Mathematical Geosciences

, Volume 44, Issue 2, pp 169–185 | Cite as

Pattern Recognition in a Bimodal Aquifer Using the Normal-Score Ensemble Kalman Filter

  • Haiyan Zhou
  • Liangping Li
  • Harrie-Jan Hendricks Franssen
  • J. Jaime Gómez-Hernández
Special Issue


The ensemble Kalman filter (EnKF) is now widely used in diverse disciplines to estimate model parameters and update model states by integrating observed data. The EnKF is known to perform optimally only for multi-Gaussian distributed states and parameters. A new approach, the normal-score EnKF (NS-EnKF), has been recently proposed to handle complex aquifers with non-Gaussian distributed parameters. In this work, we aim at investigating the capacity of the NS-EnKF to identify patterns in the spatial distribution of the model parameters (hydraulic conductivities) by assimilating dynamic observations in the absence of direct measurements of the parameters themselves. In some situations, hydraulic conductivity measurements (hard data) may not be available, which requires the estimation of conductivities from indirect observations, such as piezometric heads. We show how the NS-EnKF is capable of retrieving the bimodal nature of a synthetic aquifer solely from piezometric head data. By comparison with a more standard implementation of the EnKF, the NS-EnKF gives better results with regard to histogram preservation, uncertainty assessment, and transport predictions.


Large heterogeneity Parameter identification Non-multi-Gaussian Uncertainty Groundwater modeling Hard data 


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Copyright information

© International Association for Mathematical Geosciences 2011

Authors and Affiliations

  • Haiyan Zhou
    • 1
  • Liangping Li
    • 1
  • Harrie-Jan Hendricks Franssen
    • 2
  • J. Jaime Gómez-Hernández
    • 1
  1. 1.Grupo de Hidrogeología, Departamento de Ingeniería Hidraulica y Medio AmbienteUniversitat Politècnica de ValènciaValènciaSpain
  2. 2.Agrosphere, IBG-3Forschungszentrum Jülich GmbHJülichGermany

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