Search for anomalies in Stromboli's pre-paroxysm activity through an automatic hybrid method of time series analysis

Stromboli (Italy) is an open-vent volcano with persistent explosive activity producing up to five hundred mild explosions per day. Fluctuations in explosion intensity, varying even by orders of magnitude in terms of emitted volume and their subsequent impact on the surrounding regions, sometimes occur abruptly. Consequently, identifying precursors of larger eruptive activities, particularly for more intense (paroxysmal) explosions, is challenging. In order to search for anomalies in the pre-paroxysm activity related to the summer 2019 eruption, we applied a hybrid method to the automatic analysis of geophysical and geochemical time series. This approach is based on the combination of two methods: 1. the Empirical Mode Decomposition (EMD) and 2. the Support Vector Regression (SVR). The aggregation of these two methods allowed us to identify anomalies in the patterns of the geophysical and geochemical parameters measured on Stromboli in a ten-month period including the July–August 2019 eruption. The results of this study are encouraging for an improvement of the monitoring systems and for volcano early warning applications.

This work has been supported by the INGV project Pianeta Dinamico 2023-2025 - ObseRvation, Measurement and modelling of Eruptive processes (ORME), and partially supported by the Progetto Strategico Dipartimentale INGV 2019 “Forecasting eruptive activity at Stromboli volcano: timing, eruptive style, size, intensity and duration” (FIRST, Delibera n. 144/2020; Scientific Responsibility: S.C.). Furthermore, this research has benefited from the support of Convenzione B2 DPC-INGV 2022-2024, Stromboli, Task 1.3 “Development of a unique activity index and estimation of the probability of the transition between ‘ordinary’ and ‘extraordinary’ eruptive activity”, and of the INGV project “Reti Multiparametriche”, Task A2 “Development of methods for the identification of precursors of Stromboli's paroxysms and major explosions based on multiparametric data analysis and study of possible early warning techniques”.

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