In this paper, a parallel heuristic search strategy based on Bayesian approach was first proposed for recognizing groundwater DNAPL contaminant source and aquifer parameters (unknown variables). Frequent calls to numerical simulation model effectuated large computational burden during likelihood calculation. Single surrogate system was established to reduce the burden, but it had unavoidable limitations. Thus, we first presented the particle swarm optimization-tabu search hybrid algorithm to construct an optimal combined surrogate system for the simulation model, which assembled Gaussian process, kernel extreme learning machine, support vector regression, and also improved the accuracy of the surrogate system to simulation model. Thereafter, a parallel heuristic search iterative process was first implemented for simultaneous recognition of unknown variables. Each round of iteration involved determination of candidate points and state transitions. The Monte Carlo approach was used widely for selecting candidate point, but it did not readily converge to posterior distribution when the probability density functions were complex. And the search ergodicity was weak. In order to improve the search ergodicity, a DE algorithm with variable mutation rate based on rand-to-best, 1, and bin strategy was first proposed in this paper to determine multiple candidate points. The recognition results were obtained when the iteration process terminated. The accuracy and efficiency of our approaches were demonstrated through a hypothetical case in DNAPLs-contaminated aquifer, and the recognizing accuracy was high. More importantly, the new simulation model based on the recognition results is helpful in calculating future contaminant plume in the aquifer, which can provide credible basis for groundwater contaminant remediation plan design and risk assessment.

本文首先提出了一种基于贝叶斯方法的并行启发式搜索策略，用于识别地下水DNAPL污染源和含水层参数（未知变量）。在似然计算过程中，频繁调用数值模拟模型会导致很大的计算负担。建立了单一代理系统以减轻负担，但它不可避免地存在局限性。因此，我们首先提出了粒子群优化-tabu搜索混合算法，为模拟模型构建了最优的组合替代系统，该系统组合了高斯过程，核极限学习机，支持向量回归，并提高了替代系统的精度。模拟模型。之后，首先实现了并行启发式搜索迭代过程，以同时识别未知变量。每轮迭代都涉及确定候选点和状态转换。蒙特卡罗方法被广泛用于选择候选点，但是当概率密度函数很复杂时，它不容易收敛到后验分布。搜索遍历性很弱。为了提高搜索遍历性，本文首先提出了一种基于rand-to-best，1和bin策略的可变变异率DE算法来确定多个候选点。当迭代过程终止时获得识别结果。我们的方法的准确性和效率通过一个假设案例在被DNAPLs污染的含水层中得到证明，识别精度高。更重要的是，基于识别结果的新模拟模型有助于计算含水层中未来的污染物羽流，从而可以为地下水污染物修复计划设计和风险评估提供可靠的依据。