Joint Inversion of Seismic and Geoelectrical Data Targeting Geological Faults

Mauricio Arboleda-Zapata , Olaf Arie Cirpka, Peter Dietrich, Emmanuel Léger, Carsten Leven

Near-surface geological faults play a crucial role in subsurface systems because they influence fluid flow and transport and can cause geotechnical failure. Topography can sometimes indicate the presence of fault zones, particularly in active tectonic settings where geomorphic features such as triangular facets are preserved. However, particularly for older inactive faults, characteristic surface geomorphic signatures may have completely been removed by erosion, or the fault may be buried beneath younger sediments. Although direct observations from outcrops or borehole data can confirm the presence of a fault very reliably, such data is commonly too sparse and/or distant to precisely constrain fault location in the target area. In this context, near-surface geophysical methods, such as electrical resistivity tomography (ERT) and seismic refraction tomography (SRT), may be useful to locate faults. However, these geophysical data are typically inverted using smoothness-constrained approaches, which can obscure sharp structural features unless the latter are explicitly accounted for in the models. In this study, we present a workflow that enhances the identification of fault zones by jointly inverting collocated ERT and SRT data, considering a parameterization approach based on a layered subsurface model explicitly including a fault. Unlike conventional approaches, we run multiple inversions in which the starting models are initialized with randomly generated fault geometries, guiding the search toward different structural scenarios that yield comparable data misfits. In the absence of additional prior information, these solutions should be interpreted as equally plausible representations of the subsurface. The resulting ensemble of models provides a practical indication of where the structure is well constrained (i.e. features that appear consistently across solutions) and where ambiguity remains (i.e. features that vary among solutions). We showcase the applicability of our workflow with synthetic and field data, where collocated ERT and SRT measurements were collected across expected fault zones. Our results show that the joint inversion of ERT and SRT data consistently narrows the fault location to within a few meters. Iterative incorporation of prior information (e.g., one vs. two boreholes) leads to significant improvements in model geometry and physical property estimates. However, consistent with the intrinsic ambiguity of geophysical inversion, the results remain non-unique; even models depicting opposite fault types (normal vs. reverse) may reproduce the data to comparable levels. This outcome highlights the strength of the workflow in identifying competing structural scenarios that are equally consistent with the observations. Thus, the resulting ensemble provides essential guidance for targeted ground-truthing and follow-up investigations aimed at resolving the remaining ambiguity.