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

TL;DR

This paper introduces a joint-inversion methodology combining hydrogeological, self-potential, and magnetotelluric data to estimate subsurface hydraulic conductivity without relying on petrophysical relationships, improving accuracy and robustness.

Contribution

The novel joint-inversion approach integrates SP and MT data without assuming petrophysical relationships, enhancing subsurface property estimation accuracy.

Findings

Joint inversion estimates hydraulic conductivity with 25% better accuracy.

SP data effectively connect groundwater flow to geophysical measurements.

Method is robust even without known petrophysical relationships.

Abstract

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.