We focus on a sequence of 9 lava fountains from Etna that occurred in 2011, separated by intervals of 5 to 10 days. Continuous measurements allowed to discover the occurrence of gravity decreases before the onset of most fountaining episodes. We propose that the gravity changes are due to the pre-fountaining accumulation of a foam layer at shallow levels in the plumbing system of the volcano. Relying on the relationship between amount of gas trapped in the foam and amount of gas emitted during each episode, we develop a conceptual model of the mechanism controlling the passage from Strombolian to lava fountaining activity. Gas leakage from the foam layer during the late stages of its accumulation increases the gas volume fraction at upper levels, thus inducing a decrease of the magma-static pressure in the trapping zone and a further growth of the foam. This feedback mechanism eventually leads to the collapse of the foam layer and to the onset of lava fountaining. The possibility to detect the development of a foam layer at depth and to set quantitative constraints on the amount of trapped gas is important because of the implications for forecasting explosive eruptions and predicting their intensity.
SUMMARY
States of volcanic activity at Mt Etna develop in well‐defined regimes with variable duration from a few hours to several months. Changes in the regimes are usually concurrent with variations of the characteristics of volcanic tremor, which is continuously recorded as background seismic radiation. This strict relationship is useful for monitoring volcanic activity in any moment and in whatever condition. We investigated the development of tremor features and its relation to regimes of volcanic activity applying pattern classification techniques. We present results from supervised and unsupervised classification methods applied to 425 patterns of volcanic tremor recorded between 2001 July and August, when a volcano unrest occurred.
Support Vector Machine (SVM) and multilayer perceptron (MLP) were used as pattern classifiers with supervised learning. For the SVM and MLP training, we considered four target classes, that is, pre‐eruptive, lava fountains, eruptive and post‐eruptive. Using a leave one out testing scheme, SVM reached a score of 94.8 per cent of patterns matching the actual class membership, whereas MLP achieved 81.9 per cent of matching patterns. The excellent results, in particular those obtained with SVM, confirmed the reproducibility of the a priori classification.
Unsupervised classification was carried out using cluster analysis (CA) and self‐organizing maps (SOM). The clusters identified in unsupervised classification formed well‐defined regimes, which can be easily related to the four a priori classes aforementioned. Besides, CA found a further cluster concurrent with the climax of eruptive activity. Applying a proper colour‐coding to the microclusters (the so‐called best matching units) identified by SOM, it was visually possible to follow the development of the characteristics of the tremor data with time, highlighting transitional stages from a regime of volcanic activity to another one.
We conclude that supervised and unsupervised classification methods can be conveniently implemented as complementary tools for an in‐depth understanding of the relationships between tremor data and volcanic phenomena.
The MEV project aims at developing a muon telescope expressly designed for the muography of Etna Volcano. In particular, one of the active craters in the summit area of the volcano would be a suitable target for this experiment. A muon tracking telescope with high imaging resolution was built and tested during 2017. The telescope is a tracker based on extruded scintillating bars with WLS fibres and featuring an innovative read-out architecture. It is composed of three XY planes with a sensitive area of 1 m 2 ; the angular resolution does not exceeds 0.4 msr and the total angular aperture is about ±45 • . A special effort concerned the design of mechanics and electronics in order to meet the requirements of a detector capable to work in a hostile environment such as the top of a tall volcano, at a far distance from any facility. The test phase started in January 2017 and ended successfully at the end of July 2017. An extinct volcanic crater (the Monti Rossi, in the village of Nicolosi, about 15km from Catania) is the target of the measurement. The detector acquired data for about 120 days and the preliminary results are reported in this work.
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