The Bayesian approach is attractive because it can consider various uncertainties in the inverse process. Although the Bayesian algorithm has strong random ergodicity, it still lacks the ability to perform local optimization. Therefore, an improved single-component adaptive Metropolis (SCAM) algorithm based on Bayesian theory was developed to solve this problem and it was applied to the simultaneous identification of groundwater contaminant sources and simulation model parameters. The nondeterministic simulation model parameters have been introduced into the prior distribution as random variables. However, this will increase the number of random variables in the inverse problem, besides making the solution difficult. To alleviate this difficulty, the SCAM algorithm was applied to groundwater contaminant source identification. The acceptance probability formula was adjusted to enhance the local optimization ability of the SCAM algorithm. This improves the searching efficiency of the algorithm in the second stage, without losing the ergodicity in the first stage. In the inverse process, the simulation model is used multiple times to evaluate the likelihood function. To reduce the computational burden, the likelihood function is calculated by the surrogate model of the simulation model instead of by the simulation model itself, which greatly accelerates the process of Bayesian inversion. The effectiveness of this approach has been demonstrated by a hypothetical case study. Finally, the results of previous and improved algorithms have been compared. The results indicate that the improved SCAM algorithm can identify groundwater contaminant sources and simulation model parameters, simultaneously, with high accuracy and efficiency.

贝叶斯方法很有吸引力，因为它可以在逆过程中考虑各种不确定性。尽管贝叶斯算法具有很强的随机遍历性，但仍然缺乏执行局部优化的能力。因此，提出了一种基于贝叶斯理论的改进的单分量自适应都会算法（SCAM）来解决该问题，并将其应用于地下水污染物源的同时识别和模拟模型参数的确定。非确定性仿真模型参数已作为随机变量引入到先验分布中。但是，这将增加反问题中随机变量的数量，除了使求解变得困难之外。为了减轻这一困难，将SCAM算法应用于地下水污染物源识别。调整了接受概率公式，以增强SCAM算法的局部优化能力。这提高了第二阶段算法的搜索效率，而不会在第一阶段失去遍历性。在逆过程中，仿真模型被多次使用以评估似然函数。为了减轻计算负担，似然函数是通过模拟模型的替代模型而不是模拟模型本身来计算的，这极大地加快了贝叶斯反演的过程。假设的案例研究证明了这种方法的有效性。最后，比较了先前算法和改进算法的结果。