Stochastic Flow and Transport in Heterogeneous Soils: A New Approach for Measurement Conditioning

人们逐渐认识到，非均质土壤中溶质运移是极其难以预测的。 使预测复杂化的因素包括地质不均匀性和数据局限性。 本计画将探讨一种新的方法来预测场尺度的水流与溶质运移，以系统的方式来解释变异性与不确定性。新的方法是基于分布式随机估计理论，结合现场测量与随机地下水模型的预测。 该方法可用于表征地下水污染的程度和严重程度，在危险废物的补救开始之前，并评估在补救过程中的清理技术的进展。 随机模型提供了事先估计的平均值和方差的头和溶质浓度在整个污染的网站。 每当有新的水力传导率、水头和/或浓度测量数据时，这些估计数就会更新。 这些估计值可用于指导取样威尔斯的位置和评估场地特征的准确性。该研究项目旨在确定方法学方向，并解决坚韧的概念和计算问题。 该项目将开发一种新的算法，用于耦合流和输运调节，不受线性，平稳性或遍历性假设的限制。 具体来说，该项目将扩展PI的非平稳谱方法迭代测量条件，并提出了一种新的技术，其有效的实施。 该方法应大大提高估计精度，并显着增加的规模和复杂性的网站表征问题，可以用随机方法进行分析。 第一阶段将测试地下水流问题的非平稳谱条件。 第一阶段研究的目标是：1）使用具体实例证明新理论和特定目标阶段的应用程序，2）通过与现有解决方案技术进行比较，证明新方法的准确性，效率和鲁棒性，以及3）证明新方法对大型地下水系统的可行性。 第二阶段将测试耦合三维流动和运输问题的新方法，并将其应用于预测和描述真实的现场的地下水流和污染。 根据NSF的建议，第2阶段的实施将推迟到第1阶段成功完成。 我们稍后将根据第一阶段的结果向NSF提交一份修订后的提案，其中包括第二阶段的改进研究计划，并为第二阶段的研究提供令人信服的理由。

9805357LiIt is becoming widely recognized that solute transport in heterogeneous soils are extremely difficult to predict. Factors, which complicate the prediction, include geological heterogeneity and data limitations. This project will investigate a new approach to predict field-scale flow and solute transport which accounts for variability and uncertainty in a systematic way. The new approach is based on the distributed stochastic estimation theory which combines field measurements with predictions from a stochastic groundwater model. The approach can be applied to characterize the extent and severity of groundwater contamination at hazardous waste sites before remediation begins and to evaluate the progress of cleanup techniques during remediation. The stochastic model provides prior estimates of the mean and variance of head and solute concentration throughout a contaminated site. These estimates are updated whenever new measurements of hydraulic conductivity, head and/or concentration become available. These estimates may be used to guide the placement of sampling wells and to evaluate the accuracy of the site characterization.The research project is intended to be methodological in orientation and address tough conceptual and computationalproblems. The project will develop a new algorithm for coupled flow and transport conditioning that is not restricted by the assumptions of linearity, stationarity, or ergodicity. Specifically, the project will extend the PI's nonstationary spectral approach to iterative measurement conditioning and present a novel technique for its efficient implementation. The approach should greatly improve the estimation accuracy and dramatically increases the size and complexity of the site characterization problems that can be analyzed with stochastic methods.The proposed research will proceed in two phases. The first phase will test nonstationary spectral conditioning on a groundwater flow problem. The objective of phase 1 study is to 1) demonstrate the new theory and specific objective phase is to application procedure using a concrete example, 2) demonstrate the accuracy, efficiency, and robustness of the new approach by comparing it with existing solution techniques, and 3) demonstrate the feasibility of the new approach for large groundwater systems. The second phase will test the new approach for coupled three-dimensional flow and transport problems and apply it to predict and characterize groundwater flow and contamination at a real field site. As suggested by NSF implementation of phase 2 will be deferred until phase 1 is successfully completed. We will later submit a revised proposal to NSF with a refined research plan for the second phase based on phase 1 results and make a compelling case for phase 2 investigation.