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dc.contributor.authorEl Gharamti, Mohamad
dc.contributor.authorAit-El-Fquih, Boujemaa
dc.contributor.authorHoteit, Ibrahim
dc.date.accessioned2015-05-17T20:31:04Z
dc.date.available2015-05-17T20:31:04Z
dc.date.issued2015-05-11
dc.identifier.citationAn Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Models 2015 Journal of Hydrology
dc.identifier.issn00221694
dc.identifier.doi10.1016/j.jhydrol.2015.05.004
dc.identifier.urihttp://hdl.handle.net/10754/553012
dc.description.abstractThe ensemble Kalman filter (EnKF) is a popular method for state-parameters estimation of subsurface flow and transport models based on field measurements. The common filtering procedure is to directly update the state and parameters as one single vector, which is known as the Joint-EnKF. In this study, we follow the one-step-ahead smoothing formulation of the filtering problem, to derive a new joint-based EnKF which involves a smoothing step of the state between two successive analysis steps. The new state-parameters estimation scheme is derived in a consistent Bayesian filtering framework and results in separate update steps for the state and the parameters. This new algorithm bears strong resemblance with the Dual-EnKF, but unlike the latter which first propagates the state with the model then updates it with the new observation, the proposed scheme starts by an update step, followed by a model integration step. We exploit this new formulation of the joint filtering problem and propose an efficient model-integration-free iterative procedure on the update step of the parameters only for further improved performances. Numerical experiments are conducted with a two-dimensional synthetic subsurface transport model simulating the migration of a contaminant plume in a heterogenous aquifer domain. Contaminant concentration data are assimilated to estimate both the contaminant state and the hydraulic conductivity field. Assimilation runs are performed under imperfect modeling conditions and various observational scenarios. Simulation results suggest that the proposed scheme efficiently recovers both the contaminant state and the aquifer conductivity, providing more accurate estimates than the standard Joint and Dual EnKFs in all tested scenarios. Iterating on the update step of the new scheme further enhances the proposed filter’s behavior. In term of computational cost, the new Joint-EnKF is almost equivalent to that of the Dual-EnKF, but requires twice more model integrations than the standard Joint-EnKF.
dc.publisherElsevier BV
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S002216941500339X
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Hydrology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Hydrology, 11 May 2015. DOI: 10.1016/j.jhydrol.2015.05.004
dc.subjectState-parameter estimation
dc.subjectSubsurface contaminant transport
dc.subjectEnsemble Kalman filter
dc.subjectOne-step-ahead smoothing
dc.subjectBayesian estimation
dc.titleAn Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Models
dc.typeArticle
dc.contributor.departmentEarth Fluid Modeling and Prediction Group
dc.contributor.departmentEarth Science and Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalJournal of Hydrology
dc.eprint.versionPost-print
dc.contributor.institutionNansen Environmental and Remote Sensing Center (NERSC), Bergen, Norway
kaust.personEl Gharamti, Mohamad
kaust.personHoteit, Ibrahim
kaust.personAit-El-Fquih, Boujemaa
refterms.dateFOA2017-05-11T00:00:00Z
dc.date.published-online2015-05-11
dc.date.published-print2015-08


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