Sparse calibration of subsurface flow models using nonlinear orthogonal matching pursuit and an iterative stochastic ensemble method

Handle URI:
http://hdl.handle.net/10754/562785
Title:
Sparse calibration of subsurface flow models using nonlinear orthogonal matching pursuit and an iterative stochastic ensemble method
Authors:
Elsheikh, Ahmed H.; Wheeler, Mary Fanett; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
We introduce a nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of subsurface flow models. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated basis function with the residual from a large pool of basis functions. The discovered basis (aka support) is augmented across the nonlinear iterations. Once a set of basis functions are selected, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on stochastically approximated gradient using an iterative stochastic ensemble method (ISEM). In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm. The proposed algorithm is the first ensemble based algorithm that tackels the sparse nonlinear parameter estimation problem. © 2013 Elsevier Ltd.
KAUST Department:
Earth Science and Engineering Program; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Environmental Science and Engineering Program; Earth Fluid Modeling and Prediction Group
Publisher:
Elsevier BV
Journal:
Advances in Water Resources
Issue Date:
Jun-2013
DOI:
10.1016/j.advwatres.2013.02.002
Type:
Article
ISSN:
03091708
Appears in Collections:
Articles; Environmental Science and Engineering Program; Applied Mathematics and Computational Science 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.authorWheeler, Mary Fanetten
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-08-03T11:05:38Zen
dc.date.available2015-08-03T11:05:38Zen
dc.date.issued2013-06en
dc.identifier.issn03091708en
dc.identifier.doi10.1016/j.advwatres.2013.02.002en
dc.identifier.urihttp://hdl.handle.net/10754/562785en
dc.description.abstractWe introduce a nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of subsurface flow models. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated basis function with the residual from a large pool of basis functions. The discovered basis (aka support) is augmented across the nonlinear iterations. Once a set of basis functions are selected, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on stochastically approximated gradient using an iterative stochastic ensemble method (ISEM). In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm. The proposed algorithm is the first ensemble based algorithm that tackels the sparse nonlinear parameter estimation problem. © 2013 Elsevier Ltd.en
dc.publisherElsevier BVen
dc.subjectIterative stochastic ensemble methoden
dc.subjectOrthogonal matching pursuiten
dc.subjectParameter estimationen
dc.subjectSparse regularizationen
dc.subjectSubsurface flow modelsen
dc.titleSparse calibration of subsurface flow models using nonlinear orthogonal matching pursuit and an iterative stochastic ensemble methoden
dc.typeArticleen
dc.contributor.departmentEarth Science and Engineering Programen
dc.contributor.departmentApplied Mathematics and Computational Science 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.journalAdvances in Water Resourcesen
dc.contributor.institutionCenter for Subsurface Modeling (CSM), Institute for Computational Engineering and Sciences (ICES), University of Texas at Austin, TX, United Statesen
kaust.authorHoteit, Ibrahimen
kaust.authorElsheikh, Ahmed H.en
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