The project aims to investigate how temporal variations in seismic velocity relate to the different phases of a landslide's life cycle—nucleation, failure, sliding, and stopping—and whether these variations can help predict paroxysmal reactivations. To this end, 48 three-component seismic sensors from the INGV ESITO Laboratory were installed across the Cà di Sotto earthflow, a recently reactivated landslide located in the Northern Apennines. The sensors were deployed in two zones: 35 on the toe, which showed significant movements during heavy rainfall just after the reactivation, and 13 in the middle sector, about 700 meters from the valley floor. Some sensors were also placed on stable ground for comparison. The sensor layout was concentric, with varying radii to monitor ambient vibration wavefield propagation across different wavelengths. Data acquisition lasted 14 days (May 6–20, 2025) at a sampling rate of 250 Hz. Although heavy rainfall events occurred during this period, they did not cause significant landslide movement.
The upcoming analysis will investigate whether small localized displacements occurred and if they correlate with Rayleigh wave velocity changes and HVSR frequency and amplitude variations. Additionally, the study will estimate the thickness of landslide deposits by inverting the collected seismic data to obtain shear-wave velocity profiles. These findings will support the development of a reference geological model and help assess deposit stability under seismic conditions.
This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-GenerationEU (National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).
Current and future global warming will cause the degradation of mountain permafrost, which may strongly influence the stability of permafrost slopes or rock walls with potentially hazardous consequences. Due to the strong heterogeneity of both the thermal regime and the ground composition of mountain permafrost, its response to atmospheric forcing can however be highly variable for different landforms and within short distances. The spatial distribution of ice and liquid water is important for determining the sensitivity of a specific permafrost occurrence to climate change because of their large influence on the pace of temperature changes (by effects of latent heat) and their importance for geotechnical properties of the ground. Detailed knowledge of the material properties and internal structures of frozen ground is therefore an important prerequisite to determine the sensitivity of permafrost to climate change. Except for the active layer ice and water contents and their temporal and spatial variability usually cannot be measured directly. Geophysical methods are sensitive for the ice and liquid water content in the ground. With the proposed collaboration, two similar but complementary approaches to quantify the composition of the ground based on 2D sections of geophysical data will be combined for an improved determination of ice and water contents in permafrost regions. The so-called 4-phase model (4PM) is based on two simple petrophysical relationships for electrical resistivity and seismic velocity and estimates volumetric fractions of ice, water, and air within the pore volume of a rock matrix by jointly using complementary data sets from electric and seismic measurements. Due to inherent ambiguities in the model it is still restricted to specific cases and often allows only a rough estimation of the phase fractions. Major drawbacks of the current 4PM comprise the unsatisfactory discrimination between rock and ice and its under-determinedness, requiring the prescription of the porosity and further parameters. The so-called RSANN model (developed and used by the host institution) uses the technique of simulated annealing (a Monte-Carlo-type stochastic simulation approach) as an optimization tool for the integration of electrical resistivity and P-wave velocity to derive 2D sections of porosity, water saturation and volumetric water content. The simulated annealing technique allows - due to its iterative procedure - more parameters to be predicted instead of being prescribed as in the 4PM. The objective of the proposed collaboration is to combine the advantages of the two algorithms (4PM and RSANN) to overcome the shortcomings of the 4PM in order to improve the reliability of the determined ice and liquid water contents. (...)