Integration of single-station ambient seismic noise techniques in multi-hazard assessment framework: case study of the historical landslide of San Vito Romano (Italy)

Multi-hazards approaches are recommended for landslide risk mitigation and sustainable planning in vulnerable urban areas, especially in case of occurrence of independent events, which can be triggered by multiple sources and can involve different slope-failure mechanisms. Such a complex scenarios can be monitored using ambient seismic noise techniques that can represent a feasible strategy for detecting and tracking seasonal ground instability effects (GIE), as well as a tool for calibrating numerical models devoted to reproducing multi-hazard scenarios in a predictive perspective. This preliminary work is part of a PhD research to be carried out within the RETURN Extended Partnership and it received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005). The study presents, the first step of such integration focused on monitoring GIE using single-station landslide mobility indicators (LSMI), such as relative change in velocity (dV/V), natural period (dT/T) and resonance peak-polarization (dP/P) as proxies for detection change in rigidity, amplification, and nonlinear elastic properties. The ambient seismic noise is recorded at four three-component short-period velocimeters at San Vito Romano (SVR), about 50 km east of Rome, Italy. SVR hosts an active retrogressive historical earth slide involving the turbidite deposits, in which the alternation of sandstone and shales layers may represent a predisposing hydrogeological trigger. Rainfall and earthquakes have the role of trigger factors and soil moisture and freezing-thawing cycles that of preparatory factors. The reliability of multi-parametric geophysical results is investigated by statistical comparison approaches with meteorological, earthquake, and zonal instability information inferred from satellites InSAR. The monitoring will be followed by prediction approaches for the interpretation of GIE, hence integrating the single-station LSMI along with satellite interferometry, into the multi-hazard assessment framework for the landslide at SVR.