The main objective of this work is to show that Shannon Entropy (SE) calculated on continuous seismic signals can be used in a volcanic eruption monitoring system. We analysed three years of volcanic activity of Volcán de Colima, México, recorded between January 2015 and May 2017. This period includes two large explosions, with pyroclastic and lava flows, and intense activity of less energetic explosion, culminating with a period of quiescence. In order to confirm the success of our results, we used images of the Visual Monitoring system of Colima Volcano Observatory. Another of the objectives of this work is to show how the decrease in SE values can be used to track minor explosive activity, helping Machine Learning algorithms to work more efficiently in the complex problem of distinguishing the explosion signals in the seismograms. We show that the two big eruptions selected were forecasted successfully (6 and 2 days respectively) using the decay of SE. We conclude that SE could be used as a complementary tool in seismic volcano monitoring, showing its successful behaviour prior to energetic eruptions, giving time enough to alert the population and prepare for the consequences of an imminent and well predicted moment of the eruption.
In this work we demonstrate that Shannon Entropy (SE) calculated on continuous seismic signals can be used efficiently in a volcanic monitoring system. We analysed three years of volcanic activity of Volcán de Colima, México, recorded between January 2015 and May 2017. This period includes two large explosions, with pyroclastic and lava flows, and intense activity of less energetic explosion, culminating with a period of quiescence. In order to confirm the success of our results, we used images of the Visual Monitoring system of Colima Volcano Observatory. Another of the objectives of this work is to show how the decrease in the SE values can be used to track minor explosive activity, helping Machine Learning algorithms to work more efficiently in the complex problem of distinguishing the explosion signals in the seismograms. We demonstrated the two big eruptions selected were forecasted successfully (6 and 2 days respectively) using the decay of the SE. We conclude that the SE could be used as a complementary tool in seismic volcano monitoring, showing its successful behaviour prior to energetic eruptions, giving time enough to alert the population and prepare for the consequences of an imminent and well predicted moment of the eruption.
The search for pre-eruptive observables that can be used for short-term volcanic early warning remains a scientific challenge. Pre-eruptive patterns in seismic data are usually identified by analyzing seismic catalogues (e.g., the number and types of recorded seismic events), the evolution of seismic energy, or changes in the tensional state of the volcanic medium as a consequence of changes in the volume of the volcano. However, although successful volcanic predictions have been achieved, there is still no generally valid model suitable for a large range of eruptive scenarios. In this study, we evaluate the potential successful use of Shannon entropy as short-term volcanic eruption forecasting extracted from seismic signals at five well studied volcanoes (Etna, Mount St. Helens, Kilauea, Augustine, and Bezymianny). We identified temporal patterns that can be used as short-term eruptive precursors. We quantified how the Shannon entropy drops several hours before the eruptions analyzed, between 4 days and 12 h before. When Shannon entropy is combined with the temporal evolution of other features (i.e., energy, kurtosis, and the frequency index) and complementary information on types of seismic sources, the meaning of physical changes in the volcanic system could be obtained. Our results show that pre-eruptive variation in Shannon entropy offers is a confident short-term volcanic eruption forecasting tool.
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