@presentation {herrmann2024dt4gcs,
	title = {Digital Twins in the Era of Generative AI: Application to Geological CO 2  Storage},
	year = {2024},
	month = {2},
	abstract = {Our industry is experiencing significant changes due to AI and the challenges of the energy transition. While some view these changes as threats, recent advances in AI offer unique opportunities, especially in the context of Digital Twins for subsurface monitoring and control. IBM defines "A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." During this talk, I will explore these concepts and their significance in addressing the challenges of monitoring \& control of geological CO 2  storage projects. This talk also aims to illustrate how Digital Twins can serve as a platform to integrate the seemingly disparate and siloed fields of geophysics and reservoir engineering.},
	keywords = {Bayesian inference, CCS, deep learning, digital twin, Imaging, monitoring, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Halliburton/2024/herrmann2024dt4gcs.pdf},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Huseyin Tuna Erdinc and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@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 = {4},
	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}
}
@unpublished {zeng2024IMAGEefw,
	title = {Enhancing Full-Waveform Variational Inference through Stochastic Resampling and Data Augmentation},
	year = {2024},
	month = {3},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, Imaging, Inverse problems, MVA, RTM, 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 {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}
}
@article {yin2023wise,
	title = {WISE: full-Waveform variational Inference via Subsurface Extensions},
	journal = {Geophysics},
	year = {2024},
	note = {(GEOPHYSICS)},
	month = {04},
	abstract = {We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, Geophysics, Imaging, Inverse problems, MVA, RTM, Summary Statistics, Uncertainty quantification, WISE},
	doi = {10.1190/geo2023-0744.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2024/yin2023wise/paper.html},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@unpublished {yin2024IMAGEwiser,
	title = {WISER: full-Waveform variational Inference via Subsurface Extensions with Refinements},
	year = {2024},
	month = {3},
	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, Imaging, Inverse problems, MVA, RTM, 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}
}
@unpublished {yin2024wiser,
	title = {WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction},
	year = {2024},
	month = {5},
	abstract = {We present a semi-amortized variational inference framework designed for computationally feasible uncertainty quantification in 2D full-waveform inversion to explore the multimodal posterior distribution without dimensionality reduction. The framework is called WISER, short for full-Waveform variational Inference via Subsurface Extensions with Refinements. WISER leverages the power of generative artificial intelligence to perform approximate amortized inference that is low-cost albeit showing an amortization gap. This gap is closed through non-amortized refinements that make frugal use of acoustic wave physics. Case studies illustrate that WISER is capable of full-resolution, computationally feasible, and reliable uncertainty estimates of velocity models and imaged reflectivities.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, Imaging, Inverse problems, MVA, Summary Statistics, Uncertainty quantification, WISE, WISER},
	doi = {10.13140/RG.2.2.34906.15044},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2024/yin2024wiser/WISER.html},
	author = {Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@article {louboutin2023lmi,
	title = {Learned multiphysics inversion with differentiable programming and machine learning},
	journal = {The Leading Edge},
	volume = {42},
	year = {2023},
	note = {(The Leading Edge)},
	month = {07},
	pages = {452-516},
	abstract = {We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning.},
	keywords = {CCS, deep learning, Fourier neural operators, GCS, Imaging, inversion, invertible networks, machine learning, monitoring, multiphysics, Normalizing flows, SLIM, software, time-lapse},
	url = {https://library.seg.org/doi/10.1190/tle42070474.1},
	presentation = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.html},
	author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Ali Siahkoohi and Gabrio Rizzuti and Philipp A. Witte and Olav M{\o}yner and Gerard J. Gorman and Felix J. Herrmann}
}
@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}
}
@presentation {herrmann2023ML4SEISMICmsc,
	title = {Monitoring subsurface CO2 plumes with learned sequential Bayesian inference},
	year = {2023},
	month = {11},
	abstract = {Reservoir engineers frequently employ two-phase flow simulations and history-matching to oversee and anticipate the behavior of CO2 plumes within geological carbon storage. These simulations, while valuable for gaining insights, face limitations due to several complex factors, such as uncertainties surrounding the plume{\textquoteright}s dynamics. To investigate this phenomenon more comprehensively, we introduce the concept of stochasticity in the dynamics, accounting for uncertainties in the underlying permeability of the reservoir. To enhance the accuracy of CO2 plume predictions and quantify the uncertainties involved, we utilize machine learning techniques to condition these predictions on time-lapse seismic and well observations. This framework works on the principle of sequential Bayesian inference that continuously assimilates information from time-lapse observations, updates the CO2 plume predictions, and characterizes uncertainties about the plumes.},
	keywords = {Bayesian inference, CCS, deep learning, Imaging, ML4SEISMIC, monitoring, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/herrmann2023ML4SEISMICmsc},
	author = {Abhinav Prakash Gahlot and Ting-ying Yu and Rafael Orozco and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@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}
}
@presentation {louboutin2022ML4SEISMIClew,
	title = {Learned extensions for wave-based simulation and inversion},
	year = {2022},
	month = {11},
	abstract = {We introduce a new method that explores velocities as an operator (extended velocities) for wave-equation based inversion. Through this extended formulation, we obtain the known benefits of working with subsurface offset volumes. The offset-dependence of these volumes has been studied in the linear case, i.e as part of extended Born scattering and extended least-squares reverse-time migration, but has been avoided for non-linear inversion due to computational concderns and challenges. By using techniques from randomized linear algebra, we will show that we can work with extended velocities for inversion while maintaining an acceptable computational cost much lower than solving one PDE per extended velocity model.},
	keywords = {deep learning, Imaging, ML4SEISMIC, SLIM, wave equation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/louboutin2022ML4SEISMIClew/index.html},
	author = {Mathias Louboutin and Yadhu Kartha and Rafael Orozco and Felix J. Herrmann}
}
@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}
}
