Bayesian uncertainty estimation for full waveform inversion: A numerical study

Muhammad Izzatullah, Tristan van Leeuwen, Daniel Peter

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

Abstract

Full waveform inversion enables us to obtain high-resolution subsurface images. However, estimating the associated uncertainties is not trivial. Hessian-based method gives us an opportunity to assess the uncertainties around a given estimate based on the inverse of the Hessian, evaluated at that estimate. In this work we study various algorithms for extracting information from this inverse Hessian based on a low-rank approximation. In particular, we compare the Lanczos method to the randomized singular value decomposition. We demonstrate that the low-rank approximation may lead to a biased conclusion.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2019
PublisherSociety of Exploration Geophysicists
Pages1685-1689
Number of pages5
DOIs
StatePublished - Aug 10 2019

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