InSAR pattern recognition and data analysis in characterizing high-risk seismic areas: a case study from L’Aquila city, Italy

A statistical analysis of time series of A-DInSAR post-seismic data, acquired at the historical centre of L’Aquila city (Italy), in the time range 2010-2021, from the Cosmo-SkyMed and Sentinel-1 missions, has been carried out. This has allowed analysing the relationships between ground deformations and geological, hydrogeological features of the study area, as well as the level of seismic damage to budlings. The analysis of these data is still ongoing and offers promising research perspectives in the field of subsoil characterization, based on satellite ground deformation data, also useful in seismic hazard characterization and mitigation. This study is structured into three phases: – Correlation between Permanent Scatterers (PSs) displacement and earthquake related building damage, – Correlation between seasonal ground deformation and precipitation fluctuations, – Correlation between PSs displacement and geological features. The data analysis revealed a subsidence phenomenon still ongoing, involving the whole study area and showing local heterogeneities. A pattern analysis, involving cluster analysis and inferential statistics, has highlighted that higher ground deformation rate values are associated with higher damage levels to buildings (Sciortino et al., 2024). The correlation analysis between predisposing geological factors and SAR deformation has corroborated the hypothesis that subsidence rates are substantially controlled by the properties and thicknesses of shallower rock layers. The main results are summarized below. PSs displacements (i.e., ground deformation) are correlated with earthquake related building damage intensity, Seasonal average subsidence rate fluctuations and precipitation oscillations exhibit significant statistical correlation, PSs velocities are mainly controlled by the properties and thicknesses of shallower rock layers. These evidences pave the way for a potential use of SAR data in mapping and characterizing high-risk seismic areas, at urban scale and at relatively low costs.