LANGEVIN DYNAMICS MARKOV CHAIN MONTE CARLO SOLUTION FOR SEISMIC INVERSION

Muhammad Izzatullah, T. Van Leeuwen, Daniel Peter

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

Abstract

In this abstract, we review the gradient-based Markov Chain Monte Carlo (MCMC) and demonstrate its applicability in inferring the uncertainty in seismic inversion. There are many flavours of gradient-based MCMC; here, we will only focus on the Unadjusted Langevin algorithm (ULA) and Metropolis-Adjusted Langevin algorithm (MALA). We propose an adaptive step-length based on the Lipschitz condition within ULA to automate the tuning of step-length and suppress the Metropolis-Hastings acceptance step in MALA. We consider the linear seismic travel-time tomography problem as a numerical example to demonstrate the applicability of both methods.
Original languageEnglish (US)
Title of host publication82nd EAGE Annual Conference & Exhibition
PublisherEuropean Association of Geoscientists & Engineers
Pages486-490
Number of pages5
ISBN (Print)9781713841449
DOIs
StatePublished - 2021

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