Comparison of the pyGIMLi and BERT packages for landslide and cavity detection: A case study from Tghat-Oued Fez, Morocco

In the present study, two-dimensional (2D) electrical resistivity tomography (ERT) and vertical electrical sounding (VES) techniques were adopted to predict and detect landslides and subsurface voids occurrences in the Tghat-Oued Fez district of Fez city, Morocco. The purpose of this research is to examine the effects of electrical-based applied geophysics methods for data inversion and modelling using open-source algorithms for spatial distribution and shape of sliding and underground cavities as well as fractured zones in the study area. The data acquisition was based on nine ERT profiles using a dipole–dipole electrode array configuration and 72 VES soundings laid out on an area in which underground cavities were expected. 2D electrical sections, derived from electrical resistivity tomography measurements, in terms of electrical resistivity contrast, correctly highlighted the site's well-defined lithology, identified the presence of cavities with variable geometry, and were capable of inferring the sliding surface. VES-based measurements, in conjunction with the ERT technique, mapped the potential presence of near-surface voids inside a conductive marly formation caused by high electrical resistivity anomalies and delimited the conglomerate roof layer. The joint combination of these two electrical-based geophysical methods along with the numerical modelling and inversion of the data structure and parameters using the useful features of the open-source python-based environment pyGIMLi and BERT, have demonstrated their capability to infer near-surface voids in the study area. The geophysical survey results suggest that the integrated geophysical approach is a reliable and capable geophysical tool and could be effectively used for landslide, and cavity detection and to assess the risk of those subsidence-prone areas. Thus, this approach could be easily applied and reproduced in those areas with similar characteristics.