Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo
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 mult...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-10-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://www.hydrol-earth-syst-sci.net/21/5375/2017/hess-21-5375-2017.pdf |
| Summary: | 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. |
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| ISSN: | 1027-5606 1607-7938 |