Synchronous inversion of groundwater pollution source and aquifer parameters based on Bayesian formula
ZHANG Shuang-sheng1,3, LIU Han-hu1, QIANG Jing2, LIU Xi-kun3, ZHU Xue-qiang1
1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
2. School of Mathematics, China University of Mining and Technology, Xuzhou 221116, China;
3. Xuzhou City Water Resource Administrative Office, Xuzhou 221018, China
Aiming at the optimization of monitoring schemes in the process of the identification of pollution source and the inversion of aquifer parameters in the heterogeneous underground aquifer, this paper proposes an optimization method for the multi-well monitoring schemes based on Bayesian formula and progressive addition of wells with minimum information entropy. Firstly, the two-dimensional heterogeneous isotropic subsurface groundwater flow and solute transport models under hypothetical case were constructed, and the numerical simulation models were solved by GMS software. The surrogate model of the numerical simulation model was established by the optimal Latin hypercube sampling method and Kriging method. Then Taking the minimum information entropy of the parameter posterior distribution as the objective function, the optimization design of multi-well monitoring schemes was carried out by means of progressive addition of wells. Finally, the differential evolution adaptive Metropolis algorithm was used to inverse the pollution source and aquifer parameters synchronously according to the optimized monitoring scheme. The case study results showed that:The 5combination monitoring scheme (6, 5, 1, 2, 8) under the condition of taking into account the inversion accuracy and monitoring cost and ensuring that there was at least one monitoring well in each parameter section was the optimal monitoring scheme. Compared with the 10combined monitoring scheme (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) with the smallest information entropy, the 11parameters posterior mean deviation rate increased by 1.2%, but the monitoring cost was reduced by 50%.
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