Subsurface Source Zone Characterization and Uncertainty Quantification Using Discriminative Random Fields

A novel statistical approach is developed and implemented for the stochastic reconstruction of nonaqueous phase liquid (NAPL) source zone realizations and the quantification of source zone metrics and associated uncertainty. The approach employs discriminative random field (DRF) models, to simulate the spatial distributions and relationships among source zone properties (i.e., permeability, NAPL saturation, and aqueous concentration distributions) consistent with commonly collected field data. Application of DRF models requires a limited number of full‐scale simulations to train the model parameters. Monte Carlo sampling methods based on these trained models then provide an efficient method to generate contaminant mass realizations conditioned on measured boreholes, bypassing the need to run computationally intensive, partial differential equation‐based simulations of physical flow and transport. Postprocessing of these realizations yields approximations of uncertainty to inform further sampling for characterization and remediation. The reconstructed contaminant mass realizations provide sufficient information for calculating averaged characterization metrics, such as total contaminant mass and pool fraction, used to predict source zone longevity, mass recovery behavior, and remedial performance. The model performance is evaluated through comparisons of these predicted source zone metrics with those obtained from the “true” mass distributions generated with validated flow and transport models. These comparisons clearly demonstrate that stochastic application of a DRF model can reconstruct realistic saturation and concentration fields, conditioned to borehole data at different times. The present study should be viewed as the first step in generating a three‐dimensional characterization tool that can be applied over a wide range of conditions observed at contaminated sites.