2020-06-01
Langevin dynamics Markov Chain Monte Carlo solution for seismic inversion
Publication
Publication
Presented at the
EAGE Conference and Exhibition (June 2021), Amsterdam, the Netherlands
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.
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| doi.org/10.3997/2214-4609.202010496 | |
| EAGE Conference and Exhibition | |
| Organisation | Computational Imaging |
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Izzatullah, M, van Leeuwen, T, & Peter, D. (2020). Langevin dynamics Markov Chain Monte Carlo solution for seismic inversion. In 82nd EAGE Annual Conference & Exhibition (pp. 1–5). doi:10.3997/2214-4609.202010496
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