Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using Adaptive Markov Chain Monte Carlo
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Earth System Observation and Modelling
Environmental Science and Engineering Program
Physical Science and Engineering (PSE) Division
Water Desalination and Reuse Research Center (WDRC)
Permanent link to this recordhttp://hdl.handle.net/10754/625870
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AbstractA 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 the MCMC simulations, posterior distribution was 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. The model parameters and uncertainty 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 are not well estimated as compared to layers electrical conductivity because layer thicknesses in the model exhibits a low sensitivity to the EMI measurements, and is hence difficult to resolve. Application of the proposed MCMC based inversion to the field measurements in a drip irrigation system demonstrate that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil, and provide useful insight about parameter uncertainty for the assessment of the model outputs.
CitationJadoon KZ, Altaf MU, McCabe MF, Hoteit I, Muhammad N, et al. (2016) Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using Adaptive Markov Chain Monte Carlo. Hydrology and Earth System Sciences Discussions: 1–18. Available: http://dx.doi.org/10.5194/hess-2016-299.
SponsorsThis research was funded by the Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Saudi Arabia
CollectionsArticles; Biological and Environmental Sciences and Engineering (BESE) Division; Biological and Environmental Sciences and Engineering (BESE) Division; Environmental Science and Engineering Program; Environmental Science and Engineering Program; Physical Science and Engineering (PSE) Division; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program; Earth Science and Engineering Program; Water Desalination and Reuse Research Center (WDRC); Water Desalination and Reuse Research Center (WDRC)
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