Posterior sampling with CNN-based, Plug-and-Play regularization with applications to Post-Stack Seismic Inversion
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Published:2023-12-20
Issue:
Volume:
Page:1-73
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ISSN:0016-8033
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Container-title:GEOPHYSICS
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language:en
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Short-container-title:GEOPHYSICS
Author:
Izzatullah Muhammad1,
Alkhalifah Tariq1,
Romero Juan1,
Corrales Miguel1,
Luiken Nick1,
Ravasi Matteo1
Affiliation:
1. King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia..
Abstract
Uncertainty quantification is a crucial component in any geophysical inverse problem, as it provides decision-makers with valuable information about the inversion results. Seismic inversion is a notoriously ill-posed inverse problem, due to the band-limited and noisy nature of seismic data; as such, quantifying the uncertainties associated with the ill-posed nature of this inversion process is essential for qualifying the subsequent interpretation and decision-making processes. Selecting appropriate prior information is a crucialyet, non-trivialstep in probabilistic inversion since it influences the ability of sampling-based inference algorithms to provide geologically-plausible posterior samples. However, the necessity to encapsulate prior knowledge into a probability distribution can greatly limit our ability to define expressive priors. To address this limitation, and following in the footsteps of the Plug-and-Play methodology for deterministic inversion, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence lossa measure of the distance between the approximated and target probabilistic distributionswith a CNN-based denoiser. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability to produce high-resolution, trustworthy samples that realistically represent subsurface structures. The proposed method is validated on both synthetic and field post-stack seismic data.
Publisher
Society of Exploration Geophysicists
Subject
Geochemistry and Petrology,Geophysics