A new MCMC algorithm for seismic parameter estimation and uncertainty analysis

Abstract

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2007A new MCMC algorithm for seismic parameter estimation and uncertainty analysisAuthors: Tiancong HongMrinal K. SenTiancong HongInstitute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78758, USASearch for more papers by this author and Mrinal K. SenInstitute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78758, USASearch for more papers by this authorhttps://doi.org/10.1190/1.2792858 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract It is superior to formulate an inverse problem in a Bayesian framework and fully solve it by stochastically constructing the posterior probability density function or PPD surface using MCMC (Monte Carlo Markov Chain) algorithms. The estimated PPD can also be used to compute several measures of dispersion in model space. However, for many realistic applications, MCMC can be computationally expensive and cheap measures may lead to inaccurate PPD estimation as well as uncertainty analysis due to the strong nonlinearity and high dimensionality. In this paper, to address the fundamental issues of efficiency and accuracy in parameter estimation and PPD sampling, we incorporate some new developments into a standard Genetic Algorithm (GA) to design a more powerful Markov Chain Monte Carlo (MCMC) algorithm ‐ a multi‐scale GA based MCMC, for practical geophysical inverse problems. Multiple MCMC chains of different scales are run simultaneously in parallel in this new method. To gain the benefits of both the faster convergence of the coarse scale and the greater detail of the fine scale, realizations of chains on different scales are combined for intelligent proposals that facilitate exploration of the model space on the fine scale. In this study, this new MCMC is demonstrated using an analytical example and its performance on PPD estimation and uncertainty quantification is evaluated using a nonlinear seismic inverse problem. We find multi‐scaling to be particularly attractive in addressing model parameterization issue especially for seismic waveform inversion.Permalink: https://doi.org/10.1190/1.2792858FiguresReferencesRelatedDetailsCited ByAdaptive multiscale MCMC algorithm for uncertainty quantification in seismic parameter estimationXiaosi Tan*, Richard L. Gibson, Wing Tat Leung, and Yalchin Efendiev5 August 2014A new stochastic inference method for inversion of pre‐stack seismic dataYang Xue, Mrinal K. Sen, and Zhiwen Deng25 May 2012Quantification of uncertainty in velocity log upscaling by a Markov Chain Monte Carlo methodRichard L. Gibson and Kyubum Hwang14 October 2009Joint Bayesian inversion for reservoir characterization and uncertainty quantificationTiancong Hong and Mrinal K. Sen15 December 2008 SEG Technical Program Expanded Abstracts 2007ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2007 Pages: 3124 publication data© 2007 Copyright © 2007 Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 14 Sep 2007 CITATION INFORMATION Tiancong Hong and Mrinal K. Sen, (2007), "A new MCMC algorithm for seismic parameter estimation and uncertainty analysis," SEG Technical Program Expanded Abstracts : 1883-1887. https://doi.org/10.1190/1.2792858 Plain-Language Summary PDF DownloadLoading ...