Efficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogates

Handle URI:
http://hdl.handle.net/10754/563367
Title:
Efficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogates
Authors:
Elsheikh, Ahmed H.; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Wheeler, Mary Fanett
Abstract:
An efficient Bayesian calibration method based on the nested sampling (NS) algorithm and non-intrusive polynomial chaos method is presented. Nested sampling is a Bayesian sampling algorithm that builds a discrete representation of the posterior distributions by iteratively re-focusing a set of samples to high likelihood regions. NS allows representing the posterior probability density function (PDF) with a smaller number of samples and reduces the curse of dimensionality effects. The main difficulty of the NS algorithm is in the constrained sampling step which is commonly performed using a random walk Markov Chain Monte-Carlo (MCMC) algorithm. In this work, we perform a two-stage sampling using a polynomial chaos response surface to filter out rejected samples in the Markov Chain Monte-Carlo method. The combined use of nested sampling and the two-stage MCMC based on approximate response surfaces provides significant computational gains in terms of the number of simulation runs. The proposed algorithm is applied for calibration and model selection of subsurface flow models. © 2013.
KAUST Department:
Earth Science and Engineering Program; Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier BV
Journal:
Computer Methods in Applied Mechanics and Engineering
Issue Date:
Feb-2014
DOI:
10.1016/j.cma.2013.11.001
Type:
Article
ISSN:
00457825
Appears in Collections:
Articles; Environmental Science and Engineering Program; Physical Sciences and Engineering (PSE) Division; Earth Science and Engineering Program

Full metadata record

DC FieldValue Language
dc.contributor.authorElsheikh, Ahmed H.en
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorWheeler, Mary Fanetten
dc.date.accessioned2015-08-03T11:46:49Zen
dc.date.available2015-08-03T11:46:49Zen
dc.date.issued2014-02en
dc.identifier.issn00457825en
dc.identifier.doi10.1016/j.cma.2013.11.001en
dc.identifier.urihttp://hdl.handle.net/10754/563367en
dc.description.abstractAn efficient Bayesian calibration method based on the nested sampling (NS) algorithm and non-intrusive polynomial chaos method is presented. Nested sampling is a Bayesian sampling algorithm that builds a discrete representation of the posterior distributions by iteratively re-focusing a set of samples to high likelihood regions. NS allows representing the posterior probability density function (PDF) with a smaller number of samples and reduces the curse of dimensionality effects. The main difficulty of the NS algorithm is in the constrained sampling step which is commonly performed using a random walk Markov Chain Monte-Carlo (MCMC) algorithm. In this work, we perform a two-stage sampling using a polynomial chaos response surface to filter out rejected samples in the Markov Chain Monte-Carlo method. The combined use of nested sampling and the two-stage MCMC based on approximate response surfaces provides significant computational gains in terms of the number of simulation runs. The proposed algorithm is applied for calibration and model selection of subsurface flow models. © 2013.en
dc.publisherElsevier BVen
dc.subjectLeast Angle Regressionen
dc.subjectNested samplingen
dc.subjectPolynomial chaos expansionen
dc.subjectSparsity promoting regularizationen
dc.subjectSubsurface flow modelsen
dc.titleEfficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogatesen
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentEnvironmental Science and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.identifier.journalComputer Methods in Applied Mechanics and Engineeringen
dc.contributor.institutionCenter for Subsurface Modeling (CSM), Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX, United Statesen
dc.contributor.institutionInstitute of Petroleum Engineering (IPE), Heriot-Watt University, Edinburgh Campus, Edinburgh, EH14 1AS, Scotland, United Kingdomen
kaust.authorHoteit, Ibrahimen
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