Abstract
Two paroxysmal explosions occurred at Stromboli on 3 July and 28 August 2019, the first of which caused the death of a young tourist. After the first paroxysm an effusive activity began from the summit vents and affected the NW flank of the island for the entire period between the two paroxysms. We carried out an unsupervised analysis of seismic and infrasonic data of Strombolian explosions over 10 months (15 November 2018–15 September 2019) using a Self-Organizing Map (SOM) neural network to recognize changes in the eruptive patterns of Stromboli that preceded the paroxysms. We used a dataset of 14,289 events. The SOM analysis identified three main clusters that showed different occurrences with time indicating a clear change in Stromboli’s eruptive style before the paroxysm of 3 July 2019. We compared the main clusters with the recordings of the fixed monitoring cameras and with the Ground-Based Interferometric Synthetic Aperture Radar measurements, and found that the clusters are associated with different types of Strombolian explosions and different deformation patterns of the summit area. Our findings provide new insights into Strombolian eruptive mechanisms and new perspectives to improve the monitoring of Stromboli and other open conduit volcanoes.
Highlights
IntroductionArtificial Neural Networks (ANNs) are applied in a wide range of fields to approach classification, pattern recognition, clustering, regression analysis, and time series prediction 4.0/)
Starting from 30 s seismic signal recordings corresponding to 1500 samples (50 samples per second), we obtained
By comparing this displacement data with Self-Organizing Map (SOM) clusters and with the camera images, we found that the period dominated by the gas explosions of cluster Blue
Summary
Artificial Neural Networks (ANNs) are applied in a wide range of fields to approach classification, pattern recognition, clustering, regression analysis, and time series prediction 4.0/). ANNs been successfully applied in the field of problems. ANNs have beenhave successfully applied in the field of seismolseismology and volcanology to solve geophysical signal automatic classification and ogy and volcanology to solve geophysical signal automatic classification and clustering clustering problems andpredictive to perform predictive analyses. Of seismology, many seismology, many studies that ANNs powerfulthe tools to improve and the studies demonstrated that demonstrated. ANNs are powerful tools are to improve performances performances the automatic systems foranalyses seismological analyses that the robustnessand of the robustness automatic of systems for seismological that allow gaining allow critical for in people’s safety nearMany real time [7–9]. Many criticalgaining information forinformation people’s safety near real time in [7–9]
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