Clustering of eruptive events from high-precision strain signals recorded during the 2020–2022 lava fountains at the Etna volcano (Italy)

Abstract

Lava fountains at the Etna volcano are spectacular eruptive events characterized by powerful jets that expel hot mixtures of solid particles and volcanic gases, easily reaching stratospheric heights. Ash dispersal and fallout of solid particles affect the inhabited areas, often causing hazards both to infrastructure and to air and vehicular traffic. We focus on the extraordinary intense and frequent eruptive activity at Etna in the period of December 2020–February 2022, when more than 60 lava fountain events occurred with various ejected magma volume and lava fountain height and duration. Differences among the events are also imprinted in tiny ground deformations caught by strain signals recorded concurrently with the lava fountain events, reflecting a strict relationship with their evolution. To characterize this variability, which denotes changes in the eruption style, we clustered the lava fountain events using the k-means algorithm applied on the strain signal. A novel procedure was developed to ensure a high-quality clustering process and obtain robust results. The analysis identified four groups of strain variations which stand out for their amplitude, duration and time derivative of the signal. The temporal distribution of the clusters highlighted a transition in different types of eruptions, thus revealing the importance of clustering the strain variations for monitoring the volcano activity and evaluating the associated hazards.