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@ARTICLE{Zhou:21353,
      author       = {Zhou, H.Y. and Li, L.P. and Hendricks-Franssen, H.J. and
                      Gomez-Hernandez, J.J.},
      title        = {{P}attern {R}ecognition in a {B}imodal {A}quifer {U}sing
                      the {N}ormal-{S}core {E}nsemble {K}alman {F}ilter},
      journal      = {Mathematical geosciences},
      volume       = {44},
      issn         = {1874-8961},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer},
      reportid     = {PreJuSER-21353},
      pages        = {169 - 185},
      year         = {2012},
      note         = {The authors gratefully acknowledge the financial support by
                      the Spanish Ministry of Science and Innovation through
                      project CGL2011-23295. The first author appreciates the
                      financial aid from China Scholarship Council (CSC No.
                      [2007]3020).},
      abstract     = {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.},
      keywords     = {J (WoSType)},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Geosciences, Multidisciplinary / Mathematics,
                      Interdisciplinary Applications},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000300294500004},
      doi          = {10.1007/s11004-011-9372-3},
      url          = {https://juser.fz-juelich.de/record/21353},
}