Multi-modal, shape-based Inverse Methods for the Characterization of DNAPL Source Zone Architecture

项目摘要用于表征DNAPL源区结构的多模式和基于形状的逆方法稠密的非水相液体(DNAPL)对地下水的污染在美国和世界范围内都是一个主要的社会问题。旨在从地下彻底清除污染物的昂贵的现场修复方法未能为这一问题提供全面的解决方案，从而导致最近对旨在减少下游污染物质量通量的修复方法的兴趣增加。发展对DNAPL污染在源区的空间分布的定量了解对于基于通量的补救和管理战略至关重要。事实上，已经表明，这种源区结构与羽流的下游行为密切相关。不幸的是，源区结构的估计是一个非常具有挑战性的逆问题。我们提出了一种基于水文(向下梯度通量和浓度)和地球物理(电阻抗断层成像)数据的联合物理反演的源区表征方法。我们的处理方法通过使用源区的新表示来解决这个逆问题的不适定性质。我们没有使用有限的数据来恢复DNAPL空间分布的精细、像素化的表示，而是将池、神经节和非污染区域的边界参数化。正在开发算法来估计这种几何形状以及污染区域中空间变化的DNAPL饱和度。在图像处理和计算机视觉领域最新工作的基础上，我们采用了一种新形式的参数活动轮廓模型来描述池和神经节区域的边界。这些模型结合了传统水平集思想的拓扑灵活性和与蛇相关的低阶参数表示。我们使用一套广泛的数值模拟和一组实验室规模的实验来评估我们的方法的性能。模拟和实验将探索(A)将地球物理变量映射到水文变量的阿尔奇型混合规则的准确性和实用性，以及(B)该方法对地下表面电学和水文属性中未建模的体积非均质性的稳健性。智力优势：从本研究中获得的知识将通过将这些概念扩展到水文学来改进基于几何的反演方法。除了推动水文学领域的发展，我们的研究还将通过开发新的、基于形状的方法来扩展数学成像，用于多模式反问题。这项研究还试图量化与使用阻抗和水文数据来表征准静态DNAPL源区架构相关的限制。广泛的影响：该项目有可能影响基础科学、工程和教育培训领域。对源区架构的正确识别将为设计和选择适当的补救策略提供指导。该项目开发的方法具有在地球科学、医学成像和无损评估等领域的应用潜力，因为从高度异质的数据源中提取几何信息的问题在所有这些领域都是普遍存在的。

Project AbstractMulti-Modal and Shape-Based Inverse Methods for the Characterization of DNAPL Source Zone ArchitectureGroundwater contamination by dense, non-aqueous phase liquids (DNAPLs) represents a major societal problem both within the United States and worldwide. Costly in situ remediation methods directed at the complete removal of contaminant mass from the subsurface have failed to provide a comprehensive solution to this problem thereby leading to recent, increased interest in an approach to remediation aimed at the reduction of downstream contaminant mass flux. Developing a quantitative understanding of the spatial distribution of DNAPL contamination in the source zone is critical to flux-based remediation and management strategies. Indeed, it has been shown that this source zone architecture is closely linked to downstream behavior of the plume. Unfortunately, the estimation of the source zone architecture is a very challenging inverse problem.We propose an approach to source zone characterization based on the joint, physics-based inversion of hydrological (down-gradient flux and concentration) and geophysical (electrical impedance tomography) data. Our processing approach addresses the ill-posed nature of this inverse problem by employing a novel representation of the source zone. Rather than using the limited data to recover a fine scale, pixilated representation of the spatial distribution of DNAPL, we parameterize the boundaries separating pools, ganglia, and non-contaminated regions. Algorithms are being developed to estimate this geometry along with the space-varying DNAPL saturation in the contaminated zones. Building on recent work in the image processing and computer vision fields, we employ a new form of parametric active contour models to describe the boundaries of the pool and ganglia regions. These models combine the topological flexibility of traditional level set ideas with the low order parametric representation associated with snakes. The performance of our approach is evaluated using an extensive suite of numerical simulations, as well as a set of laboratory-scale experiments. Simulations and experiments will explore (a) the accuracy and utility of Archie-type mixing rules for mapping geophysical to hydrological variables, and (b) the robustness of the method to un-modeled volumetric heterogeneities in both the electrical and hydrological properties of the subsurface.Intellectual Merit: Knowledge gained from this research will improve geometry-based methods for inversion by extending these concepts into hydrology. In addition to advancing the field of hydrology, our research will expand mathematical imaging through the development of new, shape-based methods for multi-modal inverse problems. This research also seeks to quantify the limits associated with using electrical impedance and hydrological data to characterize quasi-static DNAPL source-zone architecture.Broader impact: This project has potential to impact areas of basic science, engineering, and educational training. Proper identification of source zone architecture will provide guidance in designing and choosing appropriate remediation strategies. The methods developed in this project have the potential for application in fields such as earth sciences, medical imaging, and nondestructive evaluation, as the problem of extracting geometric information from highly heterogeneous data sources is widely encountered in all these areas.