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@ARTICLE{Jadoon:838926,
      author       = {Jadoon, Khan Zaib and Altaf, Muhammad Umer and McCabe,
                      Matthew Francis and Hoteit, Ibrahim and Muhammad, Nisar and
                      Moghadas, Davood and Weihermüller, Lutz},
      title        = {{I}nferring soil salinity in a drip irrigation system from
                      multi-configuration {EMI} measurements using adaptive
                      {M}arkov chain {M}onte {C}arlo},
      journal      = {Hydrology and earth system sciences},
      volume       = {21},
      number       = {10},
      issn         = {1027-5606},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {FZJ-2017-07429},
      pages        = {5375 - 5383},
      year         = {2017},
      abstract     = {A substantial interpretation of electromagnetic induction
                      (EMI) measurements requires quantifying optimal model
                      parameters and uncertainty of a nonlinear inverse problem.
                      For this purpose, an adaptive Bayesian Markov chain Monte
                      Carlo (MCMC) algorithm is used to assess multi-orientation
                      and multi-offset EMI measurements in an agriculture field
                      with non-saline and saline soil. In MCMC the posterior
                      distribution is computed using Bayes' rule. The
                      electromagnetic forward model based on the full solution of
                      Maxwell's equations was used to simulate the apparent
                      electrical conductivity measured with the configurations of
                      EMI instrument, the CMD Mini-Explorer. Uncertainty in the
                      parameters for the three-layered earth model are
                      investigated by using synthetic data. Our results show that
                      in the scenario of non-saline soil, the parameters of layer
                      thickness as compared to layers electrical conductivity are
                      not very informative and are therefore difficult to resolve.
                      Application of the proposed MCMC-based inversion to field
                      measurements in a drip irrigation system demonstrates that
                      the parameters of the model can be well estimated for the
                      saline soil as compared to the non-saline soil, and provides
                      useful insight about parameter uncertainty for the
                      assessment of the model outputs.},
      cin          = {IBG-3},
      ddc          = {550},
      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:000413733500002},
      doi          = {10.5194/hess-21-5375-2017},
      url          = {https://juser.fz-juelich.de/record/838926},
}