Deep Electrical Resistivity Tomography for geophysical investigations: State-of-the-art and future directions.

Electrical Resistivity Tomography (ERT) is a robust method largely applied in near-surface geophysics. Nevertheless, the mapping of the resistivity patterns at a depth greater than 1 km was performed in just a few cases by the ERT method, called Deep ERT (DERT). Information on the electrical properties of the rocks beyond $\sim 1$ km depth has been obtained only from direct soundings or passive electromagnetic measurements ($e.g.$, Magnetotelluric-MT) so far. In this communication, a critical review of the limits and the advantages related to the recent applications of the DERT method in the geophysical exploration is discussed. The study focuses both on the purely methodological aspects ($e.g.$, 4D tomographic algorithms) and on the technological features ($e.g.$, multi-array systems). Great attention has been devoted to the novel applications of the AI-based methods and machine learning for geoelectrical data processing. The results of this review envisage the possibility to use the DERT method for studying a wide spectrum of geophysical problems that are still unsolved and/or poorly studied, from the detection of fluid migration in fault systems to the mapping of ice sheet in polar regions.