TY - GEN
T1 - LANGEVIN DYNAMICS MARKOV CHAIN MONTE CARLO SOLUTION FOR SEISMIC INVERSION
AU - Izzatullah, Muhammad
AU - Van Leeuwen, T.
AU - Peter, Daniel
N1 - KAUST Repository Item: Exported on 2022-04-29
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/676602
UR - https://www.earthdoc.org/content/papers/10.3997/2214-4609.202010496
UR - http://www.scopus.com/inward/record.url?scp=85127822743&partnerID=8YFLogxK
U2 - 10.3997/2214-4609.202010496
DO - 10.3997/2214-4609.202010496
M3 - Conference contribution
SN - 9781713841449
SP - 486
EP - 490
BT - 82nd EAGE Annual Conference & Exhibition
PB - European Association of Geoscientists & Engineers
ER -