Characterization of Volcanic Cloud Components Using Machine Learning Techniques and SEVIRI Infrared Images.

Volcanic explosive eruptions inject several different types of particles and gasses into the atmosphere, giving rise to the formation and propagation of volcanic clouds. These can pose a serious threat to the health of people living near an active volcano and cause damage to air traffic. Many efforts have been devoted to monitor and characterize volcanic clouds. Satellite infrared (IR) sensors have been shown to be well suitable for volcanic cloud monitoring tasks. Here, a machine learning (ML) approach was developed in Google Earth Engine (GEE) to detect a volcanic cloud and to classify its main components using satellite infrared images. We implemented a supervised support vector machine (SVM) algorithm to segment a combination of thermal infrared (TIR) bands acquired by the geostationary MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager). This ML algorithm was applied to some of the paroxysmal explosive events that occurred at Mt. Etna between 2020 and 2022. We found that the ML approach using a combination of TIR bands from the geostationary satellite is very efficient, achieving an accuracy of 0.86, being able to properly detect, track and map automatically volcanic ash clouds in near real-time.

Seismicity pattern changes before the M = 4.8 Aeolian Archipelago (Italy) earthquake of August 16, 2010.

We investigated the seismicity patterns associated with an M = 4.8 earthquake recorded in the Aeolian Archipelago on 16, August, 2010, by means of the region-time-length (RTL) algorithm. This earthquake triggered landslides at Lipari; a rock fall on the flanks of the Vulcano, Lipari, and Salina islands, and some damages to the village of Lipari. The RTL algorithm is widely used for investigating precursory seismicity changes before large and moderate earthquakes. We examined both the spatial and temporal characteristics of seismicity changes in the Aeolian Archipelago region before the M = 4.8 earthquake. The results obtained reveal 6-7 months of seismic quiescence which started about 15 months before the earthquake. The spatial distribution shows an extensive area characterized by seismic quiescence that suggests a relationship between quiescence and the Aeolian Archipelago regional tectonics.