A differential evolutionary Markov chain algorithm with ensemble smoother initial point selection for the identification of groundwater contaminant sources

Abstract

Groundwater contaminant source identification (GCSI) can provide support for the confirmation of responsibility and the remediation of pollution. This study has developed an innovative combination algorithm to recognize source properties and model parameters in groundwater contamination simultaneously. The research idea is based on Bayes theory and combines an ensemble smoother (ES) algorithm, differential evolutionary Markov chain (DEMC) algorithm, and adaptive kriging surrogate model (AKSM). Poor selection of initial estimates for unknown variables will slow down the convergence rate. Therefore, the initial points are not generated completely randomly, but are partially obtained by the ES algorithm. After the initial points have been determined, the DEMC algorithm is used to recognize source properties and model parameters in groundwater contamination. To improve the efficiency of the DEMC algorithm, the updating formula was adjusted by introducing information about the optimal chain into the iteration. However, the inversion process is time-consuming because both the ES and DEMC algorithms need to run the original simulation model frequently. To solve this problem, an AKSM was established for the original simulation model, which greatly accelerated the inversion process. Different hypothetical cases with different complexities were used to illustrate the validity of the combination algorithm. The identification results implied that the combination algorithm had not only faster convergence, but also higher accuracy. These improvements were more evident in the second case and third case. This implies that the proposed method will play a greater role with increasing problem complexity.