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@ARTICLE{Zovi:828240,
      author       = {Zovi, Francesco and Camporese, Matteo and
                      Hendricks-Franssen, Harrie-Jan and Huisman, Johan Alexander
                      and Salandin, Paolo},
      title        = {{I}dentification of high-permeability subsurface structures
                      with multiple point geostatistics and normal score ensemble
                      {K}alman filter},
      journal      = {Journal of hydrology},
      volume       = {548},
      issn         = {0022-1694},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2017-02208},
      pages        = {208 - 224},
      year         = {2017},
      abstract     = {Alluvial aquifers are often characterized by the presence
                      of braided high-permeable paleo-riverbeds, which constitute
                      an interconnected preferential flow network whose
                      localization is of fundamental importance to predict flow
                      and transport dynamics. Classic geostatistical approaches
                      based on two-point correlation (i.e., the variogram) cannot
                      describe such particular shapes. In contrast, multiple point
                      geostatistics can describe almost any kind of shape using
                      the empirical probability distribution derived from a
                      training image. However, even with a correct training image
                      the exact positions of the channels are uncertain. State
                      information like groundwater levels can constrain the
                      channel positions using inverse modeling or data
                      assimilation, but the method should be able to handle
                      non-Gaussianity of the parameter distribution. Here the
                      normal score ensemble Kalman filter (NS-EnKF) was chosen as
                      the inverse conditioning algorithm to tackle this issue.
                      Multiple point geostatistics and NS-EnKF have already been
                      tested in synthetic examples, but in this study they are
                      used for the first time in a real-world casestudy. The test
                      site is an alluvial unconfined aquifer in northeastern Italy
                      with an extension of approximately 3 km2. A satellite
                      training image showing the braid shapes of the nearby river
                      and electrical resistivity tomography (ERT) images were used
                      as conditioning data to provide information on channel
                      shape, size, and position. Measured groundwater levels were
                      assimilated with the NS-EnKF to update the spatially
                      distributed groundwater parameters (hydraulic conductivity
                      and storage coefficients). Results from the study show that
                      the inversion based on multiple point geostatistics does not
                      outperform the one with a multiGaussian model and that the
                      information from the ERT images did not improve site
                      characterization. These results were further evaluated with
                      a synthetic study that mimics the experimental site. The
                      synthetic results showed that only for a much larger number
                      of conditioning piezometric heads, multiple point
                      geostatistics and ERT could improve aquifer
                      characterization. This shows that state of the art
                      stochastic methods need to be supported by abundant and
                      high-quality subsurface data.},
      cin          = {IBG-3},
      ddc          = {690},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000403739000017},
      doi          = {10.1016/j.jhydrol.2017.02.056},
      url          = {https://juser.fz-juelich.de/record/828240},
}