Stochastic Modeling of Reactive Solute Transport in Heterogeneous Porous Media

[9304481]鲁宾对地下水中污染物迁移的预测，以及监测和管理，都是基于溶质运动的定量模型。目前预测污染物迁移的方法是不充分的，因为它们不能明确地说明在其位置的测量数据。它们也没有现实地考虑到电导率的非均质性和压力的时间变异性的影响（或仅在一些简化的假设下），它们也没有提供一种设计监测方案的经济手段，在该方案中，观测数据用于更新预测模型。此外，迄今为止，很少有人试图在野外尺度上更好地了解高度非均质地质地层中的运移和化学反应对运移的影响。最后，目前的预测方法无法将预测与任何拟合优度标准联系起来。在这项研究中，我们建议开发一种建模策略，将含水层水力特性、速度场和溶质浓度视为随机空间函数，用初始/边界条件和各种物理参数表示，并允许使用概率定量工具分析空间变异性和预测不确定性。将获得稳态和非稳态非均匀速度场的三维随机函数模型，并将用于预测各向异性电导率场中反应性和非反应性污染物浓度的各矩。贝叶斯分析将使预测具有局部条件。该模型将应用于两个实地研究。我们计划在基本水平上更好地理解输运现象，并获得应用中溶质扩散的风险合格、局部条件估计。我们计划推广现有的随机输运理论，以解释流场中的高阶变异性，研究流动不稳定性和非均匀性的影响，评估非稳定性与典型条件下溶质输运预测的相关性，最后提出将流动不稳定性、流动不均匀性和化学反应纳入预测的简化方法。＊＊＊

9304481 Rubin Predictions of contaminant transport by groundwater, as well as monitoring and management, are based on quantitative models of solute motion. Present methods for prediction of contaminant transport are inadequate because they fail to account explicitly for measured data at its locations. They also do not account realistically for the effects of heterogeneity in the conductivity and temporal variability in pressure (or only under some simplifying assumptions), and they do not offer an economic means of designing a monitoring scheme where the observed data are used to update the predictive models. Furthermore, little attempt has been directed so far to obtain a better understanding of transport in highly heterogeneous geologic formations and the effects of chemical reactions on transport at the field scale. Lastly, current predictive methods fail to link the predictions with any criterion of goodness of fit. In this study, we propose to develop a modeling strategy which considers the aquifer hydraulic properties, the velocity field, and the solute concentration as random space functions, expressed in terms of initial/boundary conditions and various physical parameters, and which allow, both spatial variability and prediction uncertainty to be analyzed with probabilistic, quantitative tools. Three-dimensional random function models will be obtained for steady and unsteady nonuniform velocity fields and will be used for predicting the various moments of the concentration of reactive and non-reactive contaminants for anisotropic conductivity fields. Bayesian analysis will make the predictions locally conditioned. The model will be applied to two field studies. We plan to achieve a better understanding of transport phenomena at the fundamental level and obtain risk- qualified, locally-conditioned estimates of solute spreading in applications. We plan to generalize existing stochastic transport theories to account for higher-order variability i n the flow field, to investigate the influence of flow unsteadiness and nonuniformity, to evaluate the relevance of unsteadiness to the prediction of solute transport under typical conditions, and finally to suggest simplified methods to incorporate flow unsteadiness, flow nonuniformity and chemical reactions into predictions. ***