基于随机统计方法的地下水污染源反演识别

地下水污染具有存在的隐蔽性、发现的滞后性特点,这给地下水污染修复方案设计、污染风险评估、污染责任认定都带来了很大的困难。以某垃圾填埋场垃圾渗滤液地下泄漏为例,对地下水污染源的反演识别问题进行研究。通过污染质运移模拟模型、替代模型、随机统计方法(伴随状态方法及贝叶斯方法)的综合运用,反演识别地下水污染源的位置、个数及释放历史。结果表明,伴随状态方法和贝叶斯方法的联合运用可以反演识别出污染源的个数、位置及释放历史;贝叶斯方法反演识别污染源过程中,以替代模型作为模拟模型的转化形式,用替代模型代替模拟模型大幅度地减小了计算负荷,并保持较高的精度。

Groundwater Pollution Source Identification based on Random Statistical Methods

Groundwater pollution is characterized with hidden existence, hysteretic nature, which makes it difficult to design groundwater restoration schemes, evaluate pollution risks and determine responsibilities for pollution. In this paper, the situation of leakage of sewage from a landfill was considered to study the inverse identification problem of groundwater pollution sources, by means of three methods, namely solute transport simulation model, surrogate model, random statistical method (with adjoint state method and Bayesian method), in order to identify features of groundwater pollution sources. The results show that: the number, location and release history of pollution sources can be identified through solving the inverse problem by combining adjoint state method and Bayesian method; during inverse identification of pollution sources based on Bayesian method, a surrogate model was an alternative to a simulation model, replacement of a simulation model with a surrogate model greatly reduced computational load and also maintained a fairly high degree of accuracy.