TY  - JOUR
AU  - Jadoon, Khan Zaib
AU  - Altaf, Muhammad Umer
AU  - McCabe, Matthew Francis
AU  - Hoteit, Ibrahim
AU  - Muhammad, Nisar
AU  - Moghadas, Davood
AU  - Weihermüller, Lutz
TI  - Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo
JO  - Hydrology and earth system sciences
VL  - 21
IS  - 10
SN  - 1027-5606
CY  - Katlenburg-Lindau
PB  - EGU
M1  - FZJ-2017-07429
SP  - 5375 - 5383
PY  - 2017
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000413733500002
DO  - DOI:10.5194/hess-21-5375-2017
UR  - https://juser.fz-juelich.de/record/838926
ER  -