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Automatic Classification of Seismic Signals at Mt. Vesuvius Volcano, Italy, Using Neural Networks
Author(s)
Language
English
Status
Published
Peer review journal
Yes
Title of the book
Issue/vol(year)
95, 1
Pages (printed)
185-196
Issued date
2005
Alternative Location
Abstract
We present a new strategy for reliable automatic classification of local seismic signals and volcano-tectonic earthquakes (VT). The method is based on a supervised neural network in which a new approach for feature extraction from short period seismic signals is applied. To reduce the number of records required for the analysis we set up a specialized neural classifier, able to distinguish two classes of signals, for each of the selected stations. The neural network architecture is a multilayer
perceptron (MLP) with a single hidden layer. Spectral features of the signals
and the parameterized attributes of their waveform have been used as input for this
network. Feature extraction is done by using both the linear predictor coding technique for computing the spectrograms, and a function of the amplitude for characterizing waveforms. Compared to strategies that use only spectral signatures, the inclusion of properly normalized amplitude features improves the performance of the classifiers, and allows the network to better generalize. To train the MLP network we compared the performance of the quasi-Newton algorithm with the scaled conjugate gradient method. We found that the scaled conjugate gradient approach is the faster of the two, with quite equally good performance. Our method was tested on a dataset recorded by four selected stations of the Mt. Vesuvius monitoring network, for the discrimination of low magnitude VT events and transient signals caused by either artificial (quarry blasts, underwater explosions) and natural (thunder) sources. In this test application we obtained 100% correct classification for one of the possible pairs of signal types (VT versus quarry blasts). Because this method was developed independently of this particular discrimination task, it can be applied to a broad range of other applications.
perceptron (MLP) with a single hidden layer. Spectral features of the signals
and the parameterized attributes of their waveform have been used as input for this
network. Feature extraction is done by using both the linear predictor coding technique for computing the spectrograms, and a function of the amplitude for characterizing waveforms. Compared to strategies that use only spectral signatures, the inclusion of properly normalized amplitude features improves the performance of the classifiers, and allows the network to better generalize. To train the MLP network we compared the performance of the quasi-Newton algorithm with the scaled conjugate gradient method. We found that the scaled conjugate gradient approach is the faster of the two, with quite equally good performance. Our method was tested on a dataset recorded by four selected stations of the Mt. Vesuvius monitoring network, for the discrimination of low magnitude VT events and transient signals caused by either artificial (quarry blasts, underwater explosions) and natural (thunder) sources. In this test application we obtained 100% correct classification for one of the possible pairs of signal types (VT versus quarry blasts). Because this method was developed independently of this particular discrimination task, it can be applied to a broad range of other applications.
References
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Castellano, M., C. Buonocunto, M. Capello, and M. La Rocca (2002). Seismic surveillance of active volcanoes: the Osservatorio Vesuviano Seismic Network (OVSN Southern Italy), Seism. Res. Lett. 73, 177–184.
Chouet, B. A. (1992). A seismic model for the source of long-period events and harmonic tremor, in Volcanic Seismology, P. Gasparini, R. Scarpa, and K. Aki (Editors), IAVCEI Proceedings in Volcanology, Vol. 3, Springer-Verlag, Berlin, 23 pp.
Del Pezzo, E., and S. Petrosino (2001). A local-magnitude scale for Mt. Vesuvius from synthetic Wood-Anderson seismograms, J. Seism. 5, 207–215.
Del Pezzo, E., F. Bianco, and G. Saccorotti (2004). Seismic source dynamics at Mt. Vesuvius volcano, Italy. J. Volcanol. Geoth. Res. 133, 23–39.
Del Pezzo, E., A. Esposito, F. Giudicepietro, M. Marinaro, M. Martini, and S. Scarpetta (2003). Discrimination of earthquakes and underwater explosions using neural networks, Bull. Seism. Soc. Am. 93, 215–223.
Esposito, A., M. Falanga, M. Funaro, M. Marinaro, and S. Scarpetta (2001). Signal Classification using Neural Networks, in Proceedings of WIRN ’01 (Workshop Italiano Reti Neurali) 17–19 May 2001, Vietri, pp. 187–1192.
Falsaperla, S., S. Graziani, G. Nunnari, and S. Spampinato (1996). Automatic classification of volcanic earthquakes by using multi-layered neural networks, Natural Hazards 13, 205–228.
Fedorenko, Y., E. S. Husebye, and B. O. Ruud (1999). Explosion site recognition: neural net discriminator using single three-component stations, Phys. Earth Planet. Interiors 113, 131–142.
Gendron, P., J. Ebel, and D. Manolakis (2000). Rapid joint detection and classification with wavelet bases via bayes Theorem, Bull. Seism. Soc. Am. 90, 764–774.
Giudicepietro, F., W. De Cesare, M. Martini, and V. Meglio, (2000). Il Sistema Sismometrico Modulare Integrato (SISMI). Open-File Report Osservatorio Vesuviano INGV, no. 6, 2000 (in Italian).
Hertz, J., A. Krogh, and G. Richard (1991). Introduction to the Theory of Neural Computation, Addison-Wesley, Redwood City.
Hoffmann, W., R. Kebeasy, and P. Firbas (1999). Introduction to the verification regime of the Comprehensive Nuclear-Test-Ban Treaty, Phys. Earth Planet. Int. 113, 5–9.
Joswig, M. (1990). Pattern recognition for earthquake detection, Bull. Seism. Soc. Am. 80, 170–186.
Kushnir, A. F., V. M. Lapshin, V. I. Pinsky, and J. Fyen (1990). Statistically optimal event detection using small array data, Bull. Seism. Soc. Am. 80, 1934–1950.
Kushnir, A. F., E. V. Troitsky, L. M. Haikin, and A. Dainty (1999). Statistical classification approach to discrimination between weak earthquakes and quarry blasts recorded by the Israel Seismic Network, Phys. Earth Planet. Int. 113, 161–182.
Makhoul, J. (1975). Linear Prediction: A Tutorial Review, in Proceedings of the IEEE 63.
Marzocchi, W., G. Vilardo, D. P. Hill, G. P. Ricciardi, and C. Ricco (2001). Common features and peculiarity of the seismic activity at Phlegraean Fields, Long Valley, and Vesuvius. Bull. Seism. Soc. Am. 91, 191–205.
Moller, M. (1993). A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks 6, 525–533.
Musil, M., and A. Plesinger (1996). discrimination between local microearthquakes and quarry blasts by multi-layer perceptrons and kohonen maps, Bull. Seism. Soc. Am. 86, 1077–1090.
Saccorotti, G., R. Maresca, and E. Del Pezzo (2001). Array analyses of seismic noise at Mt. Vesuvius volcano, Italy, J. Volcanol. Geotherm. Res. 110, 79–100.
Shewchuk, J. R. (1994). An Introduction to the Conjugate Gradient Method Without the Agonizing Pain, Technical Report CMU-CS-94-125, Carnegie Mellon University, Pittsburgh.
Shumway, R.H. (1996). Statistical Approaches to Seismic Discrimination, in Monitoring a Comprehensive Test Ban Treaty, E. S. Husebye and A. M. Dainty (Editors), NATO Advanced Science Institute Series, Kluwer Academic Publishers, Boston, 791–803.
Tarvainen, M. (1999). Recognizing explosion sites with a self-organizing network for unsupervised learning, Phys. Earth Planet. Int. 113, 143–154
Ursino, A., H. Langer, L. Scarfı`, G. Di Grazia, and S. Gresta (2001). Discrimination of quarry blasts from tectonic microearthquakes in the Hyblean Plateau (Southeastern Sicily), Annali di Geofisica 44, 703–722.
Van Ooyen, A., and B. Nienhuis (1992). Improving the convergence of the backpropagation algorithm, Neural Networks 5, 465–471.
Wuster, J. (1993). Discrimination of chemical explosions and earthquakes in central europe—a case study, Bull. Seism. Soc. Am. 83, 1184–1212.
Castellano, M., C. Buonocunto, M. Capello, and M. La Rocca (2002). Seismic surveillance of active volcanoes: the Osservatorio Vesuviano Seismic Network (OVSN Southern Italy), Seism. Res. Lett. 73, 177–184.
Chouet, B. A. (1992). A seismic model for the source of long-period events and harmonic tremor, in Volcanic Seismology, P. Gasparini, R. Scarpa, and K. Aki (Editors), IAVCEI Proceedings in Volcanology, Vol. 3, Springer-Verlag, Berlin, 23 pp.
Del Pezzo, E., and S. Petrosino (2001). A local-magnitude scale for Mt. Vesuvius from synthetic Wood-Anderson seismograms, J. Seism. 5, 207–215.
Del Pezzo, E., F. Bianco, and G. Saccorotti (2004). Seismic source dynamics at Mt. Vesuvius volcano, Italy. J. Volcanol. Geoth. Res. 133, 23–39.
Del Pezzo, E., A. Esposito, F. Giudicepietro, M. Marinaro, M. Martini, and S. Scarpetta (2003). Discrimination of earthquakes and underwater explosions using neural networks, Bull. Seism. Soc. Am. 93, 215–223.
Esposito, A., M. Falanga, M. Funaro, M. Marinaro, and S. Scarpetta (2001). Signal Classification using Neural Networks, in Proceedings of WIRN ’01 (Workshop Italiano Reti Neurali) 17–19 May 2001, Vietri, pp. 187–1192.
Falsaperla, S., S. Graziani, G. Nunnari, and S. Spampinato (1996). Automatic classification of volcanic earthquakes by using multi-layered neural networks, Natural Hazards 13, 205–228.
Fedorenko, Y., E. S. Husebye, and B. O. Ruud (1999). Explosion site recognition: neural net discriminator using single three-component stations, Phys. Earth Planet. Interiors 113, 131–142.
Gendron, P., J. Ebel, and D. Manolakis (2000). Rapid joint detection and classification with wavelet bases via bayes Theorem, Bull. Seism. Soc. Am. 90, 764–774.
Giudicepietro, F., W. De Cesare, M. Martini, and V. Meglio, (2000). Il Sistema Sismometrico Modulare Integrato (SISMI). Open-File Report Osservatorio Vesuviano INGV, no. 6, 2000 (in Italian).
Hertz, J., A. Krogh, and G. Richard (1991). Introduction to the Theory of Neural Computation, Addison-Wesley, Redwood City.
Hoffmann, W., R. Kebeasy, and P. Firbas (1999). Introduction to the verification regime of the Comprehensive Nuclear-Test-Ban Treaty, Phys. Earth Planet. Int. 113, 5–9.
Joswig, M. (1990). Pattern recognition for earthquake detection, Bull. Seism. Soc. Am. 80, 170–186.
Kushnir, A. F., V. M. Lapshin, V. I. Pinsky, and J. Fyen (1990). Statistically optimal event detection using small array data, Bull. Seism. Soc. Am. 80, 1934–1950.
Kushnir, A. F., E. V. Troitsky, L. M. Haikin, and A. Dainty (1999). Statistical classification approach to discrimination between weak earthquakes and quarry blasts recorded by the Israel Seismic Network, Phys. Earth Planet. Int. 113, 161–182.
Makhoul, J. (1975). Linear Prediction: A Tutorial Review, in Proceedings of the IEEE 63.
Marzocchi, W., G. Vilardo, D. P. Hill, G. P. Ricciardi, and C. Ricco (2001). Common features and peculiarity of the seismic activity at Phlegraean Fields, Long Valley, and Vesuvius. Bull. Seism. Soc. Am. 91, 191–205.
Moller, M. (1993). A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks 6, 525–533.
Musil, M., and A. Plesinger (1996). discrimination between local microearthquakes and quarry blasts by multi-layer perceptrons and kohonen maps, Bull. Seism. Soc. Am. 86, 1077–1090.
Saccorotti, G., R. Maresca, and E. Del Pezzo (2001). Array analyses of seismic noise at Mt. Vesuvius volcano, Italy, J. Volcanol. Geotherm. Res. 110, 79–100.
Shewchuk, J. R. (1994). An Introduction to the Conjugate Gradient Method Without the Agonizing Pain, Technical Report CMU-CS-94-125, Carnegie Mellon University, Pittsburgh.
Shumway, R.H. (1996). Statistical Approaches to Seismic Discrimination, in Monitoring a Comprehensive Test Ban Treaty, E. S. Husebye and A. M. Dainty (Editors), NATO Advanced Science Institute Series, Kluwer Academic Publishers, Boston, 791–803.
Tarvainen, M. (1999). Recognizing explosion sites with a self-organizing network for unsupervised learning, Phys. Earth Planet. Int. 113, 143–154
Ursino, A., H. Langer, L. Scarfı`, G. Di Grazia, and S. Gresta (2001). Discrimination of quarry blasts from tectonic microearthquakes in the Hyblean Plateau (Southeastern Sicily), Annali di Geofisica 44, 703–722.
Van Ooyen, A., and B. Nienhuis (1992). Improving the convergence of the backpropagation algorithm, Neural Networks 5, 465–471.
Wuster, J. (1993). Discrimination of chemical explosions and earthquakes in central europe—a case study, Bull. Seism. Soc. Am. 83, 1184–1212.
Type
article