A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity

[EN] Understanding a contaminant source may help in a better management and risk assessment of a polluted aquifer. However, contaminant source information may not be available when a pollutant is detected in a drinking well. The restart ensemble Kalman filter (restart EnKF, also named r-EnKF) has be...

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Autores: Xu, Teng, Chen, Zi, Lu, Chunhui, Gómez-Hernández, J. Jaime|||0000-0002-0720-2196
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/182863
Acceso en línea:https://riunet.upv.es/handle/10251/182863
Access Level:acceso abierto
Palabra clave:Contaminant source identification
Data assimilation
Ensemble smoother with multiple data assimilation
Restart ensemble Kalman filter
INGENIERIA HIDRAULICA
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dc.title.none.fl_str_mv A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity
title A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity
spellingShingle A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity
Xu, Teng
Contaminant source identification
Data assimilation
Ensemble smoother with multiple data assimilation
Restart ensemble Kalman filter
INGENIERIA HIDRAULICA
title_short A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity
title_full A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity
title_fullStr A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity
title_full_unstemmed A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity
title_sort A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivity
dc.creator.none.fl_str_mv Xu, Teng
Chen, Zi
Lu, Chunhui
Gómez-Hernández, J. Jaime|||0000-0002-0720-2196
author Xu, Teng
author_facet Xu, Teng
Chen, Zi
Lu, Chunhui
Gómez-Hernández, J. Jaime|||0000-0002-0720-2196
author_role author
author2 Chen, Zi
Lu, Chunhui
Gómez-Hernández, J. Jaime|||0000-0002-0720-2196
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Hidráulica y Medio Ambiente
Instituto Universitario de Ingeniería del Agua y del Medio Ambiente
Escuela Técnica Superior de Ingeniería de Caminos, Canales y Puertos
AGENCIA ESTATAL DE INVESTIGACION
Ministerio de Economía y Empresa
Jiangsu Provincial Department of Education
Ministerio de Educación, Cultura y Deporte
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Contaminant source identification
Data assimilation
Ensemble smoother with multiple data assimilation
Restart ensemble Kalman filter
INGENIERIA HIDRAULICA
topic Contaminant source identification
Data assimilation
Ensemble smoother with multiple data assimilation
Restart ensemble Kalman filter
INGENIERIA HIDRAULICA
description [EN] Understanding a contaminant source may help in a better management and risk assessment of a polluted aquifer. However, contaminant source information may not be available when a pollutant is detected in a drinking well. The restart ensemble Kalman filter (restart EnKF, also named r-EnKF) has been demonstrated in synthetic and laboratory experiments as an efficient solution for the identification of a contaminant source. Recently, the ensemble smoother with multiple data assimilation (ES-MDA) has been proposed as an alternative to the r-EnKF as a more efficient solution given that the r-EnKF needs to restart the simulation of the state equation from time zero after each data assimilation step. An analysis, in a synthetic aquifer, of the accuracy of the ES-MDA for the simultaneous identification of a contaminant source and the spatial distribution of hydraulic conductivity by assimilating both piezometric head and concentration observations is carried out using the r-EnKF as a benchmark. The conclusion is that the ES-MDA can outperform the r-EnKF, but the expected speed advantage, associated with the possibility of assimilating all data at once, does not exist. For the ES-MDA to reach the same level of accuracy as the r-EnKF, the number of multiple data assimilations must be large, and final computing time is similar for both approaches. However, the ES-MDA can do much better than the r-EnKF if the number of iterations increases even further, with the consequent increase of computational cost.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-04-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/182863
url https://riunet.upv.es/handle/10251/182863
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-109131RB-I00 APRENDIZAJE AUTOMATICO PARA HIDROGEOLOGOS FORENSES
Fundamental Research Funds for the Central Universities Fundamental Research Funds for the Central Universities B200201015
Fundamental Research Funds for the Central Universities Fundamental Research Funds for the Central Universities B200204002
Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 CGL2014-59841-P ¿QUIEN HA SIDO?
JPDE JPDE B19052
National Natural Science Foundation of China https://doi.org/10.13039/501100001809 51679067
National Natural Science Foundation of China https://doi.org/10.13039/501100001809 51879088
Ministerio de Educación y Cultura http://dx.doi.org/10.13039/501100003176 PRX17%2F00150
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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repository.mail.fl_str_mv
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spelling A comparison between ES-MDA and restart EnKF for the purpose of the simultaneous identification of a contaminant source and hydraulic conductivityXu, TengChen, ZiLu, ChunhuiGómez-Hernández, J. Jaime|||0000-0002-0720-2196Contaminant source identificationData assimilationEnsemble smoother with multiple data assimilationRestart ensemble Kalman filterINGENIERIA HIDRAULICA[EN] Understanding a contaminant source may help in a better management and risk assessment of a polluted aquifer. However, contaminant source information may not be available when a pollutant is detected in a drinking well. The restart ensemble Kalman filter (restart EnKF, also named r-EnKF) has been demonstrated in synthetic and laboratory experiments as an efficient solution for the identification of a contaminant source. Recently, the ensemble smoother with multiple data assimilation (ES-MDA) has been proposed as an alternative to the r-EnKF as a more efficient solution given that the r-EnKF needs to restart the simulation of the state equation from time zero after each data assimilation step. An analysis, in a synthetic aquifer, of the accuracy of the ES-MDA for the simultaneous identification of a contaminant source and the spatial distribution of hydraulic conductivity by assimilating both piezometric head and concentration observations is carried out using the r-EnKF as a benchmark. The conclusion is that the ES-MDA can outperform the r-EnKF, but the expected speed advantage, associated with the possibility of assimilating all data at once, does not exist. For the ES-MDA to reach the same level of accuracy as the r-EnKF, the number of multiple data assimilations must be large, and final computing time is similar for both approaches. However, the ES-MDA can do much better than the r-EnKF if the number of iterations increases even further, with the consequent increase of computational cost.Financial support to carry out this work was received from the Spanish Ministry of Economy and Competitiveness through project CGL2014-59841-P, and from the Spanish Ministry of Education, Culture and Sports through a fellowship for the mobility of professors in foreign research and higher education institutions of reference to the second author, reference PRX17/00150. Teng Xu also acknowledges the financial support from the Fundamental Research Funds for the Central Universities (B200201015) and Jiangsu Specially-Appointed Professor Program (B19052). Chunhui Lu acknowledges the financial support from the National Natural Science Foundation of China (51679067 and 51879088), and Fundamental Research Funds for the Central Universities (B200204002).ElsevierDepartamento de Ingeniería Hidráulica y Medio AmbienteInstituto Universitario de Ingeniería del Agua y del Medio AmbienteEscuela Técnica Superior de Ingeniería de Caminos, Canales y PuertosAGENCIA ESTATAL DE INVESTIGACIONMinisterio de Economía y EmpresaJiangsu Provincial Department of EducationMinisterio de Educación, Cultura y DeporteNational Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesRepositorio Institucional de la Universitat Politècnica de València Riunet20212021-04-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/182863reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-109131RB-I00 APRENDIZAJE AUTOMATICO PARA HIDROGEOLOGOS FORENSESFundamental Research Funds for the Central Universities Fundamental Research Funds for the Central Universities B200201015Fundamental Research Funds for the Central Universities Fundamental Research Funds for the Central Universities B200204002Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 CGL2014-59841-P ¿QUIEN HA SIDO?JPDE JPDE B19052National Natural Science Foundation of China https://doi.org/10.13039/501100001809 51679067National Natural Science Foundation of China https://doi.org/10.13039/501100001809 51879088Ministerio de Educación y Cultura http://dx.doi.org/10.13039/501100003176 PRX17%2F00150open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1828632026-06-13T07:49:27Z
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