Two-stage surrogate model-assisted Bayesian framework for groundwater contaminant source identification

Groundwater contaminant source identification is normally a prerequisite for contaminant remediation. This study proposes a new two-stage surrogate-assisted Markov chain Monte Carlo (MCMC)-based Bayesian framework to identify contaminant source parameters for groundwater polluted by dense nonaqueous phase liquid. In the framework, an adaptive update feedback process is proposed to construct a locally accurate surrogate model over posterior distributions to replace the time-consuming multiphase flow model. To increase the efficiency of the MCMC simulation, a multiobjective feasibility-enhanced particle swarm optimization algorithm (MOFEPSO) is adopted to generate the initial guess of the contamination source parameters. The accuracy and efficiency of the proposed framework are confirmed via a synthetic study. The contaminant source parameters generated by the proposed approach are compared with those computed by the one-stage surrogate-assisted MCMC-based Bayesian approach. The results demonstrate that the root mean squared error (RMSE) between true value of parameters and maximum a-posteriori density values (MAP) obtained by the proposed method decreased by 71.3% compared with those obtained by one-stage surrogate-based framework. To further assess the efficiency of MOFEPSO, the same inversion problem is solved with random values as the initial guesses of the unknown parameters during MCMC simulation; the other conditions are the same as the proposed framework. The results indicate that adopting MOFEPSO improves the efficiency of MCMC simulation. Therefore, the proposed approach can accurately and effectively identify the contaminant source parameters with achieving about 148 times of speed-up compared to the simulation-based MCMC simulation.