@conference {erdinc2025ML4SEISMICspm,
	title = {SAGE - Subsurface modeling with AI-driven Geostatistical Extraction and evaluation on North Sea Data},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Traditional machine learning based approaches often depend on large, high-quality and complete datasets of 2D Earth propety models, which can be difficult to obtain in subsurface applications. In this study, we leverage score-based generative models to synthesize high-fidelity geophysical properties (velocity, density, etc.) trained on a combination of sparse but tied well logs and seismic lines without relying on densely sampled velocity models and adress the sampling-density differences by enabling coherent integration of these complementary sources of information. With experiments on multiple synthetic datasets from subsurface models featuring diverse geological structures (e.g., faults, salt bodies), we demonstrate that our approach can accurately capture long-range geological correlations and align well with unseen ground-truth properties. Finally, we highlight the potential of our method to generalize to field data by conducting fine-tuning on a curated dataset derived from real well-log and seismic data currated from the UK Data Repository for the North Sea. This study represents an important step toward the development of foundational models for inverting physical properties and provides inputs for applications such as full-waveform inference (WISE/ASPIRE), supervised learning algorithms, and enhanced seismic-based subsurface modeling.},
	keywords = {Bayesian inference, deep learning, generative model, Imaging, Inverse problems, ML4SEISMIC, North-sea data, power-scaling, RTM, score, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/erdinc2025ML4SEISMICspm},
	author = {Huseyin Tuna Erdinc and Ipsita Bhar and Thales Souza and Rafael Orozco and Felix J. Herrmann}
}
@conference {bhar2025ML4SEISMICsdc,
	title = {Seismic Dataset Curation from UK National Data Repository to Validate SAGE and WISE},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {This work presents a curated seismic dataset pipeline developed for data residing in the UK National Data Repository (UK NDR) in support of advanced generative geophysical modeling and machine learning. The workflow integrates checkshots and 3D post-stack seismic data volumes to generate accurate depth-domain seismic sections. Custom interpolation algorithms are used to construct velocity models from checkshot data, which are then used for time-to-depth conversion with [OpendTect]https://dgbes.com/software/opendtect. The converted seismic cubes are subsequently processed in Python using [SegySAK]https://segysak.readthedocs.io/en/latest/, a python package, enabling efficient extraction and visualization of 2D lines. These datasets will be used to further validate the workflow of SAGE (Subsurface foundational model for AI-driven Geostatistical Extraction), which focuses on large-scale representation learning for the geosciences. The trained generative models will be used as input to WISE and ASPIRE that form foundation models for seismic inference designed to characterize the subsurface. This curated dataset pipeline streamlines seismic data preparation, enhances reproducibility, and bridges the gap between conventional geophysical workflows and emerging data-driven inference methods.},
	keywords = {Bayesian inference, generative model, Imaging, ML4SEISMIC, North-sea data, RTM, SLIM, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/bhar2025ML4SEISMICsdc},
	author = {Ipsita Bhar and Huseyin Tuna Erdinc and Thales Souza and Rafael Orozco and Felix J. Herrmann}
}
@presentation {orozco2024ML4SEISMICmev,
	title = {Machine-learning enabled velocity-model building with uncertainty quantification},
	year = {2024},
	month = {11},
	abstract = {Accurately characterizing subsurface properties is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional characterization methods such as Full-Waveform Inversion (FWI) represent powerful tools but often struggle with the inherent complexities of the inverse problem, including noise, limited bandwidth and aperture of data, limited azimuth and computational constraints. To address these challenges, we propose a scalable methodology that integrates generative modeling with physics-informed summary statistics, making it suitable for complicated imaging problems potentially including field datasets. Our approach leverages the power of conditional diffusion networks, and methodologically incorporates physics in the form of summary statistics, allowing for the computationally efficient generation of Bayesian posterior samples that offer an useful assessment of uncertainty of the inferred migration-velocity models. To validate our approach, we introduce a battery of tests that measure the quality of the image estimates as well as the quality of the inferred uncertainties. With modern synthetic datasets, we maximally leverage the advantages of using subsurface-offset Common Image Gathers (CIGs) as the conditioning observable. Next, we tackle the challenging SEAM salt model that requires incorporating salt flooding into our approach based on the iterative refinements of ASPIRE {\textemdash} Amortized posteriors with Summaries that are Physics-based and Iteratively REfined.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, diffusion models, FWI, Imaging, Inverse problems, ML4SEISMIC, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/orozco2024ML4SEISMICmev},
	author = {Rafael Orozco and Huseyin Tuna Erdinc and Thales Souza and Yunlin Zeng and Ziyi Yin and Felix J. Herrmann}
}
@presentation {orozco2022ML4SEISMICaos,
	title = {Adjoint operators as summary functions in amortized Bayesian inference frameworks},
	year = {2022},
	month = {11},
	abstract = {An important concern in seismic inverse problems is the large and varying size of observed data. The large size can cause computational cost concerns and its varying size (such as when changing receiver geometries) implies the need to rerun inference algorithms from scratch for each new observation. Motivated by these two problems, we take inspiration from the statistics literature which commonly relies on summary statistic of observed data. Summary statistic compress the observed data leaving only information needed for inference. In this work, we argue that the adjoint operator provides a natural candidate for a summary function in the context of physics-based inverse problems. We first mathematically show that for certain general assumptions transforming data under the adjoint operator defines a new conditional distribution which preserves the expectations of the original posterior. We validate our hypothesis by evaluating our framework in a learned amortized inference algorithm. Our seismic and medical synthetic experiments show computational gains and increased quality of point estimates using our framework. We discuss statistical metrics that show our learned posterior is well calibrated therefore justifying its use in uncertainty quantification.},
	keywords = {Amortized Inference, Bayesian inference, deep learning, Inverse problems, machine learning, medical imaging, ML4SEISMIC, Normalizing flows, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/orozco2022ML4SEISMICaos/index.html},
	author = {Rafael Orozco and Mathias Louboutin and Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
@presentation {yin2022ML4SEISMICavc,
	title = {Amortized velocity continuation with Fourier neural operators},
	year = {2022},
	month = {11},
	abstract = {Velocity continuation aims to map the migration image using one background model to the image using another background model. It is of great importance to quantify the uncertainty in seismic imaging result from various background models. With Fourier neural operators as a learned surrogate, this continuation from a given background model to an unseen background model can be quite accurately estimated with near-zero cost. However, the limitation of the prior art is that the input background model and the survey area are assumed to be fixed. The main contribution of this work is to extend the Fourier neural operator surrogate to be amortized over different given background models and survey areas. We verify the effectiveness of our learned surrogates by a realistic example on different areas of Parihaka dataset against different background models.},
	keywords = {Fourier neural operators, ML4SEISMIC, SLIM, Uncertainty quantification, velocity continuation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICavc/index.html},
	author = {Ali Siahkoohi and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {siahkoohi2022ML4SEISMICluq,
	title = {Low-cost uncertainty quantification for large-scale inverse problems},
	year = {2022},
	month = {11},
	abstract = {Bayesian inference for large-scale inverse problems is challenged by the computationally costly forward operator evaluations during posterior distribution sampling. Recent advances in variational inference and deep learning reduce these costs by pretraining a neural network capable of sampling the posterior distribution for previously unseen observed data. In geophysical applications, however, the accuracy of these methods depends on sufficiently capturing subsurface variability through a training dataset, which is challenging given the heterogeneity of the Earth{\textquoteright}s subsurface and our lack of access to it. Moreover, these methods may be unreliable in the presence of data distribution shifts, e.g., a change in the number of source experiments, noise distribution, or geological features to be imaged. As such, we present a solution that increases the robustness of deep-learning-based Bayesian inference approaches when faced with changes in data distribution. Our proposed method involves a physics-based adaptation to the latent distribution of a conditional normalizing flow that is pretrained to approximate the posterior distribution for previously unseen data. Instead of feeding standard Gaussian latent samples to the conditional normalizing flow, this method parameterizes the latent distribution by a Gaussian distribution with an unknown mean and diagonal covariance, estimated by minimizing the Kullback-Leibler divergence between predicted and true posterior distributions. This method is applicable to a wide range of inverse problems and has the potential to significantly reduce the costs of Bayesian variational inference. By means of a realistic seismic imaging example we demonstrate that the proposed latent distribution adaptation method mitigates the Bayesian inference errors induced by data distribution shifts, including shifts in the forward model and prior distribution.},
	keywords = {Inverse problems, ML4SEISMIC, Normalizing flows, seismic imaging, SLIM, Uncertainty quantification, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/siahkoohi2022ML4SEISMICluq/siahkoohi2022ML4SEISMICluq_pres.pdf},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Felix J. Herrmann}
}
@presentation {zhang2022ML4SEISMICtss,
	title = {Time-lapse seismic survey design by maximizing the spectral gap},
	year = {2022},
	month = {11},
	abstract = {While time-lapse seismic has been applied successfully to CO2 sequestration monitoring, it remains a challenging problem since replicated dense surveys come at a very high cost in the field. Wavefield reconstruction based on matrix completion (MC) from randomized subsampled data is an efficient way to reduce operational costs. This technique allows for accurate time-lapse reconstruction by employing the joint recovery model (JRM), which capitalizes on the fact that different vintages share a common component. However, combining JRM with optimal time-lapse acquisition survey design remains an unexplored area of research. In expander graph theory, spectral gap (SG) reveals the source-receiver layout connectivity and is related to reconstruction quality during MC. Building on these insights, we proposed a simulation free time-lapse survey design based on JRM that aims to get similar reconstructed quality without insisting on replicate surveys, which significantly reduces the cost in the field. This approach uses the simulated annealing algorithm to find subsampling masks for each vintage. Numerical experiments confirm a direct correlation between increased spectral gap and promising time-lapse reconstruction quality.},
	keywords = {Acquisition, JRM, matrix factorization, ML4SEISMIC, SLIM, spectral gap, survey design, time-lapse, wavefield reconstruction},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/zhang2022ML4SEISMICtss/index.html},
	author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar and Oscar Lopez and Felix J. Herrmann}
}
@presentation {yin2022ML4SEISMICutc,
	title = {Uncertainty-aware time-lapse CO$_2$ monitoring with learned end-to-end inversion},
	year = {2022},
	month = {11},
	abstract = {Seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics modeling and wave physics and differentiation through the solvers with respect to the subsurface properties of interest. In this talk, we demonstrate the effectiveness of learned coupled inversion framework using a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator, which greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. We study the effectiveness and correctness of inversion based on Fourier neural operator surrogate and a normalizing flow prior. We also demonstrate the efficacy of this framework on monitoring the growth of CO2 plumes during sequestration, and on uncertainty quantification of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can further forecast the CO2 plume in the future without any acquired seismic data with uncertainty estimation.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, Fourier neural operators, GCS, inversion, machine learning, ML4SEISMIC, Normalizing flows, SLIM, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2022/yin2022ML4SEISMICutc/index.html},
	author = {Ziyi Yin and Rafael Orozco and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {siahkoohi2021ML4SEISMICmcn,
	title = {Multifidelity conditional normalizing flows for physics-guided Bayesian inference},
	year = {2021},
	month = {11},
	abstract = {We introduce a scalable Bayesian inference approach that combines techniques from deep learning with a physic-based variational inference formulation. Bayesian inference for ill-posed inverse problems is challenged by the high-dimensionality of the unknown, computationally expensive forward operator, and choosing a prior distribution that accurately encodes prior knowledge on the unknown. To handle this situation and to assess uncertainty, we propose to approximate the posterior distribution using a pretrained conditional normalizing flow, which is trained on existing low- and high-fidelity estimations of the unknown. To further improve the accuracy of this approximation, we use transfer learning and finetune this normalizing flow by minimizing the Kullback-Leibler divergence between the predicted and the desired high-fidelity posterior density. This amounts to minimizing a physic-based variational inference objective with respect to the network weights, which we believe might scale better than Bayesian inference with Markov Chain sampling methods. We apply the proposed Bayesian inference approach to seismic imaging where we use quasi-real data obtained from the Parihaka dataset.},
	keywords = {deep learning, ML4SEISMIC, Normalizing flows, seismic imaging, SLIM, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/siahkoohi2021ML4SEISMICmcn/Mon-11-45-Siahkoohi.pdf},
	author = {Ali Siahkoohi and Rafael Orozco and Gabrio Rizzuti and Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann}
}
@presentation {siahkoohi2021ML4SEISMICuqi,
	title = {Uncertainty quantification in imaging and automatic horizon tracking{\textemdash}a Bayesian deep-prior based approach},
	year = {2021},
	month = {11},
	abstract = {In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, typically constitutes only the first stage of a sequential workflow, and UQ becomes even more relevant when applied to subsequent tasks that are highly sensitive to the inversion outcome. In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result. As such, the main contribution of this work consists in a data-guided approach to horizon tracking uncertainty analysis. This work is fundamentally based on a special reparameterization of reflectivity, known as {\textquotedblleft}deep prior{\textquotedblright}. Feasible models are restricted to the output of a convolutional neural network with a fixed input, while weights and biases are Gaussian random variables. Given a deep prior model, the network parameters are sampled from the posterior distribution via a Markov chain Monte Carlo method, from which the conditional mean and point-wise standard deviation of the inferred reflectivities are approximated. For each sample of the posterior distribution, a reflectivity is generated, and the horizons are tracked automatically. In this way, uncertainty on model parameters naturally translates to horizon tracking. As part of the validation for the proposed approach, we verified that the estimated confidence intervals for the horizon tracking coincide with geologically complex regions, such as faults.},
	keywords = {horizon picking, Imaging, machine learning, ML4SEISMIC, SLIM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/siahkoohi2021ML4SEISMICuqi/Mon-11-20-Siahkoohi.pdf},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
@presentation {orozco2021ML4SEISMICvia,
	title = {Variational inference for artifact removal of adjoint solutions in photoacoustic problems},
	year = {2021},
	month = {11},
	abstract = {Photoacoustic is a medical imaging modality which combines light and ultrasound waves to image internal structures of biological tissue. The inverse problem reconstructs the tissue initially excited by light given propagated ultrasound data at receivers outside the tissue. Due to noisy, limited-view and sparse receiver data, traditional time-reversal adjoint solutions are highly ill-posed. This necessitates uncertainty quantification to communicate to practitioners which areas of the image can be trusted. We propose a framework which leverages a machine learning based method (Conditional Normalizing Flows) to learn the full posterior distribution of viable solutions given the time-reversal adjoint solution. We show that areas of calculated uncertainty correlate with structures that are known to be difficult to image. In addition, we also propose a MAP based solution, which solves the variational least-squares problem while using the trained Conditional Normalizing Flow as a prior distribution.},
	keywords = {conditional prior, deep image, MAP, ML4SEISMIC, normalizing flow, Photoacoustic, SLIM, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/orozco2021ML4SEISMICvia/Tue-09-00-Orozco.html},
	author = {Rafael Orozco and Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
