An Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Models

Handle URI:
http://hdl.handle.net/10754/553012
Title:
An Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Models
Authors:
Gharamti, M. E.; Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
The 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.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
An Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Models 2015 Journal of Hydrology
Publisher:
Elsevier BV
Journal:
Journal of Hydrology
Issue Date:
11-May-2015
DOI:
10.1016/j.jhydrol.2015.05.004
Type:
Article
ISSN:
00221694
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S002216941500339X
Appears in Collections:
Articles; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGharamti, M. E.en
dc.contributor.authorAit-El-Fquih, Boujemaaen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2015-05-17T20:31:04Zen
dc.date.available2015-05-17T20:31:04Zen
dc.date.issued2015-05-11en
dc.identifier.citationAn Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Models 2015 Journal of Hydrologyen
dc.identifier.issn00221694en
dc.identifier.doi10.1016/j.jhydrol.2015.05.004en
dc.identifier.urihttp://hdl.handle.net/10754/553012en
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.en
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S002216941500339Xen
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.2015.05.004en
dc.subjectState-parameter estimationen
dc.subjectSubsurface contaminant transporten
dc.subjectEnsemble Kalman filteren
dc.subjectOne-step-ahead smoothingen
dc.subjectBayesian estimationen
dc.titleAn Iterative Ensemble Kalman Filter with One-Step-Ahead Smoothing for State-Parameters Estimation of Contaminant Transport Modelsen
dc.typeArticleen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.identifier.journalJournal of Hydrologyen
dc.eprint.versionPost-printen
dc.contributor.institutionNansen Environmental and Remote Sensing Center (NERSC), Bergen, Norwayen
kaust.authorEl Gharamti, Mohamaden
kaust.authorHoteit, Ibrahimen
kaust.authorAit-El-Fquih, Boujemaaen
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