Simultaneous identification of contaminant sources and hydraulic conductivity field by combining geostatistics method with self-organizing maps algorithm

Abstract

In the contaminant remediation of groundwater, the release history of contaminant sources and hydraulic conductivity field are two key parameters that need to know, but their actual values are difficult to obtain and can only be inversely identified by limited measured data. However, the process of solving the inverse problem needs to repeatedly call the forward model of contaminant transport, which is very time-consuming, especially for the high-dimensional inverse problems. In this study, based on the training data generated from a prior range of parameters (the release strength of contaminant sources and hydraulic conductivity at pilot points), the self-organizing maps (SOM) algorithm was employed to construct the surrogate model for the numerical model of contaminant transport in a simplified hypothetical aquifer, then the surrogate model was used to retrieve jointly the contaminant strength of sources and the hydraulic conductivity at pilot points, and the Kriging method of geostatistics was further used to process the estimated K-values at pilot points to obtain the hydraulic conductivity field. Also, to investigate the ability of the SOM-based surrogate model for retrieving both contaminant source strengths and hydraulic conductivity, we gradually expanded the prior range and increased the number of inversion terms in each prior range. Moreover, the robustness of the SOM-based surrogate model for inversion was illustrated by proposing the scarcity of data and different degrees of measurement error in the limited actual observation data. When the actual observation data is reduced by 2/3, the Root Mean Square Error (RMSE) of retrieving source strengths and hydraulic conductivity at pilot points are 1.07 and 0.09, respectively. The results indicated the SOM-based surrogate model shows remarkable inversion precision and robustness, and an accurate estimation of the actual hydraulic conductivity field could be obtained by the Kriging method based on that.

Keywords: Contaminant source identification; Hydraulic conductivity estimation; Kriging method; Self-organizing maps; Surrogate model.