Approximate Langevin Monte Carlo with Adaptation for Bayesian Full-Waveform Inversion

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we present a proof of concept for Bayesian full-waveform inversion (FWI) in 2-D. This is based on approximate Langevin Monte Carlo sampling with a gradient-based adaptation of the posterior distribution. We apply our method to the Marmousi model, and it reliably recovers important aspects of the posterior, including the statistical moments, and 1-D and 2-D marginals. Depending on the variations of seismic velocities, the posterior can be significantly non-Gaussian, which directly suggest that using a Hessian approximation for uncertainty quantification in FWI may not be sufficient.
Original languageEnglish (US)
Title of host publication82nd EAGE Annual Conference & Exhibition
PublisherEuropean Association of Geoscientists & Engineers
DOIs
StatePublished - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-10-05
Acknowledgements: The first author would like to thank Tristan van Leeuwen at Utrecht University for visiting his research lab, which led to this work, and his continuous support. The research visits and the work reported here were supported by funding from King Abdullah University of Science and Technology (KAUST).

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