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Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation
Joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for contaminant transport models in heterogeneous subsurface media. As for accurate characterization of hydraulic conductivities, both geostatistical modeling and groundwater inverse modeling are alternative approaches. In this study, an iterative ensemble smoother and sequential gaussian simulation (SGSIM) in geostatistics modeling were combined to realize the simultaneous inversion of contaminant sources and hydraulic conductivities, by using directly measured hydraulic conductivities and indirect hydraulic head and concentration data. To alleviate the high computational cost caused by repetitive evaluations of complex, high-dimensional groundwater models, SGSIM with the pilot points method was used. Considering the characteristics of the proposed method, four scenarios with ten cases were set up in terms of ensemble number and iteration number that affect the performance of the iterative ensemble smoother, the number of pilot points, and the observation data, respectively. The results for the synthetic example indicate that the ensemble size of 2000 and the pilot point number of 80 is an ideal combination of parameters, and the proposed method can successfully recover contaminant source information simultaneously with hydraulic conductivity.
Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation
Joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for contaminant transport models in heterogeneous subsurface media. As for accurate characterization of hydraulic conductivities, both geostatistical modeling and groundwater inverse modeling are alternative approaches. In this study, an iterative ensemble smoother and sequential gaussian simulation (SGSIM) in geostatistics modeling were combined to realize the simultaneous inversion of contaminant sources and hydraulic conductivities, by using directly measured hydraulic conductivities and indirect hydraulic head and concentration data. To alleviate the high computational cost caused by repetitive evaluations of complex, high-dimensional groundwater models, SGSIM with the pilot points method was used. Considering the characteristics of the proposed method, four scenarios with ten cases were set up in terms of ensemble number and iteration number that affect the performance of the iterative ensemble smoother, the number of pilot points, and the observation data, respectively. The results for the synthetic example indicate that the ensemble size of 2000 and the pilot point number of 80 is an ideal combination of parameters, and the proposed method can successfully recover contaminant source information simultaneously with hydraulic conductivity.
Simultaneous Estimation of a Contaminant Source and Hydraulic Conductivity Field by Combining an Iterative Ensemble Smoother and Sequential Gaussian Simulation
Simin Jiang (author) / Ruicheng Zhang (author) / Jinbing Liu (author) / Xuemin Xia (author) / Xianwen Li (author) / Maohui Zheng (author)
2022
Article (Journal)
Electronic Resource
Unknown
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An adaptive Gaussian process-based iterative ensemble smoother for data assimilation
| British Library Online Contents | 2018
An adaptive Gaussian process-based iterative ensemble smoother for data assimilation
| British Library Online Contents | 2018
An adaptive Gaussian process-based iterative ensemble smoother for data assimilation
| British Library Online Contents | 2018