CMG Collaborative Research: Subsurface Imaging and Uncertainty Quantification.

CMG 合作研究：地下成像和不确定性量化。

• 批准号: 0934594
• 负责人: Liliana Borcea
• 金额: $ 15万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0934594&HistoricalAwards=false
• 关键词: CMG; Collaborative; Research; Subsurface; Imaging

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.

这是一个多学科合作的三年期项目，解决了确定（或“成像”）地下地质材料的位置及其控制地下水和污染物运动的物理特性的空间分布的基本问题。 这些空间变化发生在复杂的模式和所有大小规模。 需要对这些变化进行准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。 来自斯坦福大学、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在数学、统计学、建模和水文地质学方面具有重叠专长的团队。 理论和建模的发展将结合控制实验在现场规模的测试设施（博伊西水文地球物理研究基地，或BHRS）与三个已知规模的沉积结构和属性变化，包括层和透镜体与高对比度和梯度边界。 特别是，研究小组将：（i）为反问题的分析奠定坚实的数学基础（或成像）在关于测量的完整性的现实假设下，包括用于表示复杂系统的改进方法;（ii）采用利用计算中的最新进展和趋势的新颖统计工具;（三）发展新的随机分析方法（或统计上不确定的）具有现实可变性的系统;（iv）将这些发展与实验研究相结合，并根据BHRS提供的档案数据集对模型性能进行独立评价;和（v）推进一种新兴的现场方法（水力层析成像），以获取数据集，用于模拟含水层中的三维导水率分布。 学生和博士后科学家将与高级研究人员合作，并将参与该项目的各个方面，以获得跨学科的知识和经验。 除了通过同行评审的文献和专业会议进行传播外，该小组还将利用该项目的数据和模型开发基于网络的教程和培训集，以及关于该项目的实地和建模方法的短期课程。 该项目对社会以及科学和工程基础设施产生了广泛的影响。 大多数可用的淡水储存在地下。 地下水是超过50%的美国人的主要水源，在农村地区约为95%。 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。 这项研究将导致更好的方法来管理这一重要的资源，通过开发下一代的地下成像能力的基础上，在数学的进步，逆建模，随机微分方程，多尺度模拟，和新的领域的方法，如液压层析成像。