Publication Date:
2017-04-04
Description:
Over fifty eruptive episodes with Strombolian activity, lava fountains, and lava flows occurred at Mt
Etna volcano between 2006 and 2013. Namely, there were seven paroxysmal lava fountains at the South-East
Crater in 2007-2008 and 46 at the New South-East Crater between 2011 and 2013. Lava emissions lasting
months affected the upper eastern flank of the volcano in 2006 and 2008-2009. Effective monitoring and
forecast of such volcanic phenomena are particularly relevant for their potential socio-economic impact in
densely populated regions like Catania and its surroundings. For example, explosive activity has often
formed thick ash clouds with widespread tephra fall able to disrupt the air traffic, as well as to cause severe
problems at infrastructures, such as highways and roads.
Timely information about changes in the state of the volcano and possible onset of dangerous eruptive
phenomena requires efficacious surveillance methods. The analysis of the continuous background seismic
signal, the so-called volcanic tremor, turned out of paramount importance to follow the evolution of volcanic
activity [e.g., Alparone et al., 2003; Falsaperla et al., 2005]. Changes in the state of the volcano as well as in
its eruptive style are usually concurrent with variations of the spectral characteristics (amplitude and
frequency) of tremor. The huge amount of digital data continuously acquired by INGV’s broadband seismic
stations every day makes a manual analysis difficult. In order to tackle this problem, techniques of automatic
classification of the tremor signal are applied. In a comparative study, the robustness of different methods for
the identification of regimes in volcanic activity were examined [Langer et al., 2009]. In particular, Langer et
al. [2011] applied unsupervised classification techniques to the tremor data recorded at one station during
seven paroxysmal episodes in 2007-2008. Their results revealed significant changes in the pattern
classification well before the onset of the eruptive episodes. This evidence led to the development of specific
software packages, such as the program KKAnalysis [Messina and Langer, 2011], a software that combines
an unsupervised classification method (Kohonen Maps) with fuzzy cluster analysis. The operational
characteristics of these tools - fail-safe, robustness with respect to noise and data outages, as well as
computational efficiency - allowed on-line processing at the operative centre of the INGV-Osservatorio
Etneo in 2010 and the identification of criteria for automatic alarm flagging. The system is hitherto one of
the main automatic alerting tools to identify impending eruptive events at Etna.
The software carries out the on-line processing of the new data stream coming from two seismic
stations, merged with reference datasets of past eruptive episodes. In doing so, results obtained for new data
are immediately compared to previous eruptive scenarios. Given the rich material collected in recent years,
we are able to apply the alert system to eleven stations at different elevations (1200-3050 m) and distances
(1-8 km) from the summit craters. Critical alert parameters were empirically defined to obtain an optimal
tuning of the alert system for each station. To verify the robustness of this new, multistation alert system, a
dataset encompassing about eight years of continuous seismic records (since 2006) was processed
automatically using KKAnalysis and collateral software off-line. Then, we analyzed the performance of the
classifier in terms of timing and spatial distribution of the stations. We also investigated the performance of the new alert system based on KKAnalysis
in case of activation of whatever eruptive centre. Intriguing results were obtained in 2010 throughout periods
characterized by the renewal of volcanic activity at Bocca Nuova-Voragine and North-East Crater, and in the
absence of paroxysmal phenomena at South-East Crater and New South-East Crater. Despite the low-energy
phenomena reported by volcanologists (i.e., degassing, low-to moderate explosions), the triggered alarms
demonstrate the robustness of the classifier and its potential: i) to identify even subtle changes within the
volcanic system using tremor, and ii) to highlight the activation of a single eruptive centre, even though
different from the one for which the classifier was initially tested. It is worth noting that in case of activation
of weak sources, the successful performance of the classifier depends upon the general level of signals
originating from other sources in that specific time span.
Description:
Published
Description:
Nicolosi (Catania, Italy)
Description:
2V. Dinamiche di unrest e scenari pre-eruttivi
Description:
open
Keywords:
Etna, Volcanic tremor
;
Volcano monitoring, Pattern recognition
;
Self Organizing Map, Fuzzy clustering
;
04. Solid Earth::04.06. Seismology::04.06.06. Surveys, measurements, and monitoring
;
04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology
;
05. General::05.01. Computational geophysics::05.01.01. Data processing
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
Oral presentation
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