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摘要:
在制定地下水污染修复方案时，污染源参数和渗透系数场是最重要的地下水数值模型参数，但前人研究多集中于单一类型参数的识别。文章中采用地下水污染物运移模型（MT3DMS）和数据同化方法（迭代局部更新集合平滑器，ILUES）构成地下水污染源识别的求解框架，并利用Karhunen-Loève展开技术实现渗透系数场的参数降维，最后通过同化水头与浓度数据实现地下水污染源强和渗透系数场的联合反演。结果表明：（1）ILUES算法能精确识别污染源参数和渗透系数场，并且具有很高的普适性；（2）精确表征渗透系数在空间上呈现出的非均质性，是预测污染物迁移路径、反演污染强度的关键；（3）ILUES算法参数影响着反演效果，综合考虑计算效率和计算精度等，可以得到算例的最佳样本集合大小（Ne=4000）和ILUES算法最佳参数组合（局部临近样本集合占比α=0.4，相对权重b=4）。但在实际工程案例中，如果对精度的要求不是过高，经验组合（α=0.1，b=1）更值得推荐。研究结果对于区域地下水资源调查、评价和管理等工作具有较强的实践意义，并可为后期地下水污染预测及地下水监测井网优化提供技术支撑。
Abstract:The pollution source parameters and hydraulic conductivity field are the most important parameters of groundwater numerical models when making groundwater pollution remediation plans. However, previous studies focused mainly on the identification of single type parameters. The groundwater pollutant transport model (MT3DMS) and data assimilation method (iterative local updating ensemble smoother, ILUES) are used to form a solution framework for groundwater pollution source identification, and Karhunen-Loève expansion technique is used to realize parameter dimension reduction of the hydraulic conductivity field. The joint inversion of groundwater pollution source intensity and hydraulic conductivity field are also realized by assimilating hydraulic heads and concentration data. The results show that (1) the ILUES algorithm can accurately identify pollution source parameters and permeability coefficient field, and it is of high universality. (2) Accurate characterization of spatial heterogeneity of the coefficient of permeability is the key to predict pollutant migration path and inversion of pollution intensity. (3) The ILUES algorithm parameters affect the inversion results. By considering the computational efficiency and accuracy, the optimal sample set size (Ne=4000) and the optimal parameter combination of ILUES algorithm (α=0.4, b=4) can be obtained. However, in practical engineering cases, the empirical combination (α=0.1, b=1) is more recommendable if the requirement for accuracy is not too high. The results of this study have strong practical significance for regional groundwater resources investigation, evaluation and management, and can provide technical support for later groundwater pollution prediction and optimization of groundwater monitoring well networks.
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Key words:
- groundwater pollution /
- parameter inversion /
- data assimilation /
- ensemble smoother
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表 1 含水层的水文地质特征
Table 1. Hydrogeological features of aquifers
参数名称 单位 参数值 含水层厚度 m 1.0 孔隙度 无量纲 0.3 纵向弥散度 m 2.0 横向弥散度 m 0.6 
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表 2 污染源释放强度参数的真实值及先验区间
Table 2. True values and a priori interval of the pollution source release intensity parameter
应力期 先验区间 污染源源强/（kg·d−1） S1 S2 S3 SP1 [20,100] 98.0 54.0 30.0 SP2 [20,100] 87.0 78.0 39.0 SP3 [20,100] 74.0 64.0 46.0 SP4 [20,100] 61.0 85.0 57.0 SP5 [20,100] 47.0 71.0 66.0 SP6 [20,100] 39.0 61.0 71.0 SP7 [20,100] 31.0 45.0 79.0 SP8 [20,100] 24.0 89.0 83.0
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