Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/6052
DC Field | Value | Language |
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dc.contributor.authorall | GIACCO, F.; Department of Physics, University of Salerno, Italy | en |
dc.contributor.authorall | ESPOSITO, A.M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia | en |
dc.contributor.authorall | SCARPETTA, S.; Department of Physics, University of Salerno, Italy; INFN and INFM CNISM, Salerno, Italy; Institute for Advanced Scientific Studies, Vietri sul Mare, Italy, Germany | en |
dc.contributor.authorall | GIUDICEPIETRO, F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia | en |
dc.contributor.authorall | MARINARO, M.; Department of Physics, University of Salerno, Italy; INFN and INFM CNISM, Salerno, Italy; Institute for Advanced Scientific Studies, Vietri sul Mare, Italy, Germany | en |
dc.contributor.editorall | Apolloni, B.; Università degli Studi di Milano, Dipartimento di Scienze dell’Informazione,Milano, Italy | en |
dc.contributor.editorall | Bassis, S.; Università degli Studi di Milano, Dipartimento di Scienze dell’Informazione,Milano, Italy | en |
dc.contributor.editorall | Morabito, C.F.; Università di Reggio Calabria, IMET, Loc. Feo di Vito,Reggio Calabria, Italy | en |
dc.date.accessioned | 2010-06-29T11:02:02Z | en |
dc.date.available | 2010-06-29T11:02:02Z | en |
dc.date.issued | 2009-05-28 | en |
dc.identifier.uri | http://hdl.handle.net/2122/6052 | en |
dc.description.abstract | We applied and compared two supervised pattern recognition techniques, namely the Multilayer Perceptron (MLP) and Support Vector Machine (SVM), to classify seismic signals recorded on Stromboli volcano. The available data are firstly preprocessed in order to obtain a compact representation of the raw seismic signals. We extract from data spectral and temporal information so that each input vector is made up of 71 components, containing both spectral and temporal information extracted from the early signal. We implemented two classification strategies to discriminate three different seismic events: landslide, explosion-quake, and volcanic microtremor signals. The first method is a two-layer MLP network, with a Cross-Entropy error function and logistic activation function for the output units. The second method is a Support Vector Machine, whose multi-class setting is accomplished through a 1vsAll architecture with gaussian kernel. The experiments show that although the MLP produces very good results, the SVM accuracy is always higher, both in term of best performance, 99.5%, and average performance, 98.8%, obtained with different sampling permutations of training and test sets | en |
dc.language.iso | English | en |
dc.publisher.name | IOS Press BV | en |
dc.relation.ispartof | NEURAL NETS WIRN09 ; Proceedings of the 19th Italian Workshop on Neural Nets | en |
dc.subject | Seismic signals discrimination, | en |
dc.subject | Linear Predictive Coding, | en |
dc.subject | Support Vector Machine, | en |
dc.subject | Multilayer Perceptron. | en |
dc.title | Support Vector Machines and MLP for automatic classification of seismic signals at Stromboli volcano | en |
dc.type | Conference paper | en |
dc.description.status | Published | en |
dc.subject.INGV | 05. General::05.01. Computational geophysics::05.01.02. Cellular automata, fuzzy logic, genetic alghoritms, neural networks | en |
dc.description.ConferenceLocation | Vietri sul Mare (Salerno) | en |
dc.relation.references | W. De Cesare, M. Orazi, R. Peluso, G. Scarpato, A. Caputo, L. D’Auria, F. Giudicepietro, M. Martini, C. Buonocunto, M. Capello, A. M. Esposito (2009) - The broadband seismic network of Stromboli volcano (Italy), Seismological Research Letters. In press. [2] M. Martini, F. Giudicepietro, L. D’auria, A. M. Esposito, T. Caputo, R. Curciotti, W. De Cesare, M. Orazi, G. Scarpato, A. Caputo, R. Peluso, P. Ricciolino, A. Linde, S. Sacks (2008) - Seismological monitoring of the February 2007 effusive eruption of the Stromboli volcano, Annals of Geophysics, Vol. 50, N. 6, December 2007, pp. 775-788. [3] Hartse, H. E.,W. S. Phillips, M. C. Fehler, and L. S. House (1995). Singlestation spectral discrimination using coda waves, Bull. Seism. Soc. Am. 85, 1464U˝ 1474. [4] 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, no. 1, 215U˝ 223. [5] Joswig, M. (1990). Pattern recognition for earthquake detection, Bull. Seism. Soc. Am. 80, 170U˝ 186.Rowe, C. A., C. H. Thurber, and R. A. White (2004). Dome growth behavior at Soufriere Hills volcano, Montserrat, revealed by relocation of volcanic event swarms, 1995U˝ 1996, J. Volc. Geotherm. Res. 134, 199U˝ 221. [7] Dowla, F. U. (1995). Neural networks in seismic discrimination, in Monitoring a Comprehensive Test Ban Treaty, E. S. Husebye and A. M. Dainty (Editors), NATO ASI, Series E, Vol. 303, Kluwer, Dordrecht, The Netherlands, 777U˝ 789. [8] Wang, J., and T. Teng (1995). Artificial neural network based seismic detector, Bull. Seism. Soc. Am. 85, 308U˝ 319. [9] Tiira, T. (1999). Detecting teleseismic events using artificial neural networks, Comp. Geosci. 25, 929U˝ 939. [10] Esposito, M., F. Giudicepietro, L. D’Auria, S. Scarpetta, M. G. Martini, M. Coltelli, and M. Marinaro (2008). Unsupervised Neural Analysis of Very-Long-Period Events at Stromboli Volcano Using the Self-Organizing Maps, Bull. Seism. Soc. Am., Vol. 98, No. 5, pp. 2449U˝ 2459. [11] Gitterman, Y., V. Pinky, and A. Shapira (1999). Spectral discrimination analysis of Eurasian nuclear tests and earthquakes recorded by the Israel seismic network and the NORESS array, Phys. Earth. Planet. Interiors 113, 111U˝ 129. [12] Martini, M., B. Chouet, L. DŠAuria, F. Giudicepietro, and P. Dawson (2004). The seismic source stability of the Very Long Period signals of the Stromboli volcano, in I General Assembly AbstractsU˝ EGU, Nice, 25U˝ 30 April 2004. [13] Vapnik, V. N. (1995). The Nature of Statistical Learning Theory, Springer. [14] Webb, A. R. (2002). Statistical Pattern Recognition, John Wiley and Sons. [15] Schffolkopf, B. and A.J. Smola (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, MIT Press. [16] Melgani, F. and L. Bruzzone (2004). Classification of hyperspectral remote sensing images with support vector machines, IEEE Trans. on Geoscience and Remote Sensing, vol. 42, pp. 1778-1790. [17] Foody, G. F. and Ajay Mathur (2004). A relative evaluation of multiclass image classification by support vector machines, IEEE Trans. on Geoscience and Remote Sensing, vol. 42, pp. 1335-1343. [18] Hsu, C.W. and C. J. Lin (2002). A comparison of methods for multiclass support vector machines, IEEE Trans. on Neural Networks, vol. 13, pp. 415-425. [19] 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. [20] Kahsay, L., F. Schwenker and G. Palm (2005). Comparison of multiclass SVM decomposition schemes for visual object recognition, LNCS, Springer, vol. 3663, pp. 334-341. [21] Makhoul, J. (1975). Linear prediction: a tutorial review, Proc. IEEE 63, 561-580. [22] Bishop, C. (1995). Neural Networks for Pattern Recognition, Oxford University Press, New York, 500 pp. [23] F.Giacco, S. Scarpetta, L. Pugliese, M. Marinaro and C. Thiel. Application of Self organizing Maps to multi-resolution and multi-spectral remote sensed images, “New directions in neural networks”, Proceedings of 18th ItalianWorkshop on neural networks (WIRN 2008), IOS Press (Netherlands), pp. 245- 253. [24] C. Thiel, F. Giacco, F. Shwenker G. Palm. Comparison of neural Classification Algorithms applied to land cover mapping, “New directions in neural networks”, Proceedings of 18th Italian Workshop on neural networks (WIRN 2008), IOS Press (Netherlands), pp. 254-263. [25] A. M. Esposito, F. Giudicepietro, S. Scarpetta, L. D’Auria, M. Marinaro, M. Martini (2006) - Automatic discrimination among landslide, explosion-quake and microtremor seismic signals at Stromboli volcano using Neural Networks, Bull. Seismol. Soc. Am. (BSSA) Vol. 96, No. 4A, pp. 1230-1240, August 2006, doi: 10.1785/0120050097 | en |
dc.description.obiettivoSpecifico | 2V. Struttura e sistema di alimentazione dei vulcani | en |
dc.description.fulltext | open | en |
dc.contributor.author | Giacco, F. | en |
dc.contributor.author | ESPOSITO, A.M. | en |
dc.contributor.author | SCARPETTA, S. | en |
dc.contributor.author | GIUDICEPIETRO, F. | en |
dc.contributor.author | MARINARO, M. | en |
dc.contributor.department | Department of Physics, University of Salerno, Italy | en |
dc.contributor.department | Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia | en |
dc.contributor.department | Department of Physics, University of Salerno, Italy; INFN and INFM CNISM, Salerno, Italy; Institute for Advanced Scientific Studies, Vietri sul Mare, Italy, Germany | en |
dc.contributor.department | Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia | en |
dc.contributor.department | Department of Physics, University of Salerno, Italy; INFN and INFM CNISM, Salerno, Italy; Institute for Advanced Scientific Studies, Vietri sul Mare, Italy, Germany | en |
dc.contributor.editor | Apolloni, B. | en |
dc.contributor.editor | Bassis, S. | en |
dc.contributor.editordepartment | Università degli Studi di Milano, Dipartimento di Scienze dell’Informazione,Milano, Italy | en |
dc.contributor.editordepartment | Università degli Studi di Milano, Dipartimento di Scienze dell’Informazione,Milano, Italy | en |
dc.contributor.editordepartment | Università di Reggio Calabria, IMET, Loc. Feo di Vito,Reggio Calabria, Italy | en |
item.openairetype | Conference paper | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | Second University of Naples | - |
crisitem.author.dept | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione OV, Napoli, Italia | - |
crisitem.author.dept | Istituto Nazionale per la Fisica della Materia Sezione di Salerno and Istituto Nazionale di Fisica Nucleare Gruppo Collegato di Salerno, Italy | - |
crisitem.author.dept | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione OV, Napoli, Italia | - |
crisitem.author.dept | Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione OV, Napoli, Italia | - |
crisitem.author.orcid | 0000-0003-2192-3720 | - |
crisitem.author.orcid | 0000-0001-6198-8655 | - |
crisitem.author.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.author.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.author.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.classification.parent | 05. General | - |
crisitem.department.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
crisitem.department.parentorg | Istituto Nazionale di Geofisica e Vulcanologia | - |
Appears in Collections: | Conference materials |
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