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

[805357]人们逐渐认识到，非均质土壤中的溶质迁移极难预测。使预测复杂化的因素包括地质非均质性和数据局限性。该项目将研究一种新的方法来预测场尺度的流动和溶质输运，以系统的方式解释变异性和不确定性。该方法基于分布式随机估计理论，将现场测量与随机地下水模型预测相结合。该方法可用于在开始补救之前确定危险废物场址地下水污染的程度和严重程度，并在补救期间评价清理技术的进展情况。随机模型提供了整个污染场地的水头和溶质浓度的均值和方差的先验估计。每当有新的水力导电性、水头和/或浓度测量数据可用时，这些估计都会更新。这些估计可用于指导取样井的布置和评估地点表征的准确性。该研究项目旨在以方法论为导向，解决棘手的概念和计算问题。该项目将开发一种新的流量和输送耦合调节算法，该算法不受线性、平稳或遍历假设的限制。具体来说，该项目将PI的非平稳光谱方法扩展到迭代测量调节，并提出一种有效实施的新技术。该方法将大大提高估计精度，并显著增加可用随机方法分析的位点表征问题的规模和复杂性。拟议的研究将分两个阶段进行。第一阶段将测试地下水流动问题的非平稳光谱调节。第一阶段研究的目标是1)证明新理论，具体目标阶段是通过具体实例应用程序，2)通过将新方法与现有解决技术进行比较，证明新方法的准确性、效率和鲁棒性，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，并将其应用于实际现场的地下水流动和污染的预测和表征。根据国家科学基金会的建议，第二阶段的实施将推迟到第一阶段成功完成。我们将以第一阶段的研究结果为基础，向NSF提交修改后的第二阶段研究计划，并为第二阶段的研究提出令人信服的理由。

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.