Improved methodology for deep aquifer characterization using hydrogeological, self-potential, and magnetotellurics data

Estimating subsurface properties like hydraulic conductivity using hydrogeological data alone is challenging in field sites with sparse wells. Geophysical data, including Self-potential (SP) and Magnetotelluric (MT), can improve understanding of hydrogeological structures and interpolate data between wells. However, determining hydraulic conductivity requires a proper petrophysical relationship between hydraulic conductivity and inferred geophysical properties, which may not exist or be unique. In this work, we propose a joint-inversion approach without assuming petrophysical relationships, using self-potential data to connect groundwater flow velocity to electrical potential differences, and MT data to estimate hydraulic conductivity and electrical conductivity. A spectral method is employed for the self-potential forward problem. To accelerate joint data inversion, a dimension reduction technique through the Principal Component Geostatistical Approach is used. The applicability and robustness of the joint-inversion method are demonstrated through inversion tests using hydrogeophysical data sets generated from subsurface models with and without petrophysical relationships. The joint hydraulic head-SP-MT data inversion can reasonably estimate hydraulic conductivity and electrical resistivity, even without knowledge of a one-to-one petrophysical relationship. On average, joint inversion yields a 25% improvement in hydraulic conductivity estimates compared to single data-type inversion. Our proposed joint inversion approach, with SP-data compensating for the absence of a known petrophysical relationship, provided close agreement with joint inversion of head and MT data using a known petrophysical relationship. Successful inversion tests demonstrate the usefulness of SP data in connecting hydrogeological properties and geophysical data without requiring a petrophysical relationship.