Manifold regression for subsurface contaminant characterization

Characterization of sites contaminated by chemicals such as trichloroethylene, perchloroethylene, and other dense non-aqueous phase liquids (DNAPLs) is a necessary first step in the design and implementation of successful remediation strategies. In this paper, we develop a machine learning-based approach for estimating characteristics of a source zone related to the distribution of contaminant mass in highly saturated pool regions and more diffuse ganglia based on observations of down-gradient concentration images. After extracting a set of morphological features from training images, Laplacian Eigenmaps is employed to embed these features with the known source zone metric in a low dimensional manifold. A spectral regression scheme is used to embed the test data into the same manifold after which a Bayesian approach is employed to estimate the associated metric as well as a confidence interval. Results based upon simulated data demonstrate the potential effectiveness of the overall approach.