2021-10-18
Langevin dynamics Markov Chain Monte Carlo solution for seismic inversion
Publication
Publication
Presented at the
82nd EAGE Annual Conference & Exhibition (October 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.
| Additional Metadata | |
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| doi.org/10.3997/2214-4609.202010496 | |
| 82nd EAGE Annual Conference & Exhibition | |
| Organisation | Computational Imaging |
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Izzatullah, M., van Leeuwen, T., & Peter, D. (2021). 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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