Low-rank Kalman filtering for efficient state estimation of subsurface advective contaminant transport models

Handle URI:
http://hdl.handle.net/10754/562146
Title:
Low-rank Kalman filtering for efficient state estimation of subsurface advective contaminant transport models
Authors:
El Gharamti, Mohamad ( 0000-0002-7229-8366 ) ; Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Sun, Shuyu ( 0000-0002-3078-864X )
Abstract:
Accurate knowledge of the movement of contaminants in porous media is essential to track their trajectory and later extract them from the aquifer. A two-dimensional flow model is implemented and then applied on a linear contaminant transport model in the same porous medium. Because of different sources of uncertainties, this coupled model might not be able to accurately track the contaminant state. Incorporating observations through the process of data assimilation can guide the model toward the true trajectory of the system. The Kalman filter (KF), or its nonlinear invariants, can be used to tackle this problem. To overcome the prohibitive computational cost of the KF, the singular evolutive Kalman filter (SEKF) and the singular fixed Kalman filter (SFKF) are used, which are variants of the KF operating with low-rank covariance matrices. Experimental results suggest that under perfect and imperfect model setups, the low-rank filters can provide estimates as accurate as the full KF but at much lower computational effort. Low-rank filters are demonstrated to significantly reduce the computational effort of the KF to almost 3%. © 2012 American Society of Civil Engineers.
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 Sciences and Engineering Program; Earth Fluid Modeling and Prediction Group; Computational Transport Phenomena Lab
Publisher:
American Society of Civil Engineers
Journal:
Journal of Environmental Engineering
Issue Date:
Apr-2012
DOI:
10.1061/(ASCE)EE.1943-7870.0000484
Type:
Article
ISSN:
07339372
Sponsors:
This publication utilized work supported in part by funds from the KAUST GCR Collaborative Fellow program.
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; Computational Transport Phenomena Lab

Full metadata record

DC FieldValue Language
dc.contributor.authorEl Gharamti, Mohamaden
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorSun, Shuyuen
dc.date.accessioned2015-08-03T09:45:52Zen
dc.date.available2015-08-03T09:45:52Zen
dc.date.issued2012-04en
dc.identifier.issn07339372en
dc.identifier.doi10.1061/(ASCE)EE.1943-7870.0000484en
dc.identifier.urihttp://hdl.handle.net/10754/562146en
dc.description.abstractAccurate knowledge of the movement of contaminants in porous media is essential to track their trajectory and later extract them from the aquifer. A two-dimensional flow model is implemented and then applied on a linear contaminant transport model in the same porous medium. Because of different sources of uncertainties, this coupled model might not be able to accurately track the contaminant state. Incorporating observations through the process of data assimilation can guide the model toward the true trajectory of the system. The Kalman filter (KF), or its nonlinear invariants, can be used to tackle this problem. To overcome the prohibitive computational cost of the KF, the singular evolutive Kalman filter (SEKF) and the singular fixed Kalman filter (SFKF) are used, which are variants of the KF operating with low-rank covariance matrices. Experimental results suggest that under perfect and imperfect model setups, the low-rank filters can provide estimates as accurate as the full KF but at much lower computational effort. Low-rank filters are demonstrated to significantly reduce the computational effort of the KF to almost 3%. © 2012 American Society of Civil Engineers.en
dc.description.sponsorshipThis publication utilized work supported in part by funds from the KAUST GCR Collaborative Fellow program.en
dc.publisherAmerican Society of Civil Engineersen
dc.subjectAquifersen
dc.subjectContaminant transporten
dc.subjectKalman filteren
dc.subjectKalman filtersen
dc.subjectLow rank Kalman filteringen
dc.subjectPollutantsen
dc.subjectSingular evolutive Kalman filteren
dc.subjectState estimationen
dc.titleLow-rank Kalman filtering for efficient state estimation of subsurface advective contaminant transport modelsen
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 Sciences and Engineering Programen
dc.contributor.departmentEarth Fluid Modeling and Prediction Groupen
dc.contributor.departmentComputational Transport Phenomena Laben
dc.identifier.journalJournal of Environmental Engineeringen
kaust.authorEl Gharamti, Mohamaden
kaust.authorHoteit, Ibrahimen
kaust.authorSun, Shuyuen
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