Parameter estimation and prediction for groundwater contamination based on measure theory

Abstract

The problem of groundwater contamination in an aquifer is one with many uncertainties. Properly quantifying these uncertainties is essential in order to make reliable probabilistic-based predictions and decisions regarding remediation strategies. In this work, a measure-theoretic framework is employed to quantify uncertainties in a simplified groundwater contamination transport model. Given uncertain data from observation wells, the stochastic inverse problem is solved numerically to obtain a probability measure on the space of unknown model parameters characterizing groundwater flow and contaminant transport in an aquifer, as well as unknown model boundary or source terms such as the contaminant source release into the environment. This probability measure is used to make predictions of future contaminant concentrations and to analyze possible remediation techniques. The ability to identify regions of small but nonzero probability using this method is illustrated.