Assessment of three mixed arrays dataset for subsurface cavities detection using resistivity tomography as inferred from numerical modelling

Abstract

Abstract A three mixed arrays dataset have been evaluated in term of their capability of detectability and enhanced the resolution of the resolved images obtained by the electrical resistivity tomography (ERT) technique. This study is based on numerical modelling for studying the detection of the subsurface cavities, where four cavity models were generated to simulate air-filled cavities embedded in limestone sets at different depths. The synthetic data were generated for the cavity models using three individual arrays. These arrays are dipole-dipole (DD), pole-dipole (PD), and Wenner- Schlumberger (W-S). Then the apparent resistivity data obtained from two different arrays were merged to form a high-resolution single model. Based on the obtained results, a combination between dipole-dipole- Wenner- Schlumberger (DD + WS) yields the highest resolution image regarding cavity detection among the other type of mixed arrays (e.g. pole-dipole, and Wenner- Schlumberger (PD + WS) or dipole-dipole and pole-dipole (DD + PD)). The inverted resistivity sections obtained from (DD and WS) arrays resolved the cavity models more accurately than other types of composite datasets or individual array data, as well as a significant resolution enhancement with depth, was observed. The recovered model’s parameters (e.g. resistivity and geometry) obtained from DD and WS composite data shows closer parameters to the true actual model.