ABSTRACT:

Hydrogeophysical techniques, such as electrical resistivity tomography (ERT), significantly enhance our ability to observe fluid transport and transformation within highly heterogeneous subsurface environments, as well as aid in inferring hydrological models. Despite their efficacy, these methods encounter discrepancies and uncertainties related to data acquisition and geophysical inversion. To address these issues, joint inversions emerge as preferred methodologies, aiming to reduce ambiguity and establish a unified earth model. A primary challenge in this approach is the effective integration of prior information into the joint inversion framework. Particularly in cases involving multiple datasets focused on a single physical property (e.g., electrical resistivity), there exists an inherent and intrinsic parameter relationship that links collocated resistivity models, suggesting a consistent subsurface geoelectric structure. Addressing this, we introduce the concept of intrinsic parameter relationship coupling within compositional joint inversion frameworks. This method is applied to field scenarios involving Wenner, Wenner-Schlumberger, and dipole-dipole datasets to delineate preferential seepage pathways. Our observations indicate that the intrinsic parameter relationship coupling scheme effectively resolves discrepancies in data coverage, sensitivity, and Signal-to-Noise Ratio (SNR). This research contributes to the field of hydrogeology by providing more accurate resistivity estimates and distributions, utilizing multiple ERT datasets derived from varied electrode configurations.