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  • 1
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    Synopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data atMt Etna, Italy (2009)
    Details
    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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    Monitoring activity regimes using pattern recognition of volcanic tremor data. Case studies from Mt. Etna (2009)
    Details
    Publication Date: 2017-04-04
    Description: The monitoring of the seismic background signal – commonly referred to as volcanic tremor - has become a key tool for volcanic surveillance, particularly when field surveys are unsafe and/or visual observations are hampered by bad weather conditions. It is by now widely accepted that changes in the state of activity of the volcano show up in the volcanic tremor signature, such as amplitude and frequency content. Hence, the analysis of the characteristics of volcanic tremor leads us to pass from a mere monoparametric vision of the data to a multivariate one, which can be tackled with modern concepts of multivariate statistics and pattern recognition. For this purpose we apply a recently developed software package, which combines various concepts of unsupervised classification, in particular cluster analysis and Kohonen maps. Unsupervised classification is based on a suitable definition of similarity between patterns rather than on a-priori knowledge of their class membership. It aims at the identification of heterogeneities within a multivariate data set, thus permitting to focalize critical periods where significant changes in signal characteristics are encountered. In particular we exploit the flexibility of the software, as it allows a synoptical representation combining the results obtained with the Kohonen Maps and Cluster Analysis (Figs. 1, 2). For clustering we focus on Fuzzy Cluster Analysis, expressing the class membership of a pattern by a vector rather than a single value or ID. In so doing, we can effectively distinguish between phases in which volcanic tremor characteristics change rapidly and those where changes are smoother. The comparison of the time development of tremor characteristics obtained from other disciplines (such as volcanology, petrology) is intriguing, as it furnishes background information about the physical reasons of changes in tremor features. Particular attention is devoted to transitions from pre-eruptive to eruptive activity, such as the onset of Strombolian activity, often heralding episodes of lava fountaining. We investigate possible differences in the regimes of seismic radiation prior to summit (Strombolian or lava fountaining) and flank activity (opening of fissures, short-lived lava fountaining, lava flow emission) observed in 2007 and 2008, and compare them to changes in the patterns of eruptive activity based on field and other observations available for these years. We also discuss a possible near-real time application of these techniques, which may offer interesting perspectives to monitoring and early warning.
    Description: Published
    Description: Nicolosi (Italy)
    Description: 1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
    Description: 1.5. TTC - Sorveglianza dell'attività eruttiva dei vulcani
    Description: open
    Keywords: VOLCANIC TREMOR ; PATTERN RECOGNITION ; KOHONEN MAP ; CLASSIFICATION ; CLUSTER ANALYSIS ; 04. Solid Earth::04.06. Seismology::04.06.06. Surveys, measurements, and monitoring ; 04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology ; 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::05.01.05. Algorithms and implementation
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Abstract
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  • 3
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    New inferences from spectral seismic energy measurement of a link between regional seismicity and volcanic activity at Mt. Etna, Italy (2009)
    EGU
    Details
    Publication Date: 2017-04-04
    Description: The existence of a relationship between regional seismicity and changes in volcanic activity has been the subject of several studies in the last years. Generally, activity in basaltic volcanoes such as Villarica (Chile) and Tungurahua (Ecuador) shows very little changes after the occurrence of regional earthquakes. In a few cases volcanic activity has changed before the occurrence of regional earthquakes, such as observed at Teide, Tenerife, in 2004 and 2005 (Tárraga et al., 2006). In this paper we explore the possible link between regional seismicity and changes in volcanic activity at Mt. Etna in 2006 and 2007. On 24 November, 2006 at 4:37:40 GMT an earthquake of magnitude 4.7 stroke the eastern coast of Sicily. The epicenter was localized 50 km SE of the south coast of the island, and at about 160 km from the summit craters of Mt. Etna. The SSEM (Spectral Seismic Energy Measurement) of the seismic signal at stations at 1 km and 6 km from the craters highlights that four hours before this earthquake the energy associated with volcanic tremor increased, reached a maximum, and finally became steady when the earthquake occurred. Conversely, neither before nor after the earthquake, the SSEM of stations located between 80 km and 120 km from the epicentre and outside the volcano edifice showed changes. On 5 September, 2007 at 21:24:13 GMT an earthquake of magnitude 3.2 and 7.9 km depth stroke the Lipari Island, at the north of Sicily. About 38 hours before the earthquake occurrence, there was an episode of lava fountain lasting 20 hours at Etna volcano. The SSEM of the seismic signal recorded during the lava fountain at a station located at 6 km from the craters highlights changes heralding this earthquake ten hours before its occurrence using the FFM method (e.g., Voight, 1988; Ortiz et al., 2003). A change in volcanic activity – with the onset of ash emission and Strombolian explosions – was observed a couple of hours before the occurrence of the regional earthquakes. It can be interpreted as the magmatic response to a change of the distribution of tectonic stress in the edifice before the earthquake. In the light of this hypothesis, we surmise that the magmatic system behaved similar to a dilatometer and promise news lines to forecasting the volcano activity.
    Description: EGU
    Description: Published
    Description: Vienna (Austria)
    Description: 1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
    Description: open
    Keywords: VOLCANIC ACTIVITY ; TREMOR ; EARTHQUAKE ; SSEM ; FAILURE FORECAST METHOD ; 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 ; 04. Solid Earth::04.06. Seismology::04.06.10. Instruments and techniques ; 05. General::05.01. Computational geophysics::05.01.01. Data processing ; 05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Abstract
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  • 4
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    Identification of activity regimes by unsupervised pattern classification of volcanic tremor data. Case studies from Mt. Etna (2009)
    EGU
    Details
    Publication Date: 2017-04-04
    Description: The monitoring of the seismic background signal – commonly referred to as volcanic tremor - has become a key tool for volcanic surveillance, particularly when field surveys are unsafe and/or visual observations are hampered by bad weather conditions. Indeed, it could be demonstrated that changes in the state of activity of the volcano show up in the volcanic tremor signature, such as amplitude and frequency content. Hence, the analysis of the characteristics of volcanic tremor leads us to pass from a mere monoparametric vision of the data to a multivariate one, which can be tackled with modern concepts of multivariate statistics. For this aim we present a recently developed software package which combines various concepts of unsupervised classification, in particular cluster analysis and Kohonen maps. Unsupervised classification is based on a suitable definition of similarity between patterns rather than on a-priori knowledge of their class membership. It aims at the identification of heterogeneities within a multivariate data set, thus permitting to focalize critical periods where significant changes in signal characteristics are encountered. The application of the software is demonstrated on sample sets derived from Mt. Etna during eruptions in 2001, 2006 and 2007-8.
    Description: EGU
    Description: Published
    Description: Vienna (Austria)
    Description: 1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
    Description: 1.5. TTC - Sorveglianza dell'attività eruttiva dei vulcani
    Description: open
    Keywords: PATTERN CLASSIFICATION ; TREMOR ; KOHONEN MAP ; CLUSTER ANALYSIS ; 04. Solid Earth::04.06. Seismology::04.06.06. Surveys, measurements, and 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 ; 05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks ; 05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Abstract
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  • 5
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    A Software Package for Unsupervised Pattern Recognition and Synoptic Representation of Results: Application to Volcanic Tremor Data of Mt Etna (2009)
    Eos,Vol. 90, Number 52, 29 December 2009, Fall Meet. Suppl., Abstract NH53-A1076
    Details
    Publication Date: 2017-04-04
    Description: Artificial Intelligence (AI) has found broad applications in volcano observatories worldwide with the aim of reducing volcanic hazard. The need to process larger and larger quantity of data makes indeed AI techniques appealing for monitoring purposes. Tools based on Artificial Neural Networks and Support Vector Machine have proved to be particularly successful in the classification of seismic events and volcanic tremor changes heralding eruptive activity, such as paroxysmal explosions and lava fountaining at Stromboli and Mt Etna, Italy (e.g., Falsaperla et al., 1996; Langer et al., 2009). Moving on from the excellent results obtained from these applications, we present KKAnalysis, a MATLAB based software which combines several unsupervised pattern classification methods, exploiting routines of the SOM Toolbox 2 for MATLAB (http://www.cis.hut.fi/projects/somtoolbox). KKAnalysis is based on Self Organizing Maps (SOM) and clustering methods consisting of K-Means, Fuzzy C-Means, and a scheme based on a metrics accounting for correlation between components of the feature vector. We show examples of applications of this tool to volcanic tremor data recorded at Mt Etna between 2007 and 2009. This time span - during which Strombolian explosions, 7 episodes of lava fountaining and effusive activity occurred - is particularly interesting, as it encompassed different states of volcanic activity (i.e., non-eruptive, eruptive according to different styles) for the unsupervised classifier to identify, highlighting their development in time. Even subtle changes in the signal characteristics allow the unsupervised classifier to recognize features belonging to the different classes and stages of volcanic activity. A convenient color-code representation shows up the temporal development of the different classes of signal, making this method extremely helpful for monitoring purposes and surveillance. Though being developed for volcanic tremor classification, KKAnalysis is generally applicable to any type of physical or chemical pattern, provided that feature vectors are given in numerical form.
    Description: Published
    Description: San Francisco, California, USA
    Description: 1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
    Description: open
    Keywords: Volcano seismology ; Pattern recognition ; 05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
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  • 6
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    TREMOrEC: a software utility for automatic classification of volcanic tremor (2007)
    Details
    Publication Date: 2012-02-03
    Description: A paper on the software TREMOrEC has been accepted for publication on G-cubed.
    Description: We describe a stand-alone software utility named TREMOrEC, which carries out training and test of a Support Vector Machine (SVM) classifier. TREMOrEC is developed in Visual C++ and runs under Microsoft Windows operating systems. Ease of use and short time processing, along with the excellent performance of the SVM classifier, make this tool ideal for volcano monitoring. The development of TREMOrEC is motivated by the successful application of the SVM classifier to volcanic tremor data recorded at Mount Etna in 2001 [Masotti et al,. 2006]. In that application, spectrograms of volcanic tremor were divided according to their recording date into four classes associated with different states of activity, i.e., pre-eruptive, lava fountain, eruptive, or post-eruptive. During the training, SVM learned the a-priori classification. The classifier’s performance was then evaluated on test sets not considered for training. The classification results matched the actual class membership with less than 6% of error.
    Description: This work was financially supported by Istituto Nazionale di Geofisica e Vulcanologia and Dipartimento per la Protezione Civile (projects V4/02 and V4/03).
    Description: Submitted
    Description: 1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
    Description: open
    Keywords: software ; Support Vector Machine ; 05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: web product
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  • 7
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    Data mining in the context of monitoring Mt Etna, Italy (2015)
    Details
    Publication Date: 2017-04-04
    Description: The persistent volcanic activity of Mt Etna makes the continuous monitoring of multidisciplinary data a first-class issue. Indeed, the monitoring systems rapidly accumulate huge quantity of data, arising specific problems of an- dling and interpretation. In order to respond to these problems, the INGV staff has developed a number of software tools for data mining. These tools have the scope of identifying structures in the data that can be related to volcanic activity, furnishing criteria for the identification of precursory scenarios. In particular, we use methods of clustering and classification in which data are divided into groups according to a- priori-defined measures of similarity or distance. Data groups may assume various shapes, such as convex clouds or complex concave bodies.The “KKAnalysis” software package is a basket of clustering methods. Currently, it is one of the key techniques of the tremor-based automatic alarm systems of INGV Osservatorio Etneo. It exploits both Self-Organizing Maps and Fuzzy Clustering. Beside seismic data, the software has been applied to the geo- chemical composition of eruptive products as well as a combined analysis of gas-emission (radon) and seismic data. The “DBSCAN” package exploits a concept based on density-based clustering. This method allows discovering clusters with arbitrary shape. Clusters are defined as dense regions of objects in the data space separated by re- gions of low density. In DBSCAN a cluster grows as long as the density within a group of objects exceeds some threshold. In the context of volcano monitoring, the method is particularly promising in the recognition of ash par- ticles as they have a rather irregular shape. The “MOTIF” software allows us to identify typical waveforms in time series, outperforming methods like cross-correlation that entail a high computational effort. MOTIF can recognize the non-imilarity of two patterns on a small number of data points without going through the whole length of data vectors. All the developments aforementioned come along with modules for feature extraction and post-processing. Spe- cific attention is devoted to the obustness of the feature extraction to avoid misinterpretations due to the presence of disturbances from environmental noise or other undesired signals originating from the source, which are not relevant for the purpose of volcano surveillance.
    Description: Unpublished
    Description: Vienna (Austria)
    Description: 2V. Dinamiche di unrest e scenari pre-eruttivi
    Description: open
    Keywords: Etna, Data mining ; Self Organizing Map, Clustering methods ; Pattern classification ; 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::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks ; 05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Poster session
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  • 8
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    Data Mining in the Context of Monitoring Mt Etna, Italy (2016)
    Details
    Publication Date: 2017-04-04
    Description: The persistent volcanic activity of Mt Etna makes the continuous monitoring of multidisciplinary data a first-class issue. Indeed, the monitoring systems rapidly accumulate huge quantity of data, arising specific problems of andling and interpretation. In order to respond to these problems, the INGV staff has developed a number of software tools for data mining. These tools have the scope of identifying structures in the data that can be related to volcanic activity, furnishing criteria for the identification of precursory scenarios. In particular, we use methods of clustering and classification in which data are divided into groups according to apriori- defined measures of similarity or distance. Data groups may assume various shapes, such as convex clouds or complex concave bodies.The “KKAnalysis” software package is a basket of clustering methods. Currently, it is one of the key techniques of the tremor-based automatic alarm systems of INGV Osservatorio Etneo. It exploits both Self-Organizing Maps and Fuzzy Clustering. Beside seismic data, the software has been applied to the geochemical composition of eruptive products as well as a combined analysis of gas-emission (radon) and seismic data. The “DBSCAN” package exploits a concept based on density-based clustering. This method allows discovering clusters with arbitrary shape. Clusters are defined as dense regions of objects in the data space separated by regions of low density. In DBSCAN a cluster grows as long as the density within a group of objects exceeds some threshold. In the context of volcano monitoring, the method is particularly promising in the recognition of ash particles as they have a rather irregular shape. The “MOTIF” software allows us to identify typical waveforms in time series, outperforming methods like cross-correlation that entail a high computational effort. MOTIF can recognize the non-imilarity of two patterns on a small number of data points without going through the whole length of data vectors. All the developments aforementioned come along with modules for feature extraction and post-processing. Specific attention is devoted to the obustness of the feature extraction to avoid misinterpretations due to the presence of disturbances from environmental noise or other undesired signals originating from the source, which are not relevant for the purpose of volcano surveillance.
    Description: Published
    Description: Vienna, Austria
    Description: 3IT. Calcolo scientifico e sistemi informatici
    Description: open
    Keywords: Data mining ; Monitoring ; Etna ; 05. General::05.01. Computational geophysics::05.01.05. Algorithms and implementation
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Poster session
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