Subsurface uncertainty quantification with deep geologic priors: A variational Bayesian framework
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image2022-3750132.1.pdf
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Published version
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742.79 KB
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Author(s)
Tewari, Ashutosh
Wheelock, Brent
Clark, Jacob
Foster, Dallas
Li, Matthew
Marzouk, Youssef
Date Issued
August 8, 2022
Journal
Second International Meeting for Applied Geoscience & Energy
Publisher
Society of Exploration Geophysicists and American Association of Petroleum Geologists
Citation
Ashutosh Tewari, Brent Wheelock, Jacob Clark, Dallas Foster, Matthew Li, Youssef Marzouk; August 1, 2022. "Subsurface uncertainty quantification with deep geologic priors: A variational Bayesian framework." Proceedings of the Second International Meeting for Applied Geoscience & Energy. Second International Meeting for Applied Geoscience & Energy. Houston, Texas. (pp. pp. 1745-1749). ASME.
Version
Final published version
Abstract
Recent years have seen surging interest in the area of geophysical inversion and uncertainty quantification (UQ) using data-driven priors for geologic domains. In particular, deep generative models, e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs), have been employed in a number of studies to either regularize deterministic sub-surface inversion, or to act as prior distributions for Bayesian inversion. These models offer a low-dimensional representation of high-dimensional property fields, by capturing intrinsic structural features observed in nature, thus providing a common prior for the concomitant assimilation of data of different modalities such as seismic, fluid flow, electromagnetism, gravity etc. On one hand, this low-dimensionality is an important enabler for Bayesian methods, which are generally deemed impractical for industrial-scale subsurface UQ applications. On the other hand, the computational burden of evaluating expensive forward models and the non-convexity/multi-modality of the resulting inverse problems continue to present a formidable challenge for Bayesian methods. Towards the goal of scalable subsurface Bayesian inversion, we present a framework that considerably mitigates the aforementioned challenges of expensive physics and complex posteriors. The proposed framework exploits the low-dimensionality provided by deep generative priors, and approximates the posterior using deep mixture models via variational inference. The efficacy of our proposal is demonstrated on a joint inverse problem employing gravity and seismic data within a synthetic testbed.
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DOI of Published Version
https://doi.org/10.1190/image2022-3750132.1
