CMG Collaborative Research: Subsurface Imaging and Uncertainty Quantification.

这是一项为期三年的多学科合作项目，旨在解决确定（或“成像”）地下地质物质的位置及其物理性质的空间分布的基本问题，这些物理性质控制着地下水的运动和污染。这些空间变化以复杂的模式和各种尺度发生。需要对这些变化进行精确成像的地下工程应用包括可靠的环境监测、预测建模和有效的地下水修复。该项目将开发下一代地下成像工具，以显著改善对地层和属性分布的估计，并改善相应预测不确定性的量化，为管理或政策决策提供良好的基础。斯坦福大学、赖斯大学、犹他州大学和博伊西州立大学的科学家和工程师组成了一个团队，他们在数学、统计学、建模和水文地质学方面具有重叠的专业知识。理论和建模的发展将与现场规模测试设施（博伊西水文地球物理研究基地，或BHRS）的控制实验相结合，该设施具有三种已知的沉积结构和属性变化尺度，包括具有高对比度和渐变边界的层和透镜。特别是，研究小组将：(i)发展一个坚实的数学基础，以便在关于测量完整性的现实假设下分析反问题（或成像），包括改进表示复杂系统的方法；（ii）采用利用计算方面最新进展和趋势的新颖统计工具；（iii）为具有现实可变性的随机（或统计不确定）系统开发新的分析方法；（iv）将这些发展与实验研究结合起来，并根据BHRS提供的存档数据集对模型性能进行独立评估；(v)提出了一种新兴的油田方法（水力断层扫描），以获取数据集，为含水层的三维水力导电性分布建模。学生和博士后科学家将与资深研究人员一起工作，并将参与该项目的各个方面，以获得跨学科的知识和经验。除了通过同行评议文献和专业会议进行传播外，该团队还将开发基于网络的教程和培训集，其中包括项目中的数据和模型，以及项目中的实地和建模方法的短期课程。该项目对社会、科学和工程基础设施有着广泛的影响。大部分可用的淡水都储存在地下。地下水是超过50%的美国人的主要水源，大约95%的农村地区是地下水。在世界上，许多最重要的含水层正在逐渐枯竭。在世界人口增长最快的沿海地区，随着地下水位下降和/或海平面上升，海水侵入含水层。这项研究将基于逆建模、随机微分方程、多尺度模拟和水力层析成像等新的现场方法的进步，通过开发下一代地下成像能力，为这一重要资源的管理提供更好的方法。

This is a collaborative multi-disciplinary three-year project that addresses the fundamental problem of determining (or "imaging") the location of subsurface geologic materials and the spatial distributions of their physical properties that control movement of groundwater and contamination. These spatial variations occur in complex patterns and at all size scales. Subsurface engineering applications that require accurate imaging of these variations include reliable environmental monitoring, predictive modeling, and efficient groundwater remediation. The project will develop the next generation of subsurface imaging tools to significantly improve estimates of formation and property distributions, and to improve quantification of the corresponding predictive uncertainty to provide a sound basis for management or policy decisions. A team of scientists and engineers with overlapping expertise in mathematics, statistics, modeling, and hydrogeology has been assembled from Stanford, Rice, Utah, and Boise State universities. Theoretical and modeling developments will be combined with controlled experiments at a field-scale test facility (Boise Hydrogeophysical Research Site, or BHRS) with three known scales of sedimentary structure and property variation, including layers and lenses with both high-contrast and gradational boundaries. In particular, the research team will: (i) develop a firm mathematical foundation for the analysis of inverse problems (or imaging) under realistic assumptions about the completeness of measurements, including improved methods for representing complex systems; (ii) employ novel statistical tools that exploit recent advances and trends in computation; (iii) develop new analytical approaches for stochastic (or statistically uncertain) systems with realistic variability; (iv) combine these developments with experimental studies and independent evaluation of model performance against archive data sets available from BHRS; and (v) advance an emerging field method (hydraulic tomography) to acquire data sets for modeling 3D hydraulic conductivity distributions in aquifers. Students and a post-doctoral scientist will work with senior researchers and will participate in all aspects of this project to gain cross-disciplinary knowledge and experience. In addition to dissemination through peer-reviewed literature and professional meetings, the team will develop web-based tutorials and training sets with data and models from the project, and a short course on field and modeling methods from the project. This project has broad impacts for society and for scientific and engineering infrastructure. Most available freshwater is stored in the subsurface. Groundwater is the primary source of water for over 50 percent of Americans, and for roughly 95 percent in rural areas. In the world, many of the most important aquifers are being gradually depleted. In coastal areas, where world population is growing the fastest, seawater intrudes into aquifers as groundwater levels drop and/or sea levels rise. This research will lead to better methods for management of this important resource by developing the next generation of subsurface imaging capabilities based on advancements in the mathematics of inverse modeling, stochastic differential equations, multi-scale simulations, and new field methods such as hydraulic tomography.