globalchange  > 气候变化事实与影响
DOI: 10.5194/hess-21-5375-2017
Scopus记录号: 2-s2.0-85032494407
论文题名:
Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo
作者: Jadoon K; Z; , Altaf M; U; , McCabe M; F; , Hoteit I; , Muhammad N; , Moghadas D; , Weihermüller L
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2017
卷: 21, 期:10
起始页码: 5375
结束页码: 5383
语种: 英语
Scopus关键词: Chains ; Electric conductivity ; Electromagnetic induction ; Inverse problems ; Irrigation ; Markov processes ; Maxwell equations ; Monte Carlo methods ; Soil surveys ; Soils ; Uncertainty analysis ; Adaptive Markov chain Monte Carlo ; Apparent electrical conductivity ; Bayesian markov chain monte carlo ; Drip irrigation systems ; Electrical conductivity ; Electromagnetic inductions (EMI) ; Non-linear inverse problem ; Posterior distributions ; Parameter estimation ; agricultural soil ; algorithm ; drip irrigation ; electrical conductivity ; electromagnetic method ; farming system ; Markov chain ; Monte Carlo analysis ; salinity ; uncertainty analysis
英文摘要: 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. © Author(s) 2017.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79016
Appears in Collections:气候变化事实与影响

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作者单位: Department of the Civil Engineering, COMSATS Institute of Information Technology, Abbottabad, Pakistan; Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia; Earth Science and Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia; Brandenburg University of Technology, Research Center Landscape Development and Mining Landscapes, Cottbus, Germany; Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich, GmbH, Jülich, Germany; Department of Civil Engineering, International Islamic University, Islamabad, Pakistan

Recommended Citation:
Jadoon K,Z,, Altaf M,et al. Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo[J]. Hydrology and Earth System Sciences,2017-01-01,21(10)
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