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.

这是一个合作的多学科三年项目，它解决了确定（或“成像”）地下地质材料的位置的基本问题，以及控制地下水和污染物运动的物理特性的空间分布。 这些空间变化以复杂的模式和所有尺寸尺度出现。 需要准确成像这些变化的地下工程应用程序包括可靠的环境监测，预测性建模和有效的地下水修复。 该项目将开发下一代的地下成像工具，以显着改善形成和财产分布的估计，并改善相应的预测不确定性的量化，以为管理或政策决策提供合理的基础。 由斯坦福大学，米饭，犹他州和博伊西州立大学组装的数学，统计，建模和水文学专业知识重叠的科学家和工程师团队。 理论和建模的发展将与具有三种已知沉积结构和财产变化的已知尺度的现场测试设施（Boise Hydroegophysical Research站点或BHR）结合使用，包括具有高对比度和渐变边界的层和镜头。 特别是，研究团队将：（i）在对测量完整性的现实假设下，为分析反问题（或成像）的分析建立坚定的数学基础，包括改进的代表复杂系统的方法； （ii）采用新颖的统计工具来利用计算的最新进展和趋势； （iii）开发具有现实可变性的随机（或统计上不确定）系统的新分析方法； （iv）将这些发展与实验研究和对模型性能的独立评估与BHR可用的存档数据集相结合； （v）推进新兴场方法（液压断层扫描），以获取用于对含水层中3D液压电导率分布进行建模的数据集。 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传播外，该团队还将开发基于Web的教程和培训集，其中包括来自项目的数据和模型，以及该项目的现场和建模方法的简短课程。 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。 大多数可用的淡水存储在地下。 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消耗掉。 在世界人口增长最快的沿海地区，随着地下水水平下降和/或海平面的上升，海水侵入含水层。 这项研究将通过基于反向建模，随机微分方程，多规模仿真和新的现场方法（如液压层析成像）的进步来开发下一代地下成像功能，从而为管理这一重要资源的管理提供更好的方法。