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Abstract and Applied Analysis
Volume 2012 (2012), Article ID 805707, 18 pages
Research Article

Characterizing Curvilinear Features Using the Localized Normal-Score Ensemble Kalman Filter

1Grupo de Hidrogeologia, Departamento de Ingeniería Hidráulica y Medio Ambiente, Universitat Politècnica de València, Camino de Vera, s/n, 46022 Valencia, Spain
2Department of Petroleum and Geosystems Engineering, The University of Texas at Austin, Austin, TX 78712, USA

Received 2 January 2012; Revised 20 March 2012; Accepted 21 March 2012

Academic Editor: Muhammad Aslam Noor

Copyright © 2012 Haiyan Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The localized normal-score ensemble Kalman filter is shown to work for the characterization of non-multi-Gaussian distributed hydraulic conductivities by assimilating state observation data. The influence of type of flow regime, number of observation piezometers, and the prior model structure are evaluated in a synthetic aquifer. Steady-state observation data are not sufficient to identify the conductivity channels. Transient-state data are necessary for a good characterization of the hydraulic conductivity curvilinear patterns. Such characterization is very good with a dense network of observation data, and it deteriorates as the number of observation piezometers decreases. It is also remarkable that, even when the prior model structure is wrong, the localized normal-score ensemble Kalman filter can produce acceptable results for a sufficiently dense observation network.