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  • 1
    Publication Date: 2017-04-04
    Description: States of volcanic activity at Mt Etna develop in well-defined regimes with variable duration from a few hours to several months. Changes in the regimes are usually concurrent with variations of the characteristics of volcanic tremor, which is continuously recorded as background seismic radiation. This strict relationship is useful for monitoring volcanic activity in any moment and in whatever condition.We investigated the development of tremor features and its relation to regimes of volcanic activity applying pattern classification techniques. We present results from supervised and unsupervised classification methods applied to 425 patterns of volcanic tremor recorded between 2001 July and August, when a volcano unrest occurred. Support Vector Machine (SVM) and multilayer perceptron (MLP) were used as pattern classifiers with supervised learning. For the SVM and MLP training, we considered four target classes, that is, pre-eruptive, lava fountains, eruptive and post-eruptive. Using a leave one out testing scheme, SVM reached a score of 94.8 per cent of patterns matching the actual class membership, whereas MLP achieved 81.9 per cent of matching patterns. The excellent results, in particular those obtained with SVM, confirmed the reproducibility of the a priori classification. Unsupervised classification was carried out using cluster analysis (CA) and self-organizing maps (SOM). The clusters identified in unsupervised classification formed well-defined regimes, which can be easily related to the four a priori classes aforementioned. Besides, CA found a further cluster concurrent with the climax of eruptive activity. Applying a proper colour-coding to the microclusters (the so-called best matching units) identified by SOM, it was visually possible to follow the development of the characteristics of the tremor data with time, highlighting transitional stages from a regime of volcanic activity to another one. We conclude that supervised and unsupervised classification methods can be conveniently implemented as complementary tools for an in-depth understanding of the relationships between tremor data and volcanic phenomena.
    Description: Published
    Description: 1132 - 1144
    Description: 1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
    Description: 1.5. TTC - Sorveglianza dell'attività eruttiva dei vulcani
    Description: JCR Journal
    Description: reserved
    Keywords: neural networks ; fuzzy logic ; persistance ; memory ; correlations ; clustering ; Volcano seismology ; Statistical seismology ; Volcano monitoring ; 04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology ; 04. Solid Earth::04.06. Seismology::04.06.09. Waves and wave analysis ; 04. Solid Earth::04.06. Seismology::04.06.10. Instruments and techniques ; 04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring ; 05. General::05.01. Computational geophysics::05.01.01. Data processing ; 05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks ; 05. General::05.01. Computational geophysics::05.01.04. Statistical analysis ; 05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
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    Unknown
    American Geophysical Union
    In:  “Accepted for publication in (Journal of Geophysical Research). Copyright (2009) American Geophysical Union. Further reproduction or electronic distribution is not permitted.”
    Publication Date: 2017-04-04
    Description: The eruptive episode of Mount Etna’s Southeast Crater (SEC) on 16 November 2006, which culminated with phreatomagmatic explosions and a peculiar volcaniclastic flowage event, is the subject of different interpretations. Behncke (2009) and Behncke et al. (2008, 2009), interpret the explosions as resulting from mixing of flowing lava with fluid-saturated, hydrothermally altered rock, and describe the resulting flow as a low-temperature (but potentially deadly) pyroclastic density current (PDC). Norini et al. (2009) speak of gravity-induced flank collapse affecting the SEC cone, leading to the emplacement of a landslide (or debris avalanche) deposit. Finally, Ferlito et al., commenting our recent work (Behncke et al., 2009), re-propose their earlier (2007) scenario of a shallow intrusion from the SEC conduit, caused by unloading and decompression when a part of the SEC cone flank was removed (“sector collapse”), leading to the explosive opening of an eruptive fissure, which discharged a pyroclastic flow. An outstanding feature of this event is that it was not accompanied by any significant change in the seismic signal, which led us (Behncke et al. 2009) to exclude the opening of an eruptive fissure. However, Ferlito et al. point out that seismic evidence alone does not rule out their scenario, and cite the lack of seismic signals accompanying the start of the (rather voluminous, in terms of lava discharge, but purely effusive) 2004-2005 Etna eruption as support for their hypothesis. Finally, they describe what they interpret as the source fissure for the phreatomagmatic explosions and PDCs, and was the site of minor lava extrusion toward the end of the 16 November 2006 event. On their website, Ferlito et al. host a short (〈2 min) clip excerpted from a 40:54 min long video recorded by G. Tomarchio, cameraman of the Italian public television RAI, featuring only the 1425 GMT explosion and PDC. The integral, original version of that video (which was made available to INGV-CT immediately after the event) documents, amongst others, the presence of Behncke and INGV colleagues on-site, and shows a number of extremely similar explosions and PDCs over several hours prior to 1425 GMT, only on a smaller scale. As for the 1425 GMT event, the video spectacularly shows explosive activity, but nothing proving the opening of an eruptive fissure, neither does it show any landsliding as surmised by Norini et al. (2009). Our careful viewing of 1500 still photographs taken of the activity on that day, including nearly 1000 taken by INGV staff, plus other videos taken from different viewpoints (e.g., Movie S3 in the auxiliary material to our article) leads us to analogous conclusions. Videos and photographs document dozens of minor explosive, PDC-generating events before the major phreatomagmatic explosions and PDCs at 1425 GMT. The mechanisms of these events were virtually the same throughout, differing only in their magnitude. All were caused by hot, flowing lava mixing with wet, hydrothermally altered rocks making up the SEC cone’s flank that the lava was burrowing through. The “eruptive fracture” that Ferlito et al. refer to is a secondary feature, formed at the toe of a lava flow, which had flowed down the ESE side of the cone early on 16 November 2006 and was severed around noon by the progressive enlargement of the large scar eroded into the cone’s flank. Draining of the lava within the active channel of the severed flow led to accumulation of lava at the cone’s base, developing into a sort of bubble. For reasons unknown, this bubble drained during the late afternoon, yielding an extremely small flow. The pocket evacuated by this outflow subsided to become what Ferlito et al. interpret as an eruptive fissure, a single slightly elongate collapse depression, lying approximately 150 m northeast of the locus of the 1425 GMT phreatomagmatic explosions, which is well visible in aerial photographs taken after the events under discussion (Figure 1). The lava flow that Ferlito et al. claim to have sampled is the secondary flow formed by the draining of the pocket. It has no whatsoever genetic relationship with the phreatomagmatic explosions and PDCs of 1425 GMT. Another fundamental argument lies in the seismic record, and it is here that Ferlito et al. miss two major points. Firstly, unlike the seismic scenario usually observed at Etna in more than three decades of monitoring (e.g., Patanè et al., 2004), the start of the 2004-2005 lava effusion was exceptionally silent as many authors noted (e.g., Burton et al., 2005; Di Grazia et al., 2006; Corsaro et al., 2009). The onset of lava emission was indeed completely and unusually aseismic (in terms of volcano-tectonic seismicity, volcanic tremor changes, etc.), but it was also totally non-explosive, due to the nearly complete depletion in gas of the magma. Therefore, this effusive episode stands in marked contrast with the 16 November 2006 activity. It should be noted that when new, gas-rich magma moved toward the surface at a later stage of the 2004-2005 lava effusion, the volcanic tremor amplitude markedly increased (Di Grazia et al., 2006). Secondly, Ferlito et al. refer to papers (e.g., Cardaci et al., 1993; Patanè et al., 2004) which deal with the relationship between volcano-tectonic (VT) seismicity and the triggering of eruptive activity at Etna. VT seismicity covers just a part of the information contained in a seismic record (e.g., McNutt, 2000), a detail which can be easily missed by non-experts in seismology. There is indeed a variety of signals (e.g., long-period events, hybrid events, volcanic tremor, explosion quakes) related to the movement of fluids and/or magma, which can herald and accompany the opening of eruptive fractures. We did extensive cross-checking of the seismic record of the entire 2006 eruptive sequence, paying particular attention to episodes of new eruptive fissures opening. Each single event marked by the opening of new vents displaying some sort of explosive activity (this occurred during at least four of the paroxysms during the August-December 2006 eruptive sequence) shows conspicuous changes not only in the amplitude of the seismic (tremor) signal, but also in the location of the centroid of the tremor source, and frequency content, features amply discussed in our paper (Behncke et al., 2009). The migration of subsurface magma can thus be well documented, if it is accompanied by degassing. We would also like to point out that the phreatomagmatic explosions and PDCs of 1425 GMT occurred shortly after a conspicuous drop in the volcanic tremor amplitude (see Fig. 8 in Behncke et al., 2009). The lack of changes in the seismic signals concurrent with the PDC is also evident in the spectrograms (in which the frequency content excludes the occurrence of any seismic signals associated with fracturing, see Fig. 9 in Behncke et al., 2009) and in the records of all the broadband stations considered by Behncke et al. (2009), notwithstanding their vicinity to the site of the PDC-generating explosions (EBEL and ECPN are located ~1 km from the SEC, at 2899 and 3050 m elevation above sea level, respectively). Finally, the hypothesis of magma uprise at the base of the SEC cone caused by unloading related to the removal of a major portion of the cone’s flank, has been vested by Ferlito et al. (2007) in a volcanic sector collapse scenario similar to the catastrophic 1980 debris avalanche at Mount St. Helens. Volcanic sector collapse commonly takes place instantaneously, which is the contrary of what happened at the SEC on 16 November 2006. Thanks to our presence on site from the early morning onward, we were able to document how the removal of a portion of the flank of the cone occurred extremely slowly, over at least 5 hours (cf. Fig. 5 in Behncke et al., 2008). The material involved in this displacement moved at best at 50-80 m per hour, which is rather unlike the speed of volcanic debris avalanches. There was no such thing as a major landslide, and no such thing as a new eruptive fissure opening; what did happen was a very hazardous sequence of events, including phreatomagmatic explosions and quite low-temperature but fast-moving, dense pyroclastic density currents. Such volcanic phenomena deserve in-depth multidisciplinary studies, and the ongoing discussion underscores how much work is still necessary to better understand the dynamics of a versatile volcano such as Mount Etna.
    Description: Published
    Description: B12205
    Description: 1.5. TTC - Sorveglianza dell'attività eruttiva dei vulcani
    Description: JCR Journal
    Description: open
    Keywords: Volcano monitoring ; Mt. Etna ; Volcanic hazard ; instruments and techniques ; 04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 3
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    Unknown
    American Geophysical Union
    In:  An edited version of this paper was published by AGU. Copyright (2009) American Geophysical Union
    Publication Date: 2017-04-04
    Description: Three eruptive episodes during the 2006 summit eruptions of Mount Etna were exceptionally well documented by visual, seismic and thermal monitoring. The first (16 November) was strongly explosive, with vigorous Strombolian activity and ash emission from multiple vents, lava emission, and phreatomagmatic explosions generating pyroclastic density currents (PDCs). The second episode (19 November) had a rather weakly explosive component, with mild Strombolian activity but more voluminous lava emission. The third (24 November) was a moderately explosive paroxysm, with intermittent lava fountaining and generation of a tephra column as well as lava emission and PDCs. Data recorded by a thermal monitoring camera clearly document the different phases of each paroxysm, weather clouds occasionally hampering thermal monitoring. The images show a rapid onset of the volcanic activity, which during each of the paroxysms reached a peak in eruptive and thermal intensity, and then decreased gradually. The stronger phreatomagmatic explosions and PDCs on 16 and 24 November did not yield any seismic signature linked to the opening of new vents, nor were they associated with peculiar characteristics of the seismic signal. Nevertheless, eruptive styles (Strombolian activity, lava emission) and different levels in the intensity of explosive activity were generally well reflected in the amplitude and frequency content of the seismic signal, and in the source location of the volcanic tremor centroid throughout the three eruptive episodes. This multidisciplinary study, therefore, not only provides a key to distinguish between endogenous and exogenous origins of the phenomena observed, but also documents the complex magma dynamics within the volcano.
    Description: Published
    Description: B03211
    Description: 1.5. TTC - Sorveglianza dell'attività eruttiva dei vulcani
    Description: JCR Journal
    Description: reserved
    Keywords: Volcano monitoring ; Mt Etna ; volcanic hazard ; instruments and techniques ; 04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 4
    Publication Date: 2017-04-04
    Description: ABSTRACT FINAL ID: V53E-2673 TITLE: Complementary Methods for Volcanic Seismic Source Discrimination SESSION TYPE: Poster SESSION TITLE: V53E. Surveillance of Volcanic Unrest: New Developments in Multidisciplinary Monitoring Methods IV Posters AUTHORS (FIRST NAME, LAST NAME): Charlotte A Rowe1, Susanna M R Falsaperla2, Emily Morton3, Horst K Langer2, Boris Behncke2 INSTITUTIONS (ALL): 1. Los Alamos Natl Lab, Los Alamos, NM, United States. 2. Istituto Nazionale di Geofisica e Volcanologia, Catania, Italy. 3. Earth and Environmental Sciences, New Mexico Institute of Mining and Technology, Socorro, NM, United States. Title of Team: ABSTRACT BODY: We explore the success rates of detection and classification algorithms as applied to seismic signals from active volcanoes. The subspace detection method has shown some success in identifying repeating (but not identical) signals from seismic swarm sources, as well as pulling out nonvolcanic long period events within subduction zone tremor. We continue the exploration of this technique as applied to both discrete events and variations within volcanic tremor to determine optimal situations for its use. We will demonstrate both three-dimensional and subband applications both on raw waveforms and derived features such as skewness and kurtosis. The application can be used in both a supervised (select templates and compare) as well as unsupervised (cross-compare all samples and apply clustering to the matrix of comparisons). We compare the method to that of the KKAnalysis tool, which uses a self-organizing map approach to unsupervised clustering for feature vectors derived from the seismic waveforms. We will present a comparison of this method as applied to waveform features, spectral features and time-varying higher-order statistics as well as signal polarization, to elucidate the tools which show the best promise for problematic discrimination tasks.
    Description: Published
    Description: San Francisco, California, USA
    Description: 1.5. TTC - Sorveglianza dell'attività eruttiva dei vulcani
    Description: open
    Keywords: Volcano monitoring ; seismology ; computational seismology ; 05. General::05.01. Computational geophysics::05.01.99. General or miscellaneous
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Poster session
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  • 5
    Publication Date: 2017-04-04
    Description: Published
    Description: 1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
    Description: 5.8. TTC - Biblioteche ed editoria
    Description: open
    Keywords: Volcano seismology ; Volcano monitoring ; 04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology ; 04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring ; 05. General::05.01. Computational geophysics::05.01.01. Data processing
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: book
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  • 6
    Publication Date: 2017-04-04
    Description: During the spring of 2007, paroxysmal activity occurred at the South-East Crater of Mt Etna, always associated with sharp rises in the amplitude of the volcanic tremor. Activity ranged from strong Strombolian explosions to lava fountains coupled with copious emission of lava flows and tephra. During inter-eruptive periods, recurrent seismic unrest episodes were observed in form of temporary enhancements of the volcanic tremor amplitude, but they did not culminate in eruptive activity. Here, we present the results of an analysis of these inter-eruptive periods by integrating seismic volcanic tremor, in-soil radon, plume SO2 flux and thermal data. SO2 flux and thermal radiation are envisaged as the “smoking gun”, certifying that changes in seismic or radon data can be considered as volcanogenic. Short-term changes were investigated by pattern classification based on Kohonen Maps and fuzzy clustering on volcanic tremor, radon and ambient parameters (pressure and temperature). Our results unveil ‘failed’ eruptions between February and April 2007 that are explained as ascending magma batches, which triggered repeated episodes of gas pulses and rock fracturing, but that failed to reach the surface.
    Description: Published
    Description: 297–313
    Description: 2V. Dinamiche di unrest e scenari pre-eruttivi
    Description: JCR Journal
    Description: restricted
    Keywords: Volcano monitoring ; Failed eruption ; Mt. Etna ; Volcanic tremor ; Plume SO2 flux ; In-soil radon ; Thermal data ; 05. General::05.01. Computational geophysics::05.01.01. Data processing
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 7
    Publication Date: 2017-04-04
    Description: 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.
    Description: Published
    Description: Istanbul (Turkey)
    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
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Extended abstract
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  • 8
    Publication Date: 2017-04-04
    Description: From 11 January to 15 November 2011, 18 paroxysmal eruptions occurred at Etna, Italy. These events belong to a long sequence of eruptive episodes, which marked the prevalent explosive style of the volcano since the early 2000s. Applying “KKAnalysis”, a software for pattern classification that combines Self­Organizing Maps and fuzzy clustering, to the background seismic radiation (so-called volcanic tremor), we were able to detect critical changes in the spectral characteristics (amplitude and frequency content) at a very early stage of the volcano unrest. The online implementation for surveillance purposes of KKAnalysis provided automatic alert of the impending eruptive events from hours to a few days in advance. In its original version, the classifier analyzed the data stream continuously recorded at a single seismic station. By using offline a modified version of KKAnalysis, here we apply the software to the seismic signal recorded at 11 broadband stations in 2011. The seismic sensors were located at various distances (from 1 to 8 km) from the active craters. The continuous records and the optimal geometry of the seismic network offer us the possibility to track the spectral variations in time and space. We show the new results of pattern classification and propose a revised, more powerful multi­station alert method that now provides short­ term forecasting also in the form of animated maps that flag the detection of changes at each station. This allows us to observe how the unrest develops in various sectors of the volcano. We discuss the performance of the method and the robustness of the eruption forecasts in the context of the complex dynamics of a volcanic system such as Etna.
    Description: Published
    Description: Prague (Czech Republic)
    Description: 2V. Dinamiche di unrest e scenari pre-eruttivi
    Description: open
    Keywords: Volcano monitoring ; Short­-term forecasting ; Pattern classification  ; Volcanic tremor ; Eruptions ; Etna ; 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 ; 05. General::05.01. Computational geophysics::05.01.04. Statistical analysis
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Oral presentation
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  • 9
    Publication Date: 2017-04-04
    Description: Short-term forecasting of volcanic unrest requires high-rate/continuous data acquisition and monitoring of multidisciplinary data. Volcano Observatories worldwide usually adopt various tools for the automatic processing of geophysical and geochemical data streams to detect changes heralding impending eruptive activity. Here we discuss the application to multivariate data sets of a free software named KKAnalysis. The software is one of the data mining tools of the European MEDiterrranean Supersite Volcanoes (MED­SUV) project, and carries out the pattern classification of data of whatever nature provided in numerical format. We explain how this software works combining Self-Organizing Maps and fuzzy clustering. Beside numerical log files, changes of pattern characteristics are visualized as output of KKAnalysis in graphical form, by creating a sequence of colored symbols. This convenient color code highlights the development in time of the characteristics of whatever multidimensional feature vector. We also present results of applications to seismic data (volcanic tremor), in-soil radon activity, and ambient parameters (barometric pressure and air temperature measurements acquired at the same site of the radon data). We explore these applications at Mt. Etna, Italy, in time spans of various duration (from months to years), in which eruptive activity ranged from short-lived (usually from tens of minutes to hours) lava fountains to long-lasting (from months to years) lava effusions.
    Description: This work was supported by the MED-SUV project, which has received funding from the European Union’s Seventh Programme for research, technological development and demonstration under grant agreement No 308665.
    Description: Published
    Description: Prague (Czech Republic)
    Description: 2V. Dinamiche di unrest e scenari pre-eruttivi
    Description: open
    Keywords: Volcano monitoring ; pattern classification ; data mining methods ; volcanic tremor ; in-soil Radon activity ; Etna ; 04. Solid Earth::04.08. Volcanology::04.08.06. Volcano monitoring
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Poster session
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