PNNL

Abstract

Continuing advancements in subsurface electrical resistivity tomography (ERT) are giving the method increasing capability for understanding shallow subsurface properties and processes. The inability of ERT imaging data to uniquely resolve subsurface structure and the corresponding need include constraining information remains one of the greatest limitations, and provides one of the greatest opportunities, for further advancing the utility of the method. In this work we describe and demonstrate a method of incorporating constraining information into an ERT imaging algorithm in the form on discontinuous boundaries, known values, and spatial covariance information. We demonstrate the approach by imaging a uranium-contaminated wellfield at the Hanford Site in southwestern Washington State, USA. We incorporate into the algorithm known boundary information and spatial covariance structure derived from the highly resolved near-borehole regions of a regularized ERT inversion. The resulting inversion provides a solution which fits the ERT data (given the estimated noise level), honors the spatial covariance structure throughout the model, and is consistent with known bulk-conductivity discontinuities. The results are validated with core-scale measurements, and display a significant improvement in accuracy over the standard regularized inversion, revealing important subsurface structure known influence flow and transport at the site.