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dc.contributor.author | Xu, Teng | es_ES |
dc.contributor.author | Gómez-Hernández, J. Jaime | es_ES |
dc.contributor.author | Li ., Liangping | es_ES |
dc.contributor.author | Zhou ., Haiyan | es_ES |
dc.date.accessioned | 2014-09-29T09:04:28Z | |
dc.date.available | 2014-09-29T09:04:28Z | |
dc.date.issued | 2013-03 | |
dc.identifier.issn | 0098-3004 | |
dc.identifier.uri | http://hdl.handle.net/10251/40393 | |
dc.description.abstract | [EN] The ensemble Kalman filter (EnKF) is nowadays recognized as an excellent inverse method for hydraulic conductivity characterization using transient piezometric head data. Its implementation is well suited for a parallel computing environment. A parallel code has been designed that uses parallelization both in the forecast step and in the analysis step. In the forecast step, each member of the ensemble is sent to a different processor, while in the analysis step, the computations of the covariances are distributed between the different processors. An important aspect of the parallelization is to limit as much as possible the communication between the processors in order to maximize execution time reduction. Four tests are carried out to evaluate the performance of the parallelization with different ensemble and model sizes. The results show the savings provided by the parallel EnKF, especially for a large number of ensemble realizations. (c) 2012 Elsevier Ltd. All rights reserved. | es_ES |
dc.description.sponsorship | The first author acknowledges the financial support from China Scholarship Council (CSC). Financial support to carry out this work was also received from the Spanish Ministry of Science and Innovation through project CGL2011-23295, and from the Universitat Politecnica de Valencia through project PERFORA. | en_EN |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Computers and Geosciences | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Parallel EnKF | es_ES |
dc.subject | Cluster | es_ES |
dc.subject | Hydraulic conductivity | es_ES |
dc.subject | Parallel computing | es_ES |
dc.subject.classification | INGENIERIA HIDRAULICA | es_ES |
dc.title | Paralellized ensemble Kalman filter for hydraulic conductivity characterization | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.cageo.2012.10.007 | |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//CGL2011-23295/ES/MODELACION ESTOCASTICA INVERSA FUERA DE LO NORMAL/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient | es_ES |
dc.description.bibliographicCitation | Xu, T.; Gómez-Hernández, JJ.; Li ., L.; Zhou ., H. (2013). Paralellized ensemble Kalman filter for hydraulic conductivity characterization. Computers and Geosciences. 52:42-49. https://doi.org/10.1016/j.cageo.2012.10.007 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.cageo.2012.10.007 | es_ES |
dc.description.upvformatpinicio | 42 | es_ES |
dc.description.upvformatpfin | 49 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 52 | es_ES |
dc.relation.senia | 263347 | |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |
dc.contributor.funder | China Scholarship Council | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |