No abstract

Predicting the time of failure is a topic of major concern in the field of geological risk management. Several approaches, based on the analysis of displacement monitoring data, have been proposed in recent years to deal with the issue. Among these, the inverse velocity method surely demonstrated its effectiveness in anticipating the time of collapse of rock slopes displaying accelerating trends of deformation rate. However, inferring suitable linear trend lines and deducing reliable failure predictions from inverse velocity plots are processes that may be hampered by the noise present in the measurements; data smoothing is therefore a very important phase of inverse velocity analyses. In this study, different filters are tested on velocity time series from four case studies of geomechanical failure in order to improve, in retrospect, the reliability of failure predictions: Specifically, three major landslides and the collapse of an historical city wall in Italy have been examined. The effects of noise on the interpretation of inverse velocity graphs are also assessed. General guidelines to conveniently perform data smoothing, in relation to the specific characteristics of the acceleration phase, are deduced. Finally, with the aim of improving the practical use of the method and supporting the definition of emergency response plans, some standard procedures to automatically setup failure alarm levels are proposed. The thresholds which separate the alarm levels would be established without needing a long period of neither reference historical data nor calibration on past failure events.

A first probabilistic scenario-based hazard assessment for tephra fallout is presented for La Fossa volcano (Vulcano Island, Italy) and subsequently used to assess the impact on the built environment. Eruption scenarios are based upon the stratigraphy produced by the last 1000 years of activity at Vulcano and include long–lasting Vulcanian and sub-Plinian eruptions. A new method is proposed to quantify the evolution through time of the hazard associated with pulsatory Vulcanian eruptions lasting from weeks to years, and the increase in hazard related to typical rainfall events around Sicily is also accounted for. The impact assessment on the roofs is performed by combining a field characterization of the buildings with the composite European vulnerability curves for typical roofing stocks. Results show that a sub-Plinian eruption of VEI 2 is not likely to affect buildings, whereas a sub-Plinian eruption of VEI 3 results in 90 % of the building stock having a ≥12 % probability of collapse. The hazard related to long-lasting Vulcanian eruptions evolves through time, and our analysis shows that the town of Il Piano, located downwind of the preferential wind patterns, is likely to reach critical tephra accumulations for roof collapse 5–9 months after the onset of the eruption. If no cleaning measures are taken, half of the building stock has a probability >20 % of suffering roof collapse

In situ and remote-sensing measurements have been used to characterize the run-up phase and the phenomena that occurred during the August–November 2014 flank eruption at Stromboli. Data comprise videos recorded by the visible and infrared camera network, ground displacement recorded by the permanent-sited Ku-band, Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) device, seismic signals (band 0.02–10 Hz), and high-resolution Digital Elevation Models (DEMs) reconstructed based on Light Detection and Ranging (LiDAR) data and tri-stereo PLEIADES-1 imagery. This work highlights the importance of considering data from in situ sensors and remote-sensing platforms in monitoring active volcanoes. Comparison of data from live-cams, tremor amplitude, localization of Very-Long-Period (VLP) source and amplitude of explosion quakes, and ground displacements recorded by GBInSAR in the crater terrace provide information about the eruptive activity, nowcasting the shift in eruptive style of explosive to effusive. At the same time, the landslide activity during the run-up and onset phases could be forecasted and tracked using the integration of data from the GBInSAR and the seismic landslide index. Finally, the use of airborne and space-borne DEMs permitted the detection of topographic changes induced by the eruptive activity, allowing for the estimation of a total volume of 3.07 ± 0.37 × 106 m3 of the 2014 lava flow field emplaced on the steep Sciara del Fuoco slope.