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

Tufts University, Medford MA

Investigators

Abstract

Project Abstract Multi-Modal and Shape-Based Inverse Methods for the Characterization of DNAPL Source Zone Architecture Groundwater 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.