Cluster Analysis of 3D SRT and ERT in Urban Environment: Cavity Detection in Agrigento (Italy)

Subsurface cavity detection in urban environments poses significant challenges due to restricted access, complex stratigraphy, and anthropogenic noise. This study presents an integrated 3D geophysical approach applied in the historical center of Agrigento (Italy), aimed at identifying potential underground voids beneath narrow streets and densely built areas. A combined acquisition of Electrical Resistivity Tomography (ERT) and Seismic Refraction Tomography (SRT) was conducted using a C-shaped sensor layout tailored for confined urban settings. Both datasets were independently inverted and subsequently integrated using a non-hierarchical k-means cluster analysis within a voxel-based domain. The clustering technique, originally developed for 2D data, was extended to 3D tomographic volumes, allowing for enhanced detection of anomalous zones based on the spatial correlation of high resistivity and low seismic velocity. The results revealed three main anomalous volumes interpreted as potential cavities, with the most prominent located at shallow depth near the intersection of Via Alletto and Vicolo Cobaitari. This methodology proved effective in overcoming typical limitations of urban surveys, providing a replicable strategy for high-resolution, non-invasive subsurface diagnostics. The findings contribute to urban geohazard assessment and offer valuable support for planning and risk mitigation in areas with known or suspected subsurface instability.