate our approach with in silico experiments and demonstrate that it can significantly improve the imaging speed while quantifying uncertainty. We validate the quality of our image reconstructions by comparing against the traditional physics-only method and also verify that our provided uncertainty is calibrated with the error.},
	keywords = {Bayesian Estimation, invertible networks, medical imaging, MIDL, Physics and Machine Learning Hybrid, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/MIDL/2023/orozco2023MIDLanf/paper.pdf},
	author = {Rafael Orozco and Mathias Louboutin and Ali Siahkoohi and Gabrio Rizzuti and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {herrmann2023IMAGEWSfnf,
	title = {Fast neural FWI with amortized uncertainty quantification},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE workshop, Houston)},
	month = {08},
	keywords = {bayesian, experimental design, FWI, Normalizing flows, SEG, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/herrmann2023IMAGEWSfnf},
	author = {Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@conference {orozco2023IMAGEgsk,
	title = {Generative Seismic Kriging with Normalizing Flows},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {The objective is to demonstrate the applicability of Normalizing Flows (NFs) to subsurface kriging from wells. We will show that after supervised training, we can generate multiple realistic samples of plausible earth models that match the observed wells. We observe that these samples produce uncertainty statistics that are correlated with the complex parts of the model. The applicability of this method is for areas nearby the original survey used for training. Finally, we compare the speed and quality of our solutions with those obtained using a traditional variogram approach.},
	keywords = {kriging, Normalizing flows, SEG, Uncertainty quantification},
	url = {https://slimgroup.github.io/IMAGE2023/BayesianKrig/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/orozco2023IMAGEgsk},
	author = {Rafael Orozco 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 {louboutin2023IMAGEloi,
	title = {Learned non-linear simultenous source and corresponding supershot for seismic imaging.},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Seismic imaging{\textquoteright}s main limiting factor is the scale of the involved dataset and the number of independent wave-equation solves required to migrate thousands of shots. To tackle this dimensionality curse, we introduce a learned framework that extends the conventional computationally reductive linear source superposition (e.g., via random simultaneous-source encoding) to a nonlinear learned source superposition and its corresponding learned supershot. With this method, we can image the subsurface at the cost of a one-shot migration by learning the most informative superposition of shots.},
	keywords = {deep learning, Imaging, SEG, simultaneous},
	url = {https://slimgroup.github.io/IMAGE2023/OneShot/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/louboutin2023IMAGEloi/louboutin2023IMAGEloi_pres.pdf},
	author = {Mathias Louboutin and Rafael Orozco and Ali Siahkoohi 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 {orozco2023AABIiter,
	title = {Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics},
	booktitle = {5th Symposium on Advances in Approximate Bayesian Inference},
	year = {2023},
	note = {(AABI, Honolulu)},
	month = {06},
	abstract = {We present an iterative framework to improve the amortized approximations of posterior distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled gradient descent methods and is theoretically grounded in maximally informative summary statistics. Amortized variational inference is restricted by the expressive power of the chosen variational distribution and the availability of training data in the form of joint data and parameter samples, which often lead to approximation errors such as the amortization gap. To address this issue, we propose an iterative framework that refines the current amortized posterior approximation at each step. Our approach involves alternating between two steps: (1) constructing a training dataset consisting of pairs of summarized data residuals and parameters, where the summarized data residual is generated using a gradient-based summary statistic, and (2) training a conditional generative model {\textendash} a normalizing flow in our examples {\textendash} on this dataset to obtain a probabilistic update of the unknown parameter. This procedure leads to iterative refinement of the amortized posterior approximations without the need for extra training data. We validate our method in a controlled setting by applying it to a stylized problem, and observe improved posterior approximations with each iteration. Additionally, we showcase the capability of our method in tackling realistically sized problems by applying it to transcranial ultrasound, a high-dimensional, nonlinear inverse problem governed by wave physics, and observe enhanced posterior quality through better image reconstruction with the posterior mean.},
	keywords = {AABI, Amortization Gap, Amortized Variational Inference, Bayesian inference, conditional normalizing flows, Summary Statistics},
	url = {https://arxiv.org/abs/2305.08733},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AABI/2023/orozco2023AABIiter/aabi_poster.pdf},
	author = {Rafael Orozco and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@conference {yin2023EMIutm,
	title = {Uncertainty-aware time-lapse monitoring of geological carbon storage with learned surrogates},
	booktitle = {Engineering Mechanics Institute Conference},
	year = {2023},
	note = {(EMI, Atlanta)},
	month = {06},
	abstract = {Time-lapse seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics and wave physics. It also requires differentiation through the solvers with respect to properties of interest in the subsurface. In this talk, we present a learned end-to-end inversion framework, which uses a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator in order to greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. Through synthetic experiments, we demonstrate the efficacy of this framework on inverting the subsurface permeability of the reservoir and on monitoring CO2 plumes. We further quantify the uncertainty of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can also forecast the growth of CO2 plumes in the future with uncertainty estimation without any acquired seismic data.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, EMI, end-to-end, Fourier neural operators, GCS, inversion, machine learning, Normalizing flows, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EMI/2023/yin2023EMIutm/yin2023EMIutm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EMI/2023/yin2023EMIutm/index.html},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Ali Siahkoohi and Felix J. Herrmann}
}
@conference {louboutin2022RHPCafa,
	title = {Abstractions for at-scale seismic inversion},
	booktitle = {Rice Oil and Gas High Performance Computing Conference 2022},
	year = {2022},
	note = {Rice Oil and Gas High Performance Computing Conference 2022},
	month = {03},
	pages = {Thursday Workshop: Devito Training and Hackathon},
	abstract = {We present the SLIM open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical ultrasound). We developed a software environment aimed at scalable research and development by designing multiple layers of abstractions. This environment allows the researchers to easily formulate their problem in an abstract fashion, while still being able to exploit the latest developments in high-performance computing. We illustrate and demonstrate the benefits of our software design on many geophysical applications, including seismic inversion and physics-informed machine learning for geophysics (e.g., loop unrolled imaging, uncertainty quantification), all while facilitating the integration of external software.},
	keywords = {CCS, devito, FWI, HPC, inversion, JUDI, machine learning, RHPC, software, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/RHPC/2022/louboutin2022RHPCafa/RiceHPC22.pdf},
	url2 = {https://youtu.be/scRTbP8w6Wk?t=4542},
	author = {Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Yijun Zhang and Philipp A. Witte and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {louboutin2022SEGais,
	title = {Accelerating innovation with software abstractions for scalable computational geophysics},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {We present the SLIM open-source software framework for computational geophysics, and more generally, inverse problems based on the wave-equation (e.g., medical ultrasound). We developed a software environment aimed at scalable research and development by designing multiple layers of abstractions. This environment allows the researchers to easily formulate their problem in an abstract fashion, while still being able to exploit the latest developments in high-performance computing. We illustrate and demonstrate the benefits of our software design on many geophysical applications, including seismic inversion and physics-informed machine learning for geophysics(e.g., loop unrolled imaging, uncertainty quantification), all while facilitating the integration of external software.},
	keywords = {CCS, devito, FWI, HPC, inversion, JUDI, machine learning, SEG, software, Uncertainty quantification},
	doi = {10.1190/image2022-3750561.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/louboutin2022SEGais/louboutin_seg22.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/louboutin2022SEGais/index.html},
	author = {Mathias Louboutin and Philipp A. Witte and Ali Siahkoohi and Gabrio Rizzuti and Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@conference {siahkoohi2022SEGWSrav,
	title = {Reliable amortized variational inference with conditional normalizing flows via physics-based latent distribution correction},
	booktitle = {IMAGE Workshop on Subsurface Uncertainty Description and Estimation - Moving Away from Single Prediction with Distribution Learning},
	year = {2022},
	note = {(IMAGE Workshop, Houston)},
	month = {09},
	abstract = {Bayesian inference for high-dimensional inverse problems is challenged by the computational costs associated with the forward operator during posterior sampling, as well as the selection of an appropriate prior distribution that encodes our prior knowledge about the unknown. Amortized variational inference addresses these challenges by pretraining a conditional normalizing flow (cNF) that approximates the posterior distribution over the existing model and data joint samples where the prior is implicitly learned from the data. When fed data and normally distributed latent samples as input, the pretrained cNF provides posterior samples for previously unseen data virtually for free. The accuracy of this purely data-driven approach, however, is dependent on the availability of high-fidelity training data, which is rarely the case with geophysical inverse problems, due to the highly heterogeneous structure of the Earth. To overcome this challenge and to minimize the negative bias of data distribution shifts during inference, we propose learning a physics-based correction to the cNF latent distribution to provide a more accurate approximation to the posterior distribution. We accomplish this by parameterizing the cNF latent distribution by a Gaussian distribution with an unknown mean and diagonal covariance, which are estimated by minimizing the Kullback-Leibler divergence between the corrected posterior distribution estimate and the true posterior distribution. For a relatively well-trained cNF, this approach provides reliable posterior samples with a limited computational cost while remaining bound to data and physics. We showcase the computational gains of this approach on a "quasi" real seismic imaging example.},
	keywords = {Bayesian inference, deep learning, Generative models, Imaging, Inverse problems, SEG, Uncertainty quantification, workshop},
	url = {https://www.imageevent.org/Workshop/subsurface-uncertainty-description-estimation-moving-away-single-prediction-distribution-learning},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/siahkoohi2022SEGWSrav/Thur-15-55-Siahkoohi.pdf},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Felix J. Herrmann}
}
@conference {siahkoohi2022EAGEweb,
	title = {Wave-equation based inversion with amortized variational Bayesian inference},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2022},
	note = {(EAGE, Madrid)},
	month = {06},
	pages = {Session 2: Velocity model building and imaging (different domains)},
	abstract = {Solving inverse problems involving measurement noise and modeling errors requires regularization in order to avoid data overfit. Geophysical inverse problems, in which the Earth{\textquoteright}s highly heterogeneous structure is unknown, present a challenge in encoding prior knowledge through analytical expressions. Our main contribution is a generative-model-based regularization approach, robust to out-of-distribution data, which exploits the prior knowledge embedded in existing data and model pairs. Utilizing an amortized variational inference objective, a conditional normalizing flow (NF) is pretrained on pairs of low- and high-fidelity migrated images in order to achieve a low-fidelity approximation to the seismic imaging posterior distribution for previously unseen data. The NF is used after pretraining to reparameterize the unknown seismic image in an inversion scheme involving physics-guided data misfit and a Gaussian prior on the NF latent variable. Solving this optimization problem with respect to the latent variable enables us to leverage the benefits of data-driven conditional priors whilst being informed by physics and data. The numerical experiments demonstrate that the proposed inversion scheme produces seismic images with limited artifacts when dealing with noisy and out-of-distribution data.},
	keywords = {conditional priors, EAGE, Normalizing flows, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/siahkoohi2022EAGEweb/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/siahkoohi2022EAGEweb/Mon-Herrmann.pdf},
	software = {https://github.com/slimgroup/ConditionalNFs4Imaging.jl},
	author = {Ali Siahkoohi and Rafael Orozco and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {siahkoohi2021EarthMLfar,
	title = {Fast and reliability-aware seismic imaging with conditional normalizing flows},
	booktitle = {Intelligent illumination of the Earth},
	year = {2021},
	note = {(KAUST, virtual)},
	month = {09},
	abstract = {The posterior probability distribution provides a comprehensive description of the solution in ill-posed inverse problems. Sampling from the posterior distribution in the context of seismic imaging is challenged by the high-dimensionality of the unknown and the expensive-to-evaluate forward operator. These challenges limit the applicability of Markov Chain sampling methods due to the costs associated with the forward operator. Moreover, explicitly choosing a prior distribution that captures the true heterogeneity exhibited by the Earth{\textquoteright}s subsurface further complicates casting seismic imaging into a Bayesian framework. To handle this situation and to assess uncertainty, we propose a data-driven variational inference approach based on conditional normalizing flows (NFs). The proposed scheme leverages existing data, which are in the form of low- and high-fidelity migrated image pairs, to train a conditional NF capable of characterizing the posterior distribution. After training, the NF can be used to sample from the posterior distribution associated with a previously unseen seismic survey, which is in some sense close, e.g., data from a neighboring survey area. In our numerical example, we obtain high-fidelity images from the Parihaka dataset and low-fidelity images are derived from these images through the process of demigration, followed by adding band-limited noise and migration. During inference, given shot records from a new neighboring seismic survey, we first compute the reverse-time migration image. Next, by feeding this low-fidelity migrated image to the NF we gain access to samples from the posterior distribution virtually for free. We use these samples to compute a high-fidelity image including a first assessment of the image{\textquoteright}s reliability.},
	keywords = {Normalizing flows, seismic imaging, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/KAUST/2021/siahkoohi2021EarthMLfar/siahkoohi2021EarthMLfar.pdf},
	author = {Ali Siahkoohi and Rafael Orozco and Gabrio Rizzuti and Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@conference {herrmann2021Delftlwi,
	title = {Learned wave-based imaging - variational inference at scale},
	booktitle = {Delft},
	year = {2021},
	note = {(Delft, virtual)},
	month = {06},
	abstract = {High dimensionality, complex physics, and lack of access to the ground truth rank medical ultrasound and seismic imaging amongst the most challenging problems in the computational imaging sciences. If these challenges were not bad enough, modern applications of computational imaging increasingly call for the assessment of uncertainty on the image itself and on subsequent tasks. During this talk, I will show how recent developments in Normalizing Flows, a new type of invertible neural networks, can be used to cast wave-based imaging into a scalable Bayesian framework. Contrary to conventional methods, where sample images are drawn from the posterior distribution during inversion, our approach trains Normalizing Flows capable of generating samples from the posterior. Aside from greatly reducing the computational cost, this approach gives us access to the image itself (via Maximum a posteriori or mean estimation) and its multidimensional statistical distribution including its pointwise variance.},
	keywords = {medical imaging, Normalizing flows, seismic imaging, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Delft/2021/herrmann2021Delftlwi/herrmann2021Delftlwi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/Delft/2021/herrmann2021Delftlwi/herrmann2021Delftlwi.mp4},
	author = {Felix J. Herrmann and Ali Siahkoohi and Rafael Orozco and Gabrio Rizzuti and Philipp A. Witte and Mathias Louboutin}
}
@conference {orozco2021NIPSpicp,
	title = {Photoacoustic Imaging with Conditional Priors from Normalizing Flows},
	booktitle = {Neural Information Processing Systems (NeurIPS)},
	year = {2021},
	note = {(NIPS, virtual)},
	month = {12},
	abstract = {Photoacoustic imaging is a biomedical imaging technique based on the photoacoustic effect. It leverages the interplay between optics and acoustics as a mean to circumvent the limitations of imaging modalities relying on single-type physics. Light beams generated by a pulsed laser can penetrate biological tissues by several centimeters, and are absorbed based on oxygen saturation or hemoglobin concentration. While optical absorption is in principle an ideal parameter for medical imaging (e.g., with respect to the detection of cancerous tissue), strong scattering imposes important limitations in its imaging resolution. Ultrasonics, on the other hand, can theoretically provide resolution of medical diagnostic value, but produce images of mechanical properties whose contrasts are not sensitive. In photoacoustics, optical and acoustic effects are combined to gain the best of both worlds. Under conditions of thermal and stress confinement, thermal energy can efficiently build up in biological tissues, which in turn undergo thermal expansion and effectively act as a spatially distributed acoustic source. In photoacoustic imaging, the actual object of interest is the induced source, as it is directly related to optical absorption and can be recovered with a relatively higher resolution than pure optical imaging, based on the acquired ultrasonic data.},
	keywords = {conditional prior, deep image, MAP, NIPS, normalizing flow, Photoacoustic, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2021/orozco2021NIPSpicp/deep_inverse_2021.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2021/orozco2021NIPSpicp/posterneurips2021_orozco.pdf},
	url2 = {https://openreview.net/forum?id=woi1OTvROO1},
	software = {https://github.com/slimgroup/InvertibleNetworks.jl},
	author = {Rafael Orozco and Ali Siahkoohi and Gabrio Rizzuti and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {yin2020SEGesi,
	title = {Extended source imaging {\textendash}- a unifying framework for seismic and medical imaging},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {3502-3506},
	abstract = {We present three imaging modalities that live on the crossroads of seismic and medical imaging. Through the lens of extended source imaging, we can draw deep connections among the fields of wave-equation based seismic and medical imaging, despite first appearances. From the seismic perspective, we underline the importance to work with the correct physics and spatially varying velocity fields. Medical imaging, on the other hand, opens the possibility for new imaging modalities where outside stimuli, such as laser or radar pulses, can not only be used to identify endogenous optical or thermal contrasts but that these sources can also be used to insonify the medium so that images of the whole specimen can in principle be created.},
	keywords = {medical imaging, SEG, seismic imaging, variable projection},
	doi = {10.1190/segam2020-3426999.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/yin2020SEGesi/yin2020SEGesi.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/yin2020SEGesi/yin2020SEGesi_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/yin2020SEGesi/yin2020SEGesi_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Ziyi Yin and Rafael Orozco and Philipp A. Witte and Mathias Louboutin and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {oghenekohwo2017EAGEitl,
	title = {Improved time-lapse data repeatability with randomized sampling and distributed compressive sensing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Recently, new ideas on randomized sampling for time-lapse seismic acquisition have been proposed to address some of the challenges of replicating time-lapse surveys. These ideas, which stem from distributed compressed sensing (DCS) led to the birth of a joint recovery model (JRM) for processing time-lapse data (noise-free) acquired from non-replicated acquisition geometries. However, when the earth does not change{\textendash}-i.e. no time-lapse{\textemdash}the recovered vintages from two non-replicated surveys should show high repeatability measured in terms of normalized RMS, which is a standard metric for quantifying time-lapse data repeatability. Under this assumption of no time-lapse change, we demonstrate improved repeatability (with JRM) of the recovered data from non-replicated random samplings, first with noisy data and secondly in situations where there are calibration errors i.e. where the acquisition parameters such as source/receiver coordinates are not precise.},
	keywords = {calibration, Compressive Sensing, EAGE, noise, repeatability, time lapse},
	doi = {10.3997/2214-4609.201701389},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/oghenekohwo2017EAGEitl/oghenekohwo2017EAGEitl.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/oghenekohwo2017EAGEitl/oghenekohwo2017EAGEitl_poster.pdf},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {herrmann2016SEGWSctl,
	title = {Compressive time-lapse marine acquisition},
	booktitle = {SEG Workshop on Low cost geophysics: How to be creative in a cost-challenged environment; Dallas},
	year = {2016},
	note = {(SEG Workshop, Dallas)},
	month = {10},
	keywords = {Acquisition, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/herrmann2016SEGWSctl/herrmann2016SEGWSctl_pres.pdf},
	author = {Felix J. Herrmann and Rajiv Kumar and Felix Oghenekohwo and Shashin Sharan and Haneet Wason}
}
@conference {wason2015EAGEcsm,
	title = {Compressed sensing in 4-D marine{\textendash}-recovery of dense time-lapse data from subsampled data without repetition},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {We present an extension of our time-jittered marine acquisition for time-lapse surveys by working on more realistic field acquisition scenarios by incorporating irregular spatial grids without insisting on repeatability between the surveys. Since we are always subsampled in both the baseline and monitor surveys, we are interested in recovering the densely sampled baseline and monitor, and then the (complete) 4-D difference from subsampled/incomplete baseline and monitor data.},
	keywords = {EAGE, NFFT, off-the-grid, simultaneous acquisition, time-lapse},
	doi = {10.3997/2214-4609.201413088},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/wason2015EAGEcsm/wason2015EAGEcsm.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/wason2015EAGEcsm/wason2015EAGEcsm_poster.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {oghenekohwo2015CSEGctl,
	title = {Compressive time-lapse seismic data processing using shared information},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2015},
	note = {(CSEG, Calgary)},
	month = {05},
	abstract = {Time-lapse images void of acquisition and processing artifacts can provide more useful information about subsurface changes compared to those with acquisition footprints and other unwanted anomalies. Although, several pre-processing techniques are being developed and used to mitigate these unwanted artifacts, these operations can be very expensive, challenging and data dependent. Migration, as a processing tool, using a sparsity constraint has been shown to reduce artifacts drastically but little is known about the significance for compressed time-lapse seismic data. Leveraging ideas from distributed compressed sensing, and motivated by our earlier work on recovery of densely sampled time-lapse data from compressively sampled measurements, we present a sparsity-constrained migration for time-lapse data that uses a common component shared by the baseline and monitor data. Our algorithm tested on a synthetic example highlights the advantages of exploiting the common information, compared to ad hoc methods that involve parallel processing of the time-lapse data before differencing.},
	keywords = {CSEG, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/oghenekohwo2015CSEGctl/oghenekohwo2015CSEGctl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/oghenekohwo2015CSEGctl/oghenekohwo2015CSEGctl_pres.pdf},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {oghenekohwo2015PIMSntc,
	title = {A new take on compressive time-lapse seismic acquisition, imaging and inversion},
	booktitle = {PIMS Workshop on Advances in Seismic Imaging and Inversion},
	year = {2015},
	note = {(PIMS Workshop on Advances in Seismic Imaging and Inversion, University of Alberta, Edmonton)},
	month = {05},
	abstract = {Compressive sensing (CS), a sampling paradigm that is changing how data is acquired and processed in many fields, has attracted tremendous attention in recent years. In exploration seismology, particularly in 3D seismic technology, there have been published work on the application of CS to seismic data acquisition, processing and inversion. However, very little is known about the implications of CS in time-lapse (4D) seismic. Leveraging ideas from distributed CS where more than one signal is acquired and processed, as in 4D seismic, we present a joint recovery algorithm for time-lapse seismic data acquisition, imaging and inversion. The algorithm exploits the common information in the data sets (baseline and monitor) as part of an inversion procedure. Application of our algorithm to realistic synthetic examples highlight its advantages over other conventional approaches for processing time-lapse seismic data.},
	keywords = {PIMS, time-lapse, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/PIMS/2015/oghenekohwo2015PIMSntc/oghenekohwo2015PIMSntc_pres.pdf},
	author = {Felix Oghenekohwo 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 {petrenko2014EAGEaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {We implement the Kaczmarz row-projection algorithm (Kaczmarz (1937)) on a CPU host + FPGA accelerator platform using techniques of dataflow programming. This algorithm is then used as the preconditioning step in CGMN, a modified version of the conjugate gradients method (Bj{\"o}rck and Elfving (1979)) that we use to solve the time-harmonic acoustic isotropic constant density wave equation. Using one accelerator we achieve a speed-up of over 2{\texttimes} compared with one Intel core.},
	keywords = {CGMN, EAGE, FPGA, Helmholtz equation, Kaczmarz, reconfigurable computing},
	doi = {10.3997/2214-4609.20141141},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/petrenko2014EAGEaih/petrenko2014EAGEaih.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/petrenko2014EAGEaih/petrenko2014EAGEaih_pres.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Diego Oriato and Simon Tilbury and Felix J. Herrmann}
}
@conference {petrenko2014OGHPCaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	booktitle = {OGHPC},
	year = {2014},
	month = {03},
	abstract = {We implement the Kaczmarz row-projection algorithm [Kaczmarz, 1937] on a CPU host + FPGA accelerator platform using techniques of dataflow programming. This algorithm is then used as the preconditioning step in CGMN, a modified version of the conjugate gradients method [Bj?rck and Elfving, 1979] that we use to solve the time-harmonic acoustic isotropic constant density wave equation.},
	keywords = {CGMN, FPGA, Helmholtz equation, Kaczmarz, OGHPC, reconfigurable computing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/OGHPC/2014/petrenko2014OGHPCaih/petrenko2014OGHPCaih.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/OGHPC/2014/petrenko2014OGHPCaih/petrenko2014OGHPCaih_poster.pdf},
	author = {Art Petrenko and Felix J. Herrmann and Diego Oriato and Simon Tilbury and Tristan van Leeuwen}
}
@conference {wason2014SEGrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {46-51},
	abstract = {We present an extension of our time-jittered simultaneous marine acquisition to time-lapse surveys where the requirement for repeatability in acquisition can be waived provided we know the acquisition geometry afterwards. Our method, which does not require repetition, gives 4-D signals comparable to conventional methods where repeatability is key to their success.},
	keywords = {Acquisition, deblending, marine, SEG, time-laspe},
	doi = {http://dx.doi.org/10.1190/segam2014-1677.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGrrt/wason2014SEGrrt.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGrrt/wason2014SEGrrt_pres.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {herrmann2014EAGEWSrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	booktitle = {EAGE Workshop on Land and Ocean Bottom; Broadband Full Azimuth Seismic Surveys; Spain},
	year = {2014},
	note = {(EAGE Workshop, Spain)},
	month = {04},
	keywords = {4D seismic, EAGE, Marine acquisition, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/herrmann2014EAGEWSrrt.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {oghenekohwo2014SEGrsw,
	title = {Randomized sampling without repetition in time-lapse surveys},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {4848-4852},
	abstract = {Vouching for higher levels of repeatability in acquisition and processing of time-lapse (4D) seismic data has become the standard with oil and gas contractor companies, with significant investment in the design of acquisition systems and processing algorithms that attempt to address some of the current 4D challenges, in particular, imaging weak 4D signals. Recent developments from the field of compressive sensing have shown the benefits of variants of randomized sampling in marine seismic acquisition and its impact for the future of seismic exploration. Following these developments, we show that the requirement for accurate survey repetition in time-lapse seismic data acquisition can be waived provided we solve a sparsity-promoting convex optimization program that makes use of the shared component between the baseline and monitor data. By setting up a framework for inversion of the stacked sections of a time-lapse data, given the pre-stack data volumes, we are able to extract 4D signals with relatively highfidelity from significant subsamplings. Our formulation is applied to time-lapse data that has been acquired with different source/receiver geometries, paving the way for an efficient approach to dealing with time-lapse data acquired with initially poor repeatability levels, provided the survey geometry details are known afterwards.},
	keywords = {4D, Acquisition, random, repetition, SEG, time-lapse},
	doi = {http://dx.doi.org/10.1190/segam2014-1627.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw_pres.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar 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 {oghenekohwo2013CSEGnratld,
	title = {Assessing the need for repeatability in acquisition of time-lapse data},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2013},
	month = {05},
	abstract = {There are several factors that affect the repeatability of 4D(time-lapse) seismic data. One of the most significant factors is the repeatability of the acquisition, particularly the locations of the sources and receivers. It is important to repeat the source-receiver locations, used during the baseline survey, in the monitor or repeat survey. Also, it is essential that the stacked data volumes used for time-lapse analysis are created using the same offset ranges for each survey. This condition is crucial in order to be able to produce an image of the same location over a period of time and enhances proper reservoir characterization. The cost of repeating the seismic acquisition is very expensive, as often times, the receiver array has to be left at the same location over the period for which the data will be acquired. In other words, it is important to repeat the acquisition geometry as much as possible. In this talk, we investigate the results of changing the acquisition geometry, by a random placement of the receivers for both the baseline surveys and newer (monitor) surveys. Results show that we are still able to observe any time-lapse effects from the proposed acquisition geometry. Our experiments have been performed on a synthetic model.},
	keywords = {Acquisition, CSEG, migration, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2013/oghenekohwo2013CSEGnratld/oghenekohwo2013CSEGnratld.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2013/oghenekohwo2013CSEGnratld/oghenekohwo2013CSEGnratld_pres.pdf},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {min2012EAGEefwi,
	title = {Frequency-domain elastic waveform inversion using weighting factors related to source-deconvolved residuals},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {One of the limitations in seismic waveform inversion is that inversion results are very sensitive to initial guesses, which may be because the gradients computed at each frequency are not properly weighted depending on given models.Analyzingthe conventional waveform inversion algorithms using the pseudo-Hessian matrix as a pre-conditioner shows that the gradientsdo not properly describe the feature of given models or high- and low-end frequencies do not contribute the model parameter updates due to banded spectra of source wavelet. For a better waveform inversion algorithm, we propose applying weighting factors to gradients computed at each frequency. The weighting factors are designed using the source-deconvolved back-propagated wavefields. Numerical results for the SEG/EAGE salt model show that the weighting method improves gradient images and its inversion results are compatible with true velocities even with poorly estimated initial guesses.},
	keywords = {EAGE, elastic, frequency-domain, waveform inversion, weighting factors},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/min2012EAGEefwi/min2012EAGEefwi.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=59623},
	author = {Ju-Won Oh and Dong-Joo Min and Felix J. Herrmann}
}
@conference {min2012CSEGrgfe,
	title = {Re-establishment of gradient in frequency-domain elastic waveform inversion},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2012},
	month = {02},
	abstract = {To obtain solutions close to global minimum in waveform inversion, the gradients computed at each frequency need to be weighted to appropriately describe the residuals between modeled and field data. While the low-frequency components of the gradients should be weighted to recover the long-wavelength structures, the high-frequency components of the gradients need to be weighted when the short-wavelength structures are restored. However, the conventional elastic waveform inversion algorithms cannot properly weight the gradients computed at each frequency. When gradients are scaled using the pseudo-Hessian matrix inside the frequency loop, gradients obtained at high frequencies are over-emphasized. When the gradients are scaled outside the frequency loop, gradients are weighted by the source spectra. In this study, we propose applying weighting factors to the gradients obtained at each frequency so that gradients can properly reflect the differences between the true and assumed models satisfying the general inverse theory. The weighting factors are composed by the backpropagated residuals. Numerical examples for the simple rectangular-shaped model and the modified version of the Marmousi-2 model show that the weighting method enhances gradient images and inversion results compared to the conventional inversion algorithms.},
	keywords = {elastic, frequency-domain, waveform inversion, weighting factors},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2012/min2012CSEGrgfe/min2012CSEGrgfe.pdf},
	author = {Ju-Won Oh and Dong-Joo Min 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}
}
