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TREMOrEC: A software utility for automatic classification of volcanic tremor
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| Zeitschriftentitel: | Geochemistry, Geophysics, Geosystems |
|---|---|
| Personen und Körperschaften: | , , , , , |
| In: | Geochemistry, Geophysics, Geosystems, 9, 2008, 4 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Geophysical Union (AGU)
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| Schlagwörter: |
| author_facet |
Masotti, M. Campanini, R. Mazzacurati, L. Falsaperla, S. Langer, H. Spampinato, S. Masotti, M. Campanini, R. Mazzacurati, L. Falsaperla, S. Langer, H. Spampinato, S. |
|---|---|
| author |
Masotti, M. Campanini, R. Mazzacurati, L. Falsaperla, S. Langer, H. Spampinato, S. |
| spellingShingle |
Masotti, M. Campanini, R. Mazzacurati, L. Falsaperla, S. Langer, H. Spampinato, S. Geochemistry, Geophysics, Geosystems TREMOrEC: A software utility for automatic classification of volcanic tremor Geochemistry and Petrology Geophysics |
| author_sort |
masotti, m. |
| spelling |
Masotti, M. Campanini, R. Mazzacurati, L. Falsaperla, S. Langer, H. Spampinato, S. 1525-2027 1525-2027 American Geophysical Union (AGU) Geochemistry and Petrology Geophysics http://dx.doi.org/10.1029/2007gc001860 <jats:p>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. 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 an error of less than 6%.</jats:p> TREMOrEC: A software utility for automatic classification of volcanic tremor Geochemistry, Geophysics, Geosystems |
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TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_unstemmed |
TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_full |
TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_fullStr |
TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_full_unstemmed |
TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_short |
TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_sort |
tremorec: a software utility for automatic classification of volcanic tremor |
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Geochemistry and Petrology Geophysics |
| url |
http://dx.doi.org/10.1029/2007gc001860 |
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2008 |
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<jats:p>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. 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 an error of less than 6%.</jats:p> |
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| author | Masotti, M., Campanini, R., Mazzacurati, L., Falsaperla, S., Langer, H., Spampinato, S. |
| author_facet | Masotti, M., Campanini, R., Mazzacurati, L., Falsaperla, S., Langer, H., Spampinato, S., Masotti, M., Campanini, R., Mazzacurati, L., Falsaperla, S., Langer, H., Spampinato, S. |
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| description | <jats:p>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. 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 an error of less than 6%.</jats:p> |
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| spelling | Masotti, M. Campanini, R. Mazzacurati, L. Falsaperla, S. Langer, H. Spampinato, S. 1525-2027 1525-2027 American Geophysical Union (AGU) Geochemistry and Petrology Geophysics http://dx.doi.org/10.1029/2007gc001860 <jats:p>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. 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 an error of less than 6%.</jats:p> TREMOrEC: A software utility for automatic classification of volcanic tremor Geochemistry, Geophysics, Geosystems |
| spellingShingle | Masotti, M., Campanini, R., Mazzacurati, L., Falsaperla, S., Langer, H., Spampinato, S., Geochemistry, Geophysics, Geosystems, TREMOrEC: A software utility for automatic classification of volcanic tremor, Geochemistry and Petrology, Geophysics |
| title | TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_full | TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_fullStr | TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_full_unstemmed | TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_short | TREMOrEC: A software utility for automatic classification of volcanic tremor |
| title_sort | tremorec: a software utility for automatic classification of volcanic tremor |
| title_unstemmed | TREMOrEC: A software utility for automatic classification of volcanic tremor |
| topic | Geochemistry and Petrology, Geophysics |
| url | http://dx.doi.org/10.1029/2007gc001860 |