Equivalent-current inversion to account for infrastructure-related effects in semi-airborne electromagnetic data

Summary

As the demand for European domestic mineral resources increases, exploration is increasingly focused on deeper and covered targets, often located in populated regions. In such environments, the application of electromagnetic (EM) techniques is severely challenged by anthropogenic infrastructure. Metal-bearing structures, including power lines, pipelines, railway tracks, and mine shafts, can strongly distort the EM fields, producing significant artefacts in both the measured data and the resulting inversion models, thereby biasing or hindering geological interpretation. These effects currently limit EM investigations in inhabited areas, despite their potential for non-invasive and efficient exploration of challenging subsurface targets. Focusing on semi-airborne electromagnetic (sAEM) data, we develop a data-driven approach to address infrastructure-related effects. By inverting affected data for the distribution of infrastructure currents, we account for the full coupling between the EM transmitter, the conducting Earth, and the metal infrastructure. In sAEM field experiments, involving a grounded dipole transmitter and an airborne receiver system, we study the distortion by self-built infrastructure and investigate both measured currents in infrastructure as well as its EM coupling. We validate the functionality of our current inversion approach by reproducing the measured currents in a simplified 1D scenario. With a synthetic 3D study, mimicking a sAEM campaign in the presence of infrastructure, we find that the current inversion approach is capable of separating and correcting for infrastructure effects even if the true resistivity distribution of the subsurface is unknown. In particular, other conductive subsurface structures can be resolved well, even directly below infrastructure. On a small sAEM field data example, affected by the impact of a metal-built conveyor belt and pipeline, we demonstrate the applicability of this framework for real-world scenarios.