Inverse estimation of multiple contaminant sources in three-dimensional heterogeneous aquifers with variable-density flows

Groundwater contamination is an exacerbating global issue that severely threatens human health. Subsurface remediation thus has gained increased interest in recent years. To effectively remediate subsurface contaminated sites, one needs to identify the locations of contaminant sources and characterize contaminant spreading. Groundwater contamination is often driven by multiple contaminant sources, making source identification problems challenging. Further, when the densities of dissolved contaminants and ambient groundwater differ, the resultant variable-density flows add complexity to the task of source identification. However, most previous studies are limited to a single source identification in a two-dimensional aquifer. This study presents a novel inversion method based on ensemble smoothing that identifies the locations of multiple contaminant sources in three-dimensional heterogeneous aquifer systems. A new covariance localization algorithm based on a clustering method is integrated into the inversion method, which improves the accuracy of multi-source identification. Using the proposed inversion framework, we successfully estimate the locations of multiple contaminant sources and three-dimensional permeability fields utilizing pressure and concentration data from monitoring wells. Further, we investigate and elucidate the effects of aquifer heterogeneity and variable-density flows on multiple source identification. We find that variable-density flow increases the data information contents and thus im-proves the inversion accuracy. This is the first-time demonstration of the effects of variable-density flow on the inversion accuracy of multi-source identification in three-dimensional heterogeneous aquifer systems.

Automated summary

Groundwater contamination poses a severe threat to human health globally, and subsurface remediation has become increasingly important. This study presents a novel inversion method based on ensemble smoothing to identify the locations of multiple contaminant sources in three-dimensional heterogeneous aquifer systems. The integration of a clustering-based covariance localization algorithm improves the accuracy of multi-source identification.