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|Authors: ||Langer, H.*|
|Title: ||Multistation alarm system for eruptive activity based on the automatic classiﬁcation of volcanic tremor: speciﬁcations and performance|
|Issue Date: ||12-Apr-2015|
|Keywords: ||Etna, Volcanic tremor|
Volcano monitoring, Pattern recognition
Self Organizing Map, Fuzzy clustering
|Abstract: ||With over ﬁfty eruptive episodes (Strombolian activity, lava fountains, and lava ﬂows) between 2006 and 2013,
Mt Etna, Italy, underscored its role as the most active volcano in Europe. Seven paroxysmal lava fountains at
the South East Crater occurred in 2007-2008 and 46 at the New South East Crater between 2011 and 2013.
Month-lasting lava emissions affected the upper eastern ﬂank of the volcano in 2006 and 2008-2009. On this
background, effective monitoring and forecast of volcanic phenomena are a ﬁrst order issue for their potential
socio-economic impact in a densely populated region like the town of 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.
For timely information on changes in the state of the volcano and possible onset of dangerous eruptive phenomena,
the analysis of the continuous background seismic signal, the so-called volcanic tremor, turned out of paramount
importance. 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 content) of tremor. The huge amount of digital
data continuously acquired by INGV’s broadband seismic stations every day makes a manual analysis difﬁcult, and
techniques of automatic classiﬁcation of the tremor signal are therefore applied. The application of unsupervised
classiﬁcation techniques to the tremor data revealed signiﬁcant changes well before the onset of the eruptive
episodes. This evidence led to the development of speciﬁc software packages related to real-time processing of
the tremor data. The operational characteristics of these tools – fail-safe, robustness with respect to noise and data
outages, as well as computational efﬁciency – allowed 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 currently operating software named KKAnalysis is applied to the data stream continuously recorded at two
seismic stations. The data are merged with reference datasets of past eruptive episodes. In doing so, the results of
pattern classiﬁcation can be immediately compared to previous eruptive scenarios.
Given the rich material collected in recent years, here we propose the application of the alert system to a wider
range (up to a total of 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 ofﬂine. Then, we analyzed the performance of the classiﬁer in terms of timing and spatial distribution of
|Appears in Collections:||05.01.01. Data processing|
04.06.08. Volcano seismology
04.06.06. Surveys, measurements, and monitoring
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