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Models for Identifying Structures in the Data: A Performance Comparison
Author(s)
Type
Conference paper
Language
English
Obiettivo Specifico
1.4. TTC - Sorveglianza sismologica delle aree vulcaniche attive
Editor(s)
Status
Published
Issued date
2007
Conference Location
Vietri sul Mare
Abstract
This paper reports on the unsupervised analysis of seismic signals
recorded in Italy, respectively on the Vesuvius volcano, located in Naples, and
on the Stromboli volcano, located North of Eastern Sicily. The Vesuvius dataset
is composed of earthquakes and false events like thunders, man-made quarry
and undersea explosions. The Stromboli dataset consists of explosion-quakes,
landslides and volcanic microtremor signals. The aim of this paper is to apply
on these datasets three projection methods, the linear Principal Component
Analysis (PCA), the Self-Organizing Map (SOM), and the Curvilinear
Component Analysis (CCA), in order to compare their performance. Since
these algorithms are well known to be able to exploit structures and organize
data providing a clear framework for understanding and interpreting their
relationships, this work examines the category of structural information that
they can provide on our specific sets. Moreover, the paper suggests a
breakthrough in the application area of the SOM, used here for clustering
different seismic signals. The results show that, among the three above
techniques, SOM better visualizes the complex set of high-dimensional data
discovering their intrinsic structure and eventually appropriately clustering the
different signal typologies under examination, discriminating the explosionquakes
from the landslides and microtremor recorded at the Stromboli volcano,
and the earthquakes from natural (thunders) and artificial (quarry blasts and
undersea explosions) events recorded at the Vesuvius volcano.
recorded in Italy, respectively on the Vesuvius volcano, located in Naples, and
on the Stromboli volcano, located North of Eastern Sicily. The Vesuvius dataset
is composed of earthquakes and false events like thunders, man-made quarry
and undersea explosions. The Stromboli dataset consists of explosion-quakes,
landslides and volcanic microtremor signals. The aim of this paper is to apply
on these datasets three projection methods, the linear Principal Component
Analysis (PCA), the Self-Organizing Map (SOM), and the Curvilinear
Component Analysis (CCA), in order to compare their performance. Since
these algorithms are well known to be able to exploit structures and organize
data providing a clear framework for understanding and interpreting their
relationships, this work examines the category of structural information that
they can provide on our specific sets. Moreover, the paper suggests a
breakthrough in the application area of the SOM, used here for clustering
different seismic signals. The results show that, among the three above
techniques, SOM better visualizes the complex set of high-dimensional data
discovering their intrinsic structure and eventually appropriately clustering the
different signal typologies under examination, discriminating the explosionquakes
from the landslides and microtremor recorded at the Stromboli volcano,
and the earthquakes from natural (thunders) and artificial (quarry blasts and
undersea explosions) events recorded at the Vesuvius volcano.
References
Demartines, P., Herault, J.: Curvilinear Component Analysis: A Self-Organizing Neural
Network for Nonlinear Mapping of Data Sets. IEEE Transactions on Neural
Networks, 8(1), 148–154 (1997)
2. Esposito, A.M., Giudicepietro, F., Scarpetta, S., D’Auria, L., Marinaro, M., Martini, M.:
Automatic Discrimination among Landslide, Explosion-Quake and Microtremor Seismic
Signals at Stromboli Volcano using Neural Networks. Bulletin of Seismological Society
of America (BSSA), 96(4A)
3. Esposito, A.M., Scarpetta, S., Giudicepietro, F., Masiello, S., Pugliese, L., Esposito, A.:
Nonlinear Exploratory Data Analysis Applied to Seismic Signals. In: Apolloni, B.,
Marinaro, M., Nicosia, G., Tagliaferri, R. (eds.) WIRN 2005 and NAIS 2005. LNCS,
vol. 3931, pp. 70–77. Springer, Heidelberg (2006)
4. Jollife, I.T.: Principal Component Analysis. Springer, New York (1986)
5. Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J.: SOM_PAK: The Self-Organizing
Map Program Package, Report A31. Helsinki University, Finland (1996) Also available at
http://www.cis.hut.fi/research/som_lvq_pak.shtml
6. Kohonen, T.: Self-Organizing Maps, Series in Information Sciences, 2nd edn. vol. 30.
Springer, Heidelberg (1997)
7. Lee, J.A., Lendasse, A., Verleysen, M.: Nonlinear Projection with Curvilinear Distances:
Isomap versus Curvilinear Distance Analysis. Neurocomputing, 57, 49–76 (2004)
8. Makhoul, J.: Linear Prediction: a Tutorial Review. In: Makhoul, J. (ed.) Proceeding of
IEEE, pp. 561–580. IEEE, Los Alamitos (1975)
9. Scarpetta, S., Giudicepietro, F., Ezin, E.C., Petrosino, S., Del Pezzo, E., Martini, M.,
Marinaro, M.: Automatic Classification of Seismic Signals at Mt. Vesuvius Volcano, Italy,
Using Neural Networks, Bulletin of Seismological Society of America (BSSA), Vol. 95,
pp. 185–196 (2005)
10. Wish, M., Carroll, J.D.: Multidimensional Scaling and its Applications. In: Krishnaiah,
P.R., Kanal, L.N. (eds.) Handbook of Statistics, vol. 2, pp. 317–345. North-Holland,
Amsterdam (1982)
Network for Nonlinear Mapping of Data Sets. IEEE Transactions on Neural
Networks, 8(1), 148–154 (1997)
2. Esposito, A.M., Giudicepietro, F., Scarpetta, S., D’Auria, L., Marinaro, M., Martini, M.:
Automatic Discrimination among Landslide, Explosion-Quake and Microtremor Seismic
Signals at Stromboli Volcano using Neural Networks. Bulletin of Seismological Society
of America (BSSA), 96(4A)
3. Esposito, A.M., Scarpetta, S., Giudicepietro, F., Masiello, S., Pugliese, L., Esposito, A.:
Nonlinear Exploratory Data Analysis Applied to Seismic Signals. In: Apolloni, B.,
Marinaro, M., Nicosia, G., Tagliaferri, R. (eds.) WIRN 2005 and NAIS 2005. LNCS,
vol. 3931, pp. 70–77. Springer, Heidelberg (2006)
4. Jollife, I.T.: Principal Component Analysis. Springer, New York (1986)
5. Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J.: SOM_PAK: The Self-Organizing
Map Program Package, Report A31. Helsinki University, Finland (1996) Also available at
http://www.cis.hut.fi/research/som_lvq_pak.shtml
6. Kohonen, T.: Self-Organizing Maps, Series in Information Sciences, 2nd edn. vol. 30.
Springer, Heidelberg (1997)
7. Lee, J.A., Lendasse, A., Verleysen, M.: Nonlinear Projection with Curvilinear Distances:
Isomap versus Curvilinear Distance Analysis. Neurocomputing, 57, 49–76 (2004)
8. Makhoul, J.: Linear Prediction: a Tutorial Review. In: Makhoul, J. (ed.) Proceeding of
IEEE, pp. 561–580. IEEE, Los Alamitos (1975)
9. Scarpetta, S., Giudicepietro, F., Ezin, E.C., Petrosino, S., Del Pezzo, E., Martini, M.,
Marinaro, M.: Automatic Classification of Seismic Signals at Mt. Vesuvius Volcano, Italy,
Using Neural Networks, Bulletin of Seismological Society of America (BSSA), Vol. 95,
pp. 185–196 (2005)
10. Wish, M., Carroll, J.D.: Multidimensional Scaling and its Applications. In: Krishnaiah,
P.R., Kanal, L.N. (eds.) Handbook of Statistics, vol. 2, pp. 317–345. North-Holland,
Amsterdam (1982)
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