Collaborative Research: Fusion of Tomography Tests for DNAPL Source Zone Characterization: Technology Development and Validation

合作研究：DNAPL 源区表征的断层扫描测试融合：技术开发和验证

• 批准号: 0229713
• 负责人: Walter Illman
• 金额: $ 14万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-05-01 至 2006-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0229713&HistoricalAwards=false
• 关键词: Collaborative; Research; Fusion; Tomography; Tests

0229713IllmanDense Nonaqueous Phase Liquids (DNAPLs) are prevalent at a large number of sites throughout the world. The variable release history and geologic heterogeneity make the distribution of DNAPLs in the source zone complex. These source zones can contribute to long-term groundwater contamination for decades to centuries. Therefore, the spatial distribution, mass, and composition of DNAPLs present in the source zone need to be characterized in great detail so that efficient remediation schemes can be designed. During the last few years, many tracer techniques have been introduced to enhance the characterization of DNAPL source zones. While these tracer techniques allow for the in situ estimation of volume-averaged values of DNAPL saturation, there is an urgent need for the development of a cost-effective technology to characterize the spatial distribution of DNAPLs at high resolutions.The objectives of this proposed study are: 1) to develop a software/hardware package that fuses different types of information using a stochastic approach to provide a cost-effective characterization, monitoring, and predictive tool for the DNAPL source zone, 2) to conduct laboratory experiments to test and verify this proposed technology, and 3) to distribute the results of the research to assist scientists, engineers, and managers to solve DNAPL contamination problems. Intellectual Merit: The proposed new data processing technique, stochastic information fusion, combines different types of measurements taken at different locations over different scales in an iterative manner to provide the best estimate of the DNAPL residual distribution and its uncertainty. Specifically, it analyzes the information derived from hydraulic tomography to identify hydraulic heterogeneity first in three-dimensions. It then improves the estimate of the heterogeneity by incorporating new information acquired from the conservative tracer tomography. Afterward, the improved estimate of hydraulic heterogeneity is used to simulate the hydraulic tomography such that more detailed information about the response of the subsurface becomes available. This new information again is fed back to the technique to update the estimate of hydraulic heterogeneity. The iterative process continues until all available information and measurements are fully utilized in identifying the processes and variables that control the spatial distribution of DNAPLs. Upon completion, the newly derived knowledge of the processes and variables are then combined with data derived from the partitioning tracer tomography to effectively delineate the spatial distribution of DNAPL residual saturation in the source zone. The proposed tomography technique and the stochastic fusion of information algorithm are to be tested and validated in a sandbox. Success of the proposed research advances not only estimation theory in general but also our technologies for characterizing and monitoring the subsurface.Broader Impacts: The proposed stochastic fusion technology can be integrated with different characterization techniques in diverse geological conditions. It is also amenable to all stages of DNAPL source zone characterization including initial screening, site characterization, remediation, and long-term monitoring. A web-based virtual hydraulic/tracer tomography laboratory will be created and be available to any student, educator, practitioner, and researcher around the world. We believe this virtual laboratory will stimulate creativity to revolutionize not only classical subsurface hydrology but also other disciplines of hydrologic sciences. For example, using our stochastic information fusion technology, one may be able to assimilate meteorological information such as lighting and precipitation as alternative excitation sources for tomographic surveys of the subsurface environment at large scalesseeing into the earth.

IllmanDense非水相液体(DNAPL)广泛存在于世界各地的许多场所。可变的释放史和地质非均质性使得DNAPL在源区的分布复杂。这些源区可能会造成数十年至数百年的长期地下水污染。因此，需要非常详细地描述源区存在的DNAPL的空间分布、质量和组成，以便能够设计有效的补救方案。在过去的几年里，已经引入了许多示踪技术来加强DNAPL源区的表征。虽然这些示踪剂技术允许原位估计DNAPL饱和度的体积平均值，但迫切需要开发一种经济高效的技术来表征高分辨率的DNAPL的空间分布。本研究的目标是：1)开发一个利用随机方法融合不同类型信息的软件/硬件包，为DNAPL源区提供经济有效的表征、监测和预测工具；2)进行实验室实验以测试和验证所提出的技术；以及3)分发研究结果，以帮助科学家、工程师和管理人员解决DNAPL污染问题。智能优点：拟议的新数据处理技术，随机信息融合，以迭代的方式结合在不同位置在不同尺度上进行的不同类型的测量，以提供对DNAPL残差分布及其不确定性的最佳估计。具体地说，它通过分析水力层析成像获得的信息，首先在三维上识别水力非均质性。然后，它通过结合从保守示踪剂断层扫描获得的新信息来改进对异质性的估计。然后，改进的水力非均质性估计被用来模拟水力层析成像，以便获得关于地下响应的更详细的信息。这一新信息再次反馈给更新水力非均质性估计的技术。迭代过程一直持续到充分利用所有可用的信息和测量，以确定控制DNAPL空间分布的过程和变量。完成后，将新获得的过程和变量知识与分区示踪剂层析成像获得的数据相结合，有效地圈定DNAPL剩余饱和度在源区的空间分布。提出的层析成像技术和信息随机融合算法将在沙箱中进行测试和验证。这项研究的成功不仅在总体上促进了估计理论的发展，而且也促进了我们表征和监测地下地表的技术的发展。广泛的影响：所提出的随机融合技术可以在不同的地质条件下与不同的表征技术相结合。它还适用于DNAPL源区鉴定的所有阶段，包括初步筛选、场地鉴定、补救和长期监测。将创建一个基于网络的虚拟水力/示踪剂断层扫描实验室，供世界各地的任何学生、教育工作者、从业者和研究人员使用。我们相信，这个虚拟实验室将激发创造力，不仅将彻底改变经典的地下水文学，而且还将彻底改变水文科学的其他学科。例如，使用我们的随机信息融合技术，人们可能能够吸收诸如照明和降水等气象信息作为替代激励源，用于大范围地观察地球的地下环境的层析测量。