Abstract
Electrical Resistivity Tomography (ERT) is a robust and well-consolidated method largely applied in near-surface geophysics. Nevertheless, the mapping of the spatial resistivity patterns of the subsurface at a depth greater than 1 km was performed in just a few cases by the ERT method, called deep ERT (DERT). Since, in many cases, the term DERT was adopted with ambiguity for geoelectrical explorations varying in depth within a range of 0–500 m, the main goal of this review is to clearly define the DERT method, identifying a threshold value in the investigation depth. The study focuses both on the purely methodological aspects (e.g., geoelectrical data processing in low noise-to-signal ratio conditions; tomographic algorithms for data inversion) and on the technological features (e.g., sensor layouts, multi-array systems), envisaging the future directions of the research activity, especially that based on machine learning, for improving the geoelectrical data processing and interpretation. The results of the more significant papers published on this topic in the last 20 years are analyzed and discussed.
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