In this study, a heuristic search strategy based on probabilistic and geostatistical simulation approach is developed for simultaneous identification of groundwater contaminant source and simulation model parameters. The numerical simulation model, which is repeatedly invoked to evaluate the likelihood in Bayesian formula, can be substituted by the surrogate system to reduce the huge computational load. To improve the approximation accuracy of the surrogate system to the simulation model, we employ Entropy Weight method as a novel method to establish a combined surrogate system by combining Gaussian process, support vector regression, and kernel extreme learning machine. A state evaluation (heuristic) function based on Bayesian formula and the surrogate system is introduced to quantify the approximation degree of variables in current state to true values for contaminant sources and simulation model parameters. Thereafter, a heuristic search iterative process related to artificial intelligence is designed for simultaneous identification, which takes full advantage of the guidance and correction role of actual field monitoring data. A multi-vector and variable-step size random walk method is proposed to select the candidate point. A Metropolis formula based on the state evaluation function is constructed, and the result is used as the judging criterion for the state transition. Finally, simultaneous identification results are obtained when the iteration reaches the convergence criteria. The proposed approaches are tested with a numerical case study. The results indicate that the heuristic search strategy can assist in identifying groundwater contaminant source and simulation model parameters simultaneously with high accuracy and efficiency.

在这项研究中，开发了一种基于概率和地统计模拟方法的启发式搜索策略，用于同时识别地下水污染物源和模拟模型参数。可以反复使用数值模拟模型来评估贝叶斯公式中的似然性，可以用替代系统代替该数值模拟模型以减少巨大的计算量。为了提高代理系统对仿真模型的逼近精度，我们采用熵权法作为结合高斯过程，支持向量回归和核极限学习机建立组合代理系统的新方法。引入了基于贝叶斯公式和替代系统的状态评估（启发式）函数，以将当前状态下变量的逼近度量化为污染物源和模拟模型参数的真实值。此后，设计了一种与人工智能有关的启发式搜索迭代过程以进行同时识别，这充分利用了实际现场监视数据的指导和校正作用。提出了一种多矢量可变步长的随机游动方法来选择候选点。构造了基于状态评估函数的Metropolis公式，并将其结果作为状态转换的判断标准。最终，当迭代达到收敛标准时，同时获得识别结果。数值案例研究了提出的方法。结果表明，启发式搜索策略可以同时高效，准确地识别地下水污染物源和模拟模型参数。