Optimization of Electrical Geophysical Survey Design for Hydrogeological Applications and Subsurface Target Discrimination

Geophysical imaging methods significantly enhance our knowledge of subsurface characteristics and their use has become prevalent over a range of subsurface investigations. These methods facilitate the detection and characterization of both metallic and nonmetallic subsurface targets, and can provide spatially extensive information on subsurface structure and characteristics that is often impractical to obtain using standard drilling and sampling procedures alone. Electrical imaging methods such as electrical resistivity tomography (ERT) have proven to be particularly useful in hydrogeologic and geotechnical investigations because of the strong dependence of the electrical properties of soils to water saturation, soil texture, and solute concentration. Given the available geophysical tools as well as their applications, the selection of the appropriate geophysical survey design is an essential part of every subsurface geophysical investigation. Where investigations are located in an area with subsurface information already available, this information may be used as a guide for the design of a geophysical survey. In some instances, no subsurface information is available and a survey must be designed to cover a range of possible circumstances. Yet, in other instances, there may be significant subsurface information available, but because of subsurface complexities, a geophysical survey must still be designed to cover a broad range of possibilities. Demonstrating the application and limitations of ERT in a specific field application, the first investigation presented in this document provides guidance for developing methods to improve the design and implementation of ERT surveys in a complex subsurface environment. The two investigations that follow present the development of a relatively simple optimization approach based on limited forward modeling of the geophysical response for both static and mobile surveys. This process is demonstrated through examples of selecting a limited number of ERT surveys to identify and discriminate subsurface target tunnels (with a simple cylindrical geometry). These examples provide insights into the practical application of the optimization process for improved ERT survey design for subsurface target detection. Because of their relative simplicity, the optimization procedures developed here may be used to rapidly identify optimal array configurations without the need for computationally expensive inversion techniques