Electrical resistivity (ER, sometimes called ERI for electrical resistivity imaging or ERT for electrical resistivity tomography) is a direct-current (or low-frequency alternating-current) geophysical method that can be used to estimate the spatial and, in some applications, temporal distribution of subsurface bulk electrical resistivity, which describes the intrinsic resistance to electric current flow in geologic media. Bulk electrical resistivity, or its reciprocal, bulk electrical conductivity, is related to rock type, grain size, porosity, pore fluid electrical conductivity, saturation, and temperature; these relations underlie the utility of ER for cost-effective civil engineering and environmental studies, including imaging of lithology, differences in water saturation below ground surface, permafrost distribution, location of clays, and groundwater fluid conductivity, among other properties and processes as outlined in this book.
Although water in its pure state is non-conductive, the presence of dissolved salts in solution produces a conductive electrolyte to which ER methods are sensitive (e.g., Zohdy et al., 1974); hence, these techniques can be used to monitor multiple hydrogeologic processes such as infiltration, migration of ionic tracers or chemical amendments, and groundwater/surface water interactions. ER offers important benefits for hydrogeological studies: (1) many features, such as clay layers, variable moisture content, high salinity, low-porosity areas, and others, manifest as detectable electrical conductivity contrasts and vary in space; (2) instrumentation is relatively inexpensive, robust, and easy to operate; (3) instrumentation is mature and available commercially; and (4) ER measurements are amenable to automation, allowing for long-term, continuous, cost-effective monitoring.
Currently, while there are standards in terms of array types (Wenner, Schlumberger, dipole-dipole, as outlined in this book), there exist no community-accepted standards for ER survey design (i.e., which measurements are collected), quality assurance and quality control (QA/QC), or data analysis. In this book we review ER technology, the physics underlying ER measurements, and modeling and inversion approaches used in common ER software packages. We outline guidelines to ensure (1) design of robust survey geometries; (2) selection of appropriate acquisition and inversion parameters; and (3) documentation of data-collection configurations, QA/QC, and analysis procedures.
These practices are then demonstrated using a case study from the Defense Reutilization and Marketing Office (DRMO) Superfund site, in Brandywine, Maryland, USA (Johnson et al., 2014). Our objective is to document the best state-of-the-practice for an audience of hydrogeology students and practitioners while providing sufficient details on the mechanics of the method to relate the strengths and limitations of the data acquired. We refer more expert readers who are interested in advanced approaches to Johnson and others (2010), Singha and others (2014), or Binley and Slater (2020), all of which provide valuable reviews of theory and applications of electrical imaging methods for a variety of systems.
Early ER field measurements relied on labor-intensive methods to build up information on the vertical (one dimensional, i.e., 1-D) variation of bulk electrical conductivity with depth or along a profile. The concept of modern electrical imaging was first described by Lytle and Dines (1978) and the first field demonstrations emerged in the 1990s (e.g., Griffiths et al., 1990). Over the last three decades, advances in ER hardware have resulted in multi-channel systems capable of controlling hundreds of electrodes and acquiring thousands of measurements per hour. During this same period, advances in software and computing power have led to the proliferation of user-friendly programs for ER inverse modeling (e.g., Cockett et al., 2015; Rucker et al., 2017; Blanchy et al., 2020) or, more simply, inversion, which refers to the mathematical process of estimating unknown subsurface parameter values from measured data. Inversion of three-dimensional (3-D) datasets through time, often called 4-D, is now becoming commonplace. Binley and Slater (2020) provide a recent review of ER methods that may be of interest to students moving beyond this text.
ER imaging suffers from several limitations that include: (1) the need for direct contact with the subsurface, which is problematic in areas with resistive surficial materials such as highways or permafrost (the exception is capacitively coupled systems for surface measurements, which do not require emplaced electrodes, but require resistive surficial materials and are not discussed here); (2) significant labor for electrode array deployment, particularly for long (many hundreds of meters) or 3-D arrays; (3) data collection can be slow and can limit monitoring of rapid dynamic processes depending on instrumentation and the number of electrodes; and (4) substantial user knowledge is required for processing of data, despite commercially available code (some options are listed in Section 4.1), if quantitative, rather than qualitative, interpretation of hydrogeologic processes is required.
Perhaps the biggest disadvantage is that, just like other geophysical techniques, we are dealing with proxies of what we want to actually measure. Bulk electrical conductivity has multiple dependencies that can complicate interpretation for a specific parameter or process. In addition, choices of regularization parameters and weighting the importance or accuracy of measurements may affect the magnitude and smoothness of ER estimates, further complicating quantitative conversion of electrical proxies to estimates of other physical properties (e.g., Day-Lewis et al., 2005). Collecting ER data through time, called time–lapse imaging as described in more detail throughout this book, is one way to alleviate this problem.
We also review an increasingly popular extension of ER in groundwater studies known as induced polarization (IP). This method measures transient voltages that result from temporary, reversible storage of electric current in the Earth—similar to the storage of water in aquifer systems as defined by storativity in the time-varying groundwater flow equation—whereas ER is defined by a steady-state flow equation, as outlined below. The charge storage measured with IP primarily results from the electrical double layer forming at the mineral-fluid interface or at pore constrictions that locally reduce ionic mobility. IP measurements are consequently strongly sensitive to grain size, surface area, and pore size. When correctly acquired, the IP dataset provides valuable additional information that helps to constrain the hydrogeological interpretation of the subsurface relative to that possible from ER alone. This has important implications, including improved estimation of hydraulic conductivity from geophysical proxies (Slater, 2007). Most ER instruments allow an IP measurement to be made and most software for the interpretation of ER datasets supports the processing of IP measurements (if acquired) along with ER. Though IP is not commonly included in hydrogeologic studies because the time to collect IP data in the field is longer and processing and interpretation of ER and IP measurements combined requires additional expertise and understanding over that needed to use ER datasets effectively, as discussed in this book. Together, ER and IP comprise methods generally known as electrical imaging, which is the topic of this book.