ERT Field Datasets from Wei et al. (2021), Joint Inversion of DC Resistivity Datasets with Multiple Electrode Arrays

These data are described in Wei, Y., Shi, Z., Moorkamp, M., Wang C. and Huang M. (2021). Joint Inversion of DC Resistivity Datasets with Multiple Electrode Arrays

Hydrogeophysical approaches like electrical resistivity tomography (ERT) empower the ability to observe the transport and transformation of fluids in the highly heterogeneous subsurface and infer hydrological models but face discrepancy and uncertainty from data acquisition and geophysical inversion as well. Joint inversions would be preferred schemes to alleviate the ambiguity and construct a unified earth model. However, one of the challenges is how to properly incorporate the prior information into the joint inversion framework. Within the context of multiple single modality (electrical resistivity) datasets, there is always a plain and intrinsic parameter relationship to link the collocated resistivity models, i.e. identical subsurface geoelectric structure. Here, we present the intrinsic parameter relationship coupling under the compositional joint inversion frameworks and perform the intrinsic parameter relationship coupling to delineate the preferential seepage pathways on filed scenarios involving Wenner, Wenner-Schlumberger, and dipole-dipole datasets. It is observed that the null space arising from differences in data coverage, sensitivity, and SNR could mutually be resolved with partial model space improved in the intrinsic parameter relationship coupling scheme. Our research contributes to resolving the hydrogeological challenge of accurate resistivity estimates and distributions using multiple ERT datasets from different electrode configurations.