Identification of contaminant source architectures—A statistical inversion that emulates multiphase physics in a computationally practicable manner

Abstract

AbstractThe goal of this work is to improve the inference of nonaqueous‐phase contaminated source zone architectures (CSA) from field data. We follow the idea that a physically motivated model for CSA formation helps in this inference by providing relevant relationships between observables and the unknown CSA. Typical multiphase models are computationally too expensive to be applied for inverse modeling; thus, state‐of‐the‐art CSA identification techniques do not yet use physically based CSA formation models. To overcome this shortcoming, we apply a stochastic multiphase model with reduced computational effort that can be used to generate a large ensemble of possible CSA realizations. Further, we apply a reverse transport formulation in order to accelerate the inversion of transport‐related data such as downgradient aqueous‐phase concentrations. We combine these approaches within an inverse Bayesian methodology for joint inversion of CSA and aquifer parameters. Because we use multiphase physics to constrain and inform the inversion, (1) only physically meaningful CSAs are inferred; (2) each conditional realization is statistically meaningful; (3) we obtain physically meaningful spatial dependencies for interpolation and extrapolation of point‐like observations between the different involved unknowns and observables, and (4) dependencies far beyond simple correlation; (5) the inversion yields meaningful uncertainty bounds. We illustrate our concept by inferring three‐dimensional probability distributions of DNAPL residence, contaminant mass discharge, and of other CSA characteristics. In the inference example, we use synthetic numerical data on permeability, DNAPL saturation and downgradient aqueous‐phase concentration, and we substantiate our claims about the advantages of emulating a multiphase flow model with reduced computational requirement in the inversion.