Optimization of a mobile capacitatively-coupled geophysical survey used for electrical resistivity tomography

Improving the efficiency of geophysical surveys is becoming more important with the increased application of geophysical methods in subsurface investigations. With this in mind, we propose a relatively simple optimization approach for mobile geophysical surveys. Our approach specifically evaluates the use of a capacitatively-coupled resistivity (CCR) array for electrical resistivity tomography (ERT). We demonstrate our approach through simulating a small number of CCR surveys used to identify and discriminate a target tunnel of interest from other similar tunnels in the subsurface. These examples provide insights into the practical application of the optimization process developed through this study. The mobile electrode arrays that were evaluated used a dipole-dipole configuration with a maximum survey length of 50 m. By evaluating different CCR survey designs and their respective measurement responses to buried tunnels of different size and depth, we show a means to identify and discriminate among tunnels using only voltage measurements. The differences in voltage between surveys were used to create an error response surface for a range of tunnel sizes and depths. These error response surfaces can be converted into target discrimination maps which identify tunnels that can and cannot be discriminated from a selected target tunnel. The fractional discrimination for each tunnel obtained from the target discrimination maps is used to create target identifiability maps which indicate the ability for each survey to discriminate among a range of target tunnels. Considering all possible target tunnels, the average target discrimination for a survey was selected as a measure of survey efficiency. Using this measure of survey efficiency, we provide examples of the potential impact of survey design and speed on the discrimination of target tunnels. In turn, we identify the survey with the highest efficiency and the least number of survey measurements as our optimal survey design.