Abstract Stromboli volcano is considered one of the most active volcanoes in the world. During its effusive phases, it is possible to record a particular typology of events named “hybrid events”, that rarely are observed in the daily volcano activity. These ones are often associated to fault failure in the volcanic edifice due to magma movement and/or pressurization. Their identification, analysis and location can improve the volcano eruptive process comprehension. However, it is not easy to distinguish them from the other usually recorded events, i.e. explosion-quakes, through a visual seismogram analysis. Thus, we present an automatic supervised procedure, based on a Multi-layer Perceptron (MLP) neural network, to identify and discriminate them from the explosions-quakes. The data are encoded by using LPC coefficients and then adding to this coding waveform features. The 99% of accuracy was reached when waveform features are coded together with LPC coefficients as input to the network, emphasizing the importance of temporal features for discriminating hybrid events from explosion-quakes. The results allow us to assert that the proposed neural strategy can be included in a more complex automatic system for the monitoring of Stromboli volcano and of other volcanoes in the world.

A Neural Approach for Hybrid Events Discrimination at Stromboli Volcano

SCARPETTA, Silvia;
2018

Abstract

Abstract Stromboli volcano is considered one of the most active volcanoes in the world. During its effusive phases, it is possible to record a particular typology of events named “hybrid events”, that rarely are observed in the daily volcano activity. These ones are often associated to fault failure in the volcanic edifice due to magma movement and/or pressurization. Their identification, analysis and location can improve the volcano eruptive process comprehension. However, it is not easy to distinguish them from the other usually recorded events, i.e. explosion-quakes, through a visual seismogram analysis. Thus, we present an automatic supervised procedure, based on a Multi-layer Perceptron (MLP) neural network, to identify and discriminate them from the explosions-quakes. The data are encoded by using LPC coefficients and then adding to this coding waveform features. The 99% of accuracy was reached when waveform features are coded together with LPC coefficients as input to the network, emphasizing the importance of temporal features for discriminating hybrid events from explosion-quakes. The results allow us to assert that the proposed neural strategy can be included in a more complex automatic system for the monitoring of Stromboli volcano and of other volcanoes in the world.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4690972
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