Convergence Analysis of MCMC Methods for Subsurface Flow Problems

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dc.contributor.authorMamun, Abdullah-al
dc.contributor.authorPereira, Felipe
dc.contributor.authorRahunanthan, A.
dc.contributor.utdAuthorMamun, Abdullah-al
dc.contributor.utdAuthorPereira, Felipe
dc.date.accessioned2019-07-02T20:08:32Z
dc.date.available2019-07-02T20:08:32Z
dc.date.created2018-07-04
dc.descriptionFull text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided link to the article). Non UTD affiliates will find the web address for this item by clicking the Show full item record link and copying the "relation.uri" metadata.
dc.description.abstractIn subsurface characterization using a history matching algorithm subsurface properties are reconstructed with a set of limited data. Here we focus on the characterization of the permeability field in an aquifer using Markov Chain Monte Carlo (MCMC) algorithms, which are reliable procedures for such reconstruction. The MCMC method is serial in nature due to its Markovian property. Moreover, the calculation of the likelihood information in the MCMC is computationally expensive for subsurface flow problems. Running a long MCMC chain for a very long period makes the method less attractive for the characterization of subsurface. In contrast, several shorter MCMC chains can substantially reduce computation time and can make the framework more suitable to subsurface flows. However, the convergence of such MCMC chains should be carefully studied. In this paper, we consider multi-MCMC chains for a single–phase flow problem and analyze the chains aiming at a reliable characterization.
dc.description.departmentSchool of Natural Sciences and Mathematics
dc.description.sponsorshipNational Science Foundation under Grant Nos DMS 1514808, HRD 1600818.
dc.identifier.bibliographicCitationMamun, A., F. Pereira, and A. Rahunanthan. 2018. "Convergence analysis of MCMC methods for subsurface flow problems." Lecture Notes In Computer Science 10961: 305-317 doi:10.1007/978-3-319-95165-2_22
dc.identifier.isbn9783319951645
dc.identifier.urihttps://hdl.handle.net/10735.1/6673
dc.identifier.volume10961
dc.language.isoen
dc.publisherSpringer Verlag
dc.relation.isPartOfLecture Notes In Computer Science
dc.relation.urihttp://dx.doi.org/10.1007/978-3-319-95165-2_22
dc.rights©2018 Springer International Publishing AG, part of Springer Nature
dc.subjectNumerical analysis--Acceleration of convergence
dc.subjectMarkov processes
dc.subjectAquifers--Mathematical models
dc.subjectHidden Markov models
dc.subjectStochastic models
dc.subjectMonte Carlo method
dc.subjectPermeable reactive barriers
dc.subjectAlgorithms
dc.titleConvergence Analysis of MCMC Methods for Subsurface Flow Problems
dc.type.genrearticle

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