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Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/8293

Authors: D'Agostino, M.*
Di Grazia, G.*
Ferrari, F.*
Langer, H.*
Messina, A.*
Reitano, D.*
Spampinato, S.*
Editors: Zobin, V.
Title: Volcano monitoring and early warning on Mt Etna based on volcanic tremor – Methods and technical aspects
Publisher: NOVA Science Publishers, Inc.
Issue Date: 2013
URL: https://www.novapublishers.com/catalog/product_info.php?products_id=42535
ISBN: 978-1-62417-985-3
Keywords: Volcanic tremor
Volcano monitoring
Pattern recognition
Self Organizing Maps
Fuzzy clustering
Mt. Etna
Data storage
Abstract: Eighteen paroxysmal episodes occurred on Mt Etna in 2011, and provided rich material for testing automatic procedures of data processing and alert systems in the context of volcano monitoring. The 2011 episodes represent a typical picture of activity of Mt Etna: in 2000 and 2001, before the 2001 flank eruption, more than one hundred lava fountains were encountered. Other major lava fountains occurred before the flank eruptions of 2002/03 and 2008. All these fountains, which are powerful but usually short lived phenomena, originated from the South-East Crater area and caused the formation of thick ash clouds, followed by the fallout of material with severe problems for the infrastructure of the metropolitan area of Catania. We focus on the seismic background radiation – volcanic tremor – which plays a key role in the surveillance of Mt Etna. Since 2006 a multi-station alert system has been established in the INGV operative centre of Catania exploiting STA/LTA ratios. Besides, it has been demonstrated that also the spectral characteristics of the signal changes correspondingly to the type of volcanic activity. The simultaneous application of Self Organizing Maps and Fuzzy Clustering offers an efficient way to visualize signal characteristics and its development with time, allowing to identify early stages of eruptive events and automatically flag a critical status before this becomes evident in conventional monitoring techniques. Changes of tremor characteristics are related to the position of the source of the signal. The location of the sources exploits the distribution of the amplitudes across the seismic network. The locations were extremely useful for warning throughout both a flank eruption in 2008 as well as the 2011 lava fountains, during which a clear migration of tremor sources towards the eruptive centres could be noticed in advance. The location of the sources completes the picture of an imminent volcanic unrest and corroborates early warnings flagged by the changes of signal characteristics. On-line data processing requires computational efficiency, robustness of the methods and reliability of data acquisition. The amplitude based multi-station approach offers a reasonable stability as it is not sensitive to the failure of single stations. The single station approach, based on our unsupervised classification techniques, is cost-effective with respect to logistic efforts, as only one or few key stations are necessary. Both systems have proven to be robust with respect to disturbances (undesired transients like earthquakes, noise, short gaps in the continuous data flow), and false alarms were not encountered so far. Another critical aspect is the reliability of data storage and access. A hardware cluster architecture has been proposed for failover protection, including a Storage Area Network system. We outline concepts of the software architectures which allow easy data access following predefined user policies. We envisage the integration of seismic data and those originating from other scientific fields (such as volcano imagery, geochemistry, deformation, gravity, magneto-telluric), in order to facilitate cross-checking of the findings encountered from the single data streams, in particular allowing their immediate verification with respect to ground truth.
Appears in Collections:Book chapters
04.06.08. Volcano seismology

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