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Perfomance of a new multistation alarm system for volcanic activity based on neural network techniques
Author(s)
Type
Extended abstract
Language
English
Obiettivo Specifico
2V. Dinamiche di unrest e scenari pre-eruttivi
Status
Published
Issued date
August 25, 2014
Conference Location
Istanbul (Turkey)
Abstract
Numerous eruptive episodes with Strombolian activity, lava fountains, and lava flows occurred at Mt.
Etna volcano between 2006 and 2013. In particular, 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, while
months-long lava emissions affected the upper eastern flank of the volcano in 2006 and 2008-2009.
The monitoring of such volcanic phenomena is particularly relevant for their potential socio-economic
impact in this densely populated volcanic region. 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.
Early information about changes in the state of the volcano and/or at the onset of potentially
dangerous eruptive phenomena requires efficacious surveillance methods. Several studies on seismic
data recorded at Mt. Etna highlight that the analysis of the continuous background seismic signal, the
so-called volcanic tremor, is of paramount importance to follow the evolution of volcanic activity
(e.g., Alparone et al., 2003; Falsaperla et al., 2005; Langer et al., 2009). Indeed, 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. This signal is recorded at Etna by means of the
INGV seismic network equipped with broadband sensors. The huge amount of digital data
continuously acquired by INGV’s stations every day makes a manual analysis difficult. To overcome
this problem, techniques of automatic classification of the tremor signal were applied to explore the
robustness of different methods for the identification of regimes in volcanic activity (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. In the
wake of this evidence, Messina and Langer (2011) developed KKAnalysis, a software that combines
an unsupervised classification method (Kohonen Maps) with fuzzy cluster analysis. This tool was set
up at the operative centre of the INGV-Osservatorio Etneo in 2010, and it 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.
Here we apply KKAnalysis using 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 with KKAnalysis 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.
Etna volcano between 2006 and 2013. In particular, 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, while
months-long lava emissions affected the upper eastern flank of the volcano in 2006 and 2008-2009.
The monitoring of such volcanic phenomena is particularly relevant for their potential socio-economic
impact in this densely populated volcanic region. 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.
Early information about changes in the state of the volcano and/or at the onset of potentially
dangerous eruptive phenomena requires efficacious surveillance methods. Several studies on seismic
data recorded at Mt. Etna highlight that the analysis of the continuous background seismic signal, the
so-called volcanic tremor, is of paramount importance to follow the evolution of volcanic activity
(e.g., Alparone et al., 2003; Falsaperla et al., 2005; Langer et al., 2009). Indeed, 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. This signal is recorded at Etna by means of the
INGV seismic network equipped with broadband sensors. The huge amount of digital data
continuously acquired by INGV’s stations every day makes a manual analysis difficult. To overcome
this problem, techniques of automatic classification of the tremor signal were applied to explore the
robustness of different methods for the identification of regimes in volcanic activity (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. In the
wake of this evidence, Messina and Langer (2011) developed KKAnalysis, a software that combines
an unsupervised classification method (Kohonen Maps) with fuzzy cluster analysis. This tool was set
up at the operative centre of the INGV-Osservatorio Etneo in 2010, and it 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.
Here we apply KKAnalysis using 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 with KKAnalysis 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.
References
Alparone S, Andronico D, Lodato L, Sgroi T (2003) “Relationship between tremor and volcanic activity during
the Southeast Crater eruption on Mount Etna in early 2000”, J. Geophys. Res., 108,
doi:10.1029/2002JB001866
Behncke B., (2011) “Fontana 20110112-13”, rapporto web.,
http://www.ct.ingv.it/it/component/content/article.html?id=309, 13/01/2011.
Falsaperla S, Alparone S, D'Amico S, Di Grazia G, Ferrari F, Langer H, Sgroi T, Spampinato S (2005)
“Volcanic tremor at Mt. Etna, Italy, preceding and accompanying the eruption of July–August, 2001”,
Pure Appl. Geophys. 162 (11): 2111–2132
INGV (2011a) “Boll. settimanale sul monitoraggio vulcanico, geochimico e sismico del vulcano Etna”,
04/04/2011 - 10/04/2011. Rep. N° 15/2011, 8 pp.
INGV (2011b) “Boll. settimanale sul monitoraggio vulcanico, geochimico e sismico del vulcano Etna”,
04/07/2011 - 10/07/2011. Rep. N° 28/2011, 7 pp.
Langer H, Falsaperla S, Masotti M, Campanini R, Spampinato S, Messina A (2009) “Synopsis of supervised and
unsupervised pattern classification techniques applied to volcanic tremor data at Mt. Etna, Italy”,
Geophys. J. Int., 178: 1132–1144, doi:10.1111/j.1365-246X.2009.04179.x
Langer H, Falsaperla S, Messina A, Spampinato S, Behncke B (2011) “Detecting imminent eruptive activity at
Mt Etna, Italy, in 2007-2008 through pattern classification of volcanic tremor data”, J. Volcanol.
Geotherm. Res., doi: 10.1016/j.jvolgeores.2010.11.019
Messina A and Langer H (2011) “Pattern Recognition of Volcanic Tremor Data on Mt Etna (Italy) with
KKAnalysis—a software for Unsupervised Classification”, Computers & Geosciences, doi:
10.1016/j.cageo.2011.03.015
the Southeast Crater eruption on Mount Etna in early 2000”, J. Geophys. Res., 108,
doi:10.1029/2002JB001866
Behncke B., (2011) “Fontana 20110112-13”, rapporto web.,
http://www.ct.ingv.it/it/component/content/article.html?id=309, 13/01/2011.
Falsaperla S, Alparone S, D'Amico S, Di Grazia G, Ferrari F, Langer H, Sgroi T, Spampinato S (2005)
“Volcanic tremor at Mt. Etna, Italy, preceding and accompanying the eruption of July–August, 2001”,
Pure Appl. Geophys. 162 (11): 2111–2132
INGV (2011a) “Boll. settimanale sul monitoraggio vulcanico, geochimico e sismico del vulcano Etna”,
04/04/2011 - 10/04/2011. Rep. N° 15/2011, 8 pp.
INGV (2011b) “Boll. settimanale sul monitoraggio vulcanico, geochimico e sismico del vulcano Etna”,
04/07/2011 - 10/07/2011. Rep. N° 28/2011, 7 pp.
Langer H, Falsaperla S, Masotti M, Campanini R, Spampinato S, Messina A (2009) “Synopsis of supervised and
unsupervised pattern classification techniques applied to volcanic tremor data at Mt. Etna, Italy”,
Geophys. J. Int., 178: 1132–1144, doi:10.1111/j.1365-246X.2009.04179.x
Langer H, Falsaperla S, Messina A, Spampinato S, Behncke B (2011) “Detecting imminent eruptive activity at
Mt Etna, Italy, in 2007-2008 through pattern classification of volcanic tremor data”, J. Volcanol.
Geotherm. Res., doi: 10.1016/j.jvolgeores.2010.11.019
Messina A and Langer H (2011) “Pattern Recognition of Volcanic Tremor Data on Mt Etna (Italy) with
KKAnalysis—a software for Unsupervised Classification”, Computers & Geosciences, doi:
10.1016/j.cageo.2011.03.015
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