Manifold Regression Framework for Characterizing Source Zone Architecture

Abstract

In this paper, we develop machine learning approaches for estimating quantitative features (or metrics) characterizing subsurface zones of chemical contamination, focusing on problems involving dense nonaqueous-phase liquid (DNAPL). Source zone characterization, a necessary first step in the development of a remediation strategy, is challenging due to practical constraints associated with the data available for processing. Our methods focus on the use of manifold regression techniques for estimating source zone metrics related to the distribution of contaminant mass in highly saturated pool regions, as well as more diffuse ganglia regions, based on downgradient measurements of dissolved contaminant concentration at a defined time. We use manifold methods for jointly representing labeled training data composed of known source zone metrics, as well as features derived from the corresponding dissolved concentration data sets. We then propose a new integrated approach to the problems of 1) robustly embedding test data (downgradient dissolved concentration) into the manifold when the source zone metrics are not available and 2) constructing a regression function operating directly in the manifold space for source zone metric estimation. The utility of the approach is enhanced by the explicit incorporation of physical constraints associated with the metrics into the problem formulation. Results based upon simulated data demonstrate the potential effectiveness of the manifold regression approaches, as well as significant improvement in performance relative to the case where the algorithmic components are designed serially.