We demonstrate the value of using the self-potential method to study volcanic environments, and particularly fluid flow in those environments. We showcase the fact that self-potential measurements are a highly efficient way to map large areas of volcanic systems under challenging terrain conditions, where other geophysical techniques may be challenging or expensive to deploy. Using case studies of a variety of volcano types, including tuff cones, shield volcanoes, stratovolcanoes, and monogenetic fields, we emphasize the fact that self-potential signals enable us to study fluid flow in volcanic settings on multiple spatial and temporal scales. We categorize the examples into the following three multiscale fluid-flow processes: (1) deep hydrothermal systems, (2) shallow hydrothermal systems, and (3) groundwater. These examples highlight the different hydrological, hydrothermal, and structural inferences that can be made from self-potential signals, such as insight into shallow and deep hydrothermal systems, cooling behavior of lava flows, different hydrogeological domains, upwelling, infiltration, and lateral groundwater and hydrothermal fluid flow paths and velocities, elevation of the groundwater level, crater limits, regional faults, rift zones, incipient collapse limits, structural domains, and buried calderas. The case studies presented in this paper clearly demonstrate that the measured SP signals are a result of the coplay between microscale processes (e.g., electrokinetic, thermoelectric) and macroscale structural and environmental features. We discuss potential challenges and their causes when trying to uniquely interpret self-potential signals. Through integration with different geophysical and geochemical data types such as subsurface electrical resistivity distributions obtained from, e.g., electrical resistivity tomography or magnetotellurics, soil CO2 flux, and soil temperature, it is demonstrated that the hydrogeological interpretations obtained from SP measurements can be better constrained and/or validated.
The electrical self-potential method is a non-intrusive snow-hydrological sensor
