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 language | English |
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| Title of host publication | Conference Proceedings, 82nd EAGE Annual Conference & Exhibition |
| Place of Publication | Amsterdam |
| Publisher | European Association of Geoscientists and Engineers, EAGE |
| Number of pages | 4 |
| Volume | 2020 |
| Edition | July |
| DOIs | |
| Publication status | Published - Jul 2020 |