dor_id: 4132303

506.#.#.a: Público

590.#.#.d: Los artículos enviados a la revista "Geofísica Internacional", se juzgan por medio de un proceso de revisión por pares

510.0.#.a: Consejo Nacional de Ciencia y Tecnología (CONACyT); Scientific Electronic Library Online (SciELO); SCOPUS, Dialnet, Directory of Open Access Journals (DOAJ); Geobase

561.#.#.u: https://www.geofisica.unam.mx/

650.#.4.x: Físico Matemáticas y Ciencias de la Tierra

336.#.#.b: article

336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: http://revistagi.geofisica.unam.mx/index.php/RGI

351.#.#.b: Geofísica Internacional

351.#.#.a: Artículos

harvesting_group: RevistasUNAM

270.1.#.p: Revistas UNAM. Dirección General de Publicaciones y Fomento Editorial, UNAM en revistas@unam.mx

590.#.#.c: Open Journal Systems (OJS)

270.#.#.d: MX

270.1.#.d: México

590.#.#.b: Concentrador

883.#.#.u: https://revistas.unam.mx/catalogo/

883.#.#.a: Revistas UNAM

590.#.#.a: Coordinación de Difusión Cultural

883.#.#.1: https://www.publicaciones.unam.mx/

883.#.#.q: Dirección General de Publicaciones y Fomento Editorial

850.#.#.a: Universidad Nacional Autónoma de México

856.4.0.u: http://revistagi.geofisica.unam.mx/index.php/RGI/article/view/163/155

100.1.#.a: Vázquez-guillén, F.; Auvinet, Guichard

524.#.#.a: Vázquez-guillén, F., et al. (2017). Hydraulic conductivities identification via Ensemble Kalman Filtering with transformed data considering the risk of systematic bias. Geofísica Internacional; Vol. 56 Núm. 4: Octubre 1, 2017; 317-333. Recuperado de https://repositorio.unam.mx/contenidos/4132303

245.1.0.a: Hydraulic conductivities identification via Ensemble Kalman Filtering with transformed data considering the risk of systematic bias

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Geofísica, UNAM

264.#.0.c: 2017

264.#.1.c: 2017-10-01

653.#.#.a: Simulación estocástica; campos aleatorios condicionales; anamorfosis gaussiana; problema inverso; campos aleatorios no multi-gaussianos; Stochastic simulation; conditional random fields; Gaussian anamorphosis; inverse problem; Non multi-Gaussian random fields

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico revistagi@igeofisica.unam.mx

884.#.#.k: http://revistagi.geofisica.unam.mx/index.php/RGI/article/view/163

001.#.#.#: 063.oai:revistagi.geofisica.unam.mx:article/163

041.#.7.h: spa

520.3.#.a: In subsurface hydrology, Ensemble Kalman Filtering (EnKF) has been coupled with groundwater flow and transport models to solve the inverse problem. Several extensions of the EnKF have been proposed to improve its performance when dealing with non-multi-Gaussian random field models of the hydraulic conductivity. One such variant is the EnKF with transformed data (tEnKF), which uses Gaussian anamorphosis within a conditioning step. Although this transformation has been used in the past to identify hydraulic conductivities, previous studies have ignored the risk of introducing a systematic bias in the spatiotemporal evolution of the hydraulic head field during the forecast steps that the update steps may not correct over time. This paper proposes that in order to evaluate the performance of tEnKFs, applications in synthetically generated random porous media should take into account this risk by incorporating prior knowledge with a multi-Gaussian conductivity correlation structure, and by adopting a reference field with asymmetric correlation structure. As an example of this application, hydraulic conductivities using the tEnKF were identified by solving a onedimensional, single phase flow problem in a continuous random porous medium. Common concepts in Geostatistics are used to explain the hypothesis underlying both EnKF and tEnKF and to establish a clear link between the tEnKF and the stochastic simulation of conditional random fields.doi: https://doi.org/10.22201/igeof.00167169p.2017.56.4.1825

773.1.#.t: Geofísica Internacional; Vol. 56 Núm. 4: Octubre 1, 2017; 317-333

773.1.#.o: http://revistagi.geofisica.unam.mx/index.php/RGI

022.#.#.a: ISSN-L: 2954-436X; ISSN impreso: 0016-7169

310.#.#.a: Trimestral

300.#.#.a: Páginas: 317-333

264.#.1.b: Instituto de Geofísica, UNAM

doi: https://doi.org/10.22201/igeof.00167169p.2017.56.4.1825

handle: 00cf278a5dbb40a5

harvesting_date: 2023-06-20 16:00:00.0

856.#.0.q: application/pdf

file_creation_date: 2018-02-01 20:56:26.0

file_modification_date: 2022-04-04 20:43:17.0

file_creator: F. Vázquez-Guillén

file_name: 41f8e24927cc88854be17aac6c7c19ccd305e15f0bb85a15d3343e8d253f9f89.pdf

file_pages_number: 17

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file_size: 642032

245.1.0.b: Hydraulic conductivities identification via Ensemble Kalman Filtering with transformed data considering the risk of systematic bias

last_modified: 2023-06-20 16:00:00

license_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es

license_type: by-nc-sa

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