基于改进稀疏网格替代模拟的地下水DNAPLs运移不确定性分析

Uncertainty analysis of groundwater DNAPLs migration based on improved sparse grids surrogate model

由于实际的水文地质条件具有复杂性和变异性,地下水DNAPLs运移数值模拟的不确定性不可避免。为解决不确定性分析中因多次调用DNAPLs运移模型导致的计算耗时问题,本次研究在传统稀疏网格(SG)替代模型的基础上,提出了一种将局部自适应(LA)和维数自适应(DA)耦合的改进替代模型DA-LA-SG。通过两个解析案例和一个PCE运移室内实验,验证了DA-LA-SG的替代效率和精度,并将其用于室内砂箱PCE运移模拟不确定性分析。研究结果表明:在替代模型构建初期,LA-SG的替代效率优于或接近于DA-SG和DA-LA-SG,随着插值节点数量的增多,DA-SG和DALA-SG的替代效率逐渐优于LA-SG,DA-LA-SG具有最高的替代效率。DA-LA-SG能够高效、高精度地建立PCE运移模型的似然函数替代模型,且被用于PCE运移模拟参数不确定性分析。结果表明:背景介质渗透率k1、拟合系数n1和透镜体渗透率k2的可识别性较强,背景介质孔隙度μ1、透镜体孔隙度μ2和拟合系数n2的识别效果较差,这些参数对饱和度观测数据不敏感。

Uncertainty in the numerical simulation of groundwater DNAPLs migration is inevitable due to the complex and unknown hydrogeological conditions in the actual world. In order to overcome the computational burden caused by repetitive calls of the DNAPLs migration model in uncertainty analysis,this study is carried out,based on the traditional sparse grid( SG) surrogate model. In this paper,an improved SG method DALA-SG is proposed,which couples the local adaptive( LA) and dimensional adaptive( DA). The surrogate efficiency and accuracy of the proposed DA-LA-SG are verified through two analytical cases and a PCE migration laboratory experiment,and the proposed model is applied to the uncertainty analysis of indoor sandbox PCE migration simulation. The results show that in the early stage of establishing the surrogate model,the surrogate efficiency of LA-SG is better than or close to that of DA-SG and DA-LA-SG,but the efficiency of DA-SG and DA-LA-SG is gradually better than that of LA-SG with the increasing number of the interpolation nodes. DA-LA-SG performs the best. DA-LA-SG can be used to efficiently and accurately establish the likelihood function surrogate model of the PCE migration model,and analyze the uncertainty of parameters in PCE migration simulation. The posterior distribution of the model parameters is obtained,and it is found that the background medium permeability k1,the fitting coefficient n1 and the weak lens permeability k2 can be identified clearly,but the background medium porosity μ1,the weak lens porosity μ2 and the fitting coefficient n2 are basically evenly distributed,which are poorly recognized and insensitive to saturation observations.