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Campanini, R.
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Campanini, R.
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- PublicationRestrictedTREMOrEC: a software utility for automatic classification of volcanic tremor(2008-04-03)
; ; ; ; ; ; ;Masotti, M.; Medical Imaging Group, Department of Physics, University of Bologna,Viale Berti-Pichat 6/2, 40127, Bologna, Italy ;Campanini, R.; Medical Imaging Group, Department of Physics, University of Bologna,Viale Berti-Pichat 6/2, 40127, Bologna, Italy ;Mazzacurati, L.; Department of Computer Science, University of Bologna, Mura Anteo-Zamboni 7, 40127, Bologna, Italy ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; ; 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.356 25 - PublicationRestrictedApplication of Support Vector Machine to the classification of volcanic tremor at Etna, Italy(2006)
; ; ; ; ; ;Masotti, M.; Medical Imaging Group, Department of Physics, University of Bologna, Bologna, Italy. ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Campanin, R.; partment of Physics, University of Bologna, Bologna, Italy.; ; ; ; We applied an automatic pattern recognition technique, known as Support Vector Machine (SVM), to classify volcanic tremor data recorded during different states of activity at Etna volcano, Italy. The seismic signal was recorded at a station deployed 6 km southeast of the summit craters from 1 July to 15 August, 2001, a time span encompassing episodes of lava fountains and a 23 day-long effusive activity. Trained by a supervised learning algorithm, the classifier learned to recognize patterns belonging to four classes, i.e., pre-eruptive, lava fountains, eruptive, and posteruptive. Training and test of the classifier were carried out using 425 spectrogram-based feature vectors. Following cross-validation with a random subsampling strategy, SVM correctly classified 94.7 ± 2.4% of the data. The performance was confirmed by a leave-one-out strategy, with 401 matches out of 425 patterns. Misclassifications highlighted intrinsic fuzziness of class memberships of the signals, particularly during transitional phases. Citation: Masotti, M., S. Falsaperla, H. Langer, S. Spampinato, and R. Campanini (2006), Application of Support Vector Machine to the classification of volcanic tremor at Etna, Italy, Geophys. Res. Lett., 33, L20304, doi:10.1029/2006GL027441.316 44 - PublicationRestrictedSynopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data atMt Etna, Italy(2009-03-10)
; ; ; ; ; ; ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Masotti, M.; Medical Imaging Group, Department of Physics, University of Bologna ;Campanini, R.; Medical Imaging Group, Department of Physics, University of Bologna ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Messina, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia; ; ; ; ; 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.619 34 - PublicationOpen AccessAutomatic classification of volcanic tremor using Support Vector Machine(2008)
; ; ; ; ; ;Masotti, M.; University of Bologna, Italy ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Campanini, R.; University of Bologna, Italy; ; ; ; ; ; ; ;Marzocchi, W.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Bologna, Bologna, Italia ;Zollo, A.; University Federico II, Naples, Italy; A system for automatic recognition of different volcanic activity regimes based on supervised classification of volcanic tremor is proposed. Spectrograms are calculated from volcanic tremor time-series, separated into four classes, each assumed as representative of a different state of volcanic activity, i.e., pre-eruptive, eruptive, lava fountains, and post-eruptive. As classification features, the spectral profiles obtained by averaging each spectrogram along its rows are chosen. As supervised classification strategy, the Support Vector Machine (SVM) classifier is adopted. Evaluation of the system performance is carried out on volcanic tremor data recorded at Mt Etna during the eruptive episodes of July-August 2001. The leave-one-out classification accuracy achieved is of about 94%.159 262