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Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models

Efendiev, Y. and Hou, T. and Luo, W. (2006) Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models. SIAM Journal on Scientific Computing, 28 (2). pp. 776-803. ISSN 1064-8275. https://resolver.caltech.edu/CaltechAUTHORS:EFEsiamjsc06

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Abstract

We study the preconditioning of Markov chain Monte Carlo (MCMC) methods using coarse-scale models with applications to subsurface characterization. The purpose of preconditioning is to reduce the fine-scale computational cost and increase the acceptance rate in the MCMC sampling. This goal is achieved by generating Markov chains based on two-stage computations. In the first stage, a new proposal is first tested by the coarse-scale model based on multiscale finite volume methods. The full fine-scale computation will be conducted only if the proposal passes the coarse-scale screening. For more efficient simulations, an approximation of the full fine-scale computation using precomputed multiscale basis functions can also be used. Comparing with the regular MCMC method, the preconditioned MCMC method generates a modified Markov chain by incorporating the coarse-scale information of the problem. The conditions under which the modified Markov chain will converge to the correct posterior distribution are stated in the paper. The validity of these assumptions for our application and the conditions which would guarantee a high acceptance rate are also discussed. We would like to note that coarse-scale models used in the simulations need to be inexpensive but not necessarily very accurate, as our analysis and numerical simulations demonstrate. We present numerical examples for sampling permeability fields using two-point geostatistics. The Karhunen--Loève expansion is used to represent the realizations of the permeability field conditioned to the dynamic data, such as production data, as well as some static data. Our numerical examples show that the acceptance rate can be increased by more than 10 times if MCMC simulations are preconditioned using coarse-scale models.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1137/050628568DOIUNSPECIFIED
ORCID:
AuthorORCID
Efendiev, Y.0000-0001-9626-303X
Additional Information:©2006 Society for Industrial and Applied Mathematics Received by the editors April 5, 2005; accepted for publication (in revised form) January 13, 2006; published electronically May 26, 2006. The research of the second author was partially supported by NSF ITR grant ACI-0204932 and NSF FRG grant DMS-0353838. The authors would like to thank the referees for valuable comments and suggestions and Victor Ginting for his help in preparing this manuscript.
Subject Keywords:preconditioning; multiscale; Markov chain Monte Carlo; porous media
Issue or Number:2
Record Number:CaltechAUTHORS:EFEsiamjsc06
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:EFEsiamjsc06
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:4466
Collection:CaltechAUTHORS
Deposited By: Archive Administrator
Deposited On:24 Aug 2006
Last Modified:02 Oct 2019 23:13

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