Identification of contaminant source and hydraulic conductivity field based on an ILUES-SOM surrogate model

Contaminant source identification and hydraulic conductivity estimation are of great significance for contaminant transport model in the subsurface media, but their actual values are difficult to obtain and can usually be inversely identified and estimated by sparse observations. In order to reduce computational cost in the process of estimating groundwater model parameters, the surrogate model was often used. This study addresses this challenge by proposing a modified self-organizing map (SOM) based surrogate model, named ILUES-SOM, which combing a modified iterative ensemble smoother method (SGSIM-ILUES) and SOM algorithm, to simultaneously identify contaminant source parameters and hydraulic conductivity field. Considering the characteristics of the proposed method (ILUES-SOM), the comparison of parameter estimation accuracy and computational efficiency is performed with original SOM and SGSIM-ILUES inversion model. Moreover, the robustness of ILUES-SOM model for inversion was illustrated by proposing varying degrees of observation errors and missing early observation data. The results indicated that ILUES-SOM model can successfully retrieve unknown contaminant source simultaneously with heterogeneity hydraulic conductivity field in groundwater system.