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Seismic Signal Classification using Multi-Layer Perceptron Neural Network

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 79 - Number 15
Year of Publication: 2013
Authors:
El Hassan Ait Laasri
Es-saïd Akhouayri
Driss Agliz
Abderrahman Atmani
10.5120/13821-1950

El Hassan Ait Laasri, Es-said Akhouayri, Driss Agliz and Abderrahman Atmani. Article: Seismic Signal Classification using Multi-Layer Perceptron Neural Network. International Journal of Computer Applications 79(15):35-43, October 2013. Full text available. BibTeX

@article{key:article,
	author = {El Hassan Ait Laasri and Es-said Akhouayri and Driss Agliz and Abderrahman Atmani},
	title = {Article: Seismic Signal Classification using Multi-Layer Perceptron Neural Network},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {79},
	number = {15},
	pages = {35-43},
	month = {October},
	note = {Full text available}
}

Abstract

The aim of the present study is to investigate and explore the capability of the multilayer perceptron neural network to classify seismic signals recorded by the local seismic network of Agadir (Morocco). The problem is divided into two main steps, the feature extraction step and classification step. In the former, relevant discriminant features are extracted from the seismic signal based on the time and frequency domains. These are selected based on the analysts' experience. In the latter step, a process of trial an error was carried out to find the best neural network architecture. Classification results on a data set of 343 seismic signals have demonstrated that the accuracy of the proposed classier can achieve more than 94%.

References

  • M. Hedlin, J. B. Minster, J. A. Orcutt. An automatic means to discriminate between earthquakes and quarry blasts. Bull. Seism. Soc. Am. 80, 2143–2160, 1990.
  • Y. Gitterman and A. Shapira. Spectral discrimination of underwater explosions. Israel Journal of Earth Science 42, 37–44, 1993.
  • Y. Gitterman, V. Pinky and A. Shapira. Spectral classification methods in monitoring small local events by the Israel seismic network. J. Seismology 2, 237–256, 1998.
  • K. Koch and D. Fah. Identification of earthquakes and explosions using amplitude ratios: theVogtland area revisited. Pure and Applied Geophysics 159,735–757, 2002.
  • P. B. Allmann, P. M. Shearer and E. Hauksson. Spectral discrimination between quarry blasts and earthquakes in Southern California. Bull. Seism. Soc. Am. , Vol: 98, Issue: 4, Pages: 2073-2079, 2008.
  • S. A. Dahy and G. H. Hassib. Spectral discrimination between quarry blasts and micro earthquakes in southern Egypt. Research Journal of Earth Sciences 2 (1), 1–7, 2010.
  • 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.
  • J. Wuster. Discrimination of chemical explosions and earthquakes in central Europe—a case study. Bull. Seism. Soc. Am. 83, 1184–121, 1993.
  • A. F. Kushnir, E. V. Troitsky, L. M. Haikin, and A. Dainty. Statistical classification approach to discrimination between weak earthquakes and quarry blasts recorded by the Israel Seismic Network, Phys. Earth Planet. Int. 113, 161–182, 1999.
  • D. B. Harris. A waveform correlation method for identifying quarry explosions. Bull. Seism. Soc. Am. 81, 2395–2418, 1991.
  • P. Gendron, J. Ebel, and D. Manolakis (2000). Rapid joint detection and classification with wavelet bases via bayes Theorem, Bull. Seism. Soc. Am. 90, 764–774.
  • S. Falsaperla, S. Graziani, G. Nunnari, S. Spampinato. Automatic classification of volcanic earthquakes by using multi-layered neural networks. Natural Hazards 13, 205–228, 1996.
  • M. Musil and A. Plesinger. Discrimination between local micro earthquakes and quarry blasts by multi-layer perceptrons and Kohonen maps. Bull. Seism. Soc. Am. 86 (4), 1077–1090, 1996.
  • S. Muller, P. Garda, J. D Muller and Y. Cansi. Seismic events discrimination by neuro-fuzzy catalogue features. Physics and Chemistry of the Earth 24 (3), 201–206, 1999.
  • F. U. Dowla, S. R. Taylor and R. W Anderson. Seismic discrimination with artificial neural networks: preliminary results with regional spectral data. Bull. Seism. Soc. Am. 80, 1346–1373, 1990.
  • T. Tiira. Detecting teleseismic events using artificial neural networks. Computers and Geosciences 25, 929–939, 1999.
  • R. D. Jenkins and T. S. Sereno. Calibration of regional S/P amplitude-ratio discriminants. Pure and Applied Geophysics 158, 1279–1300, 2001.
  • A. Ursino, H. Langer, L. Scarf?, G. Di Grazia and S. Gresta. Discrimination of quarry blasts from tectonic microearthquakes in the Hyblean Plateau (south- eastern Sicily). Annali di Geofisica 44, 703–722, 2001.
  • E. Del Pezzo, A. Esposito, F. Giudicepietro, M. Marinaro, M. Martini, S. Scarpetta. Discrimination of earthquakes and underwater explosions using neural networks. Bull. Seism. Soc. Am. 93, 215–223, 2003.
  • S. Scarpetta, F. Giudicepietro, E. C. Ezin, S. Petrosino, E. Del Pezzo, M. Martini, M. Marinaro. Automatic classification of seismic signals at Mt. Vesuvius volcano, Italy, using neural networks. Bull. Seism. Soc. Am. 95, 185–196, 2005.
  • E. Y?ld?r?m, A. Gulbag, G. Horasan and E. Dogan: Discrimination of quarry blasts and earthquakes in the vicinity of Istanbul using soft computing techniques. Computers & Geosciences, 01-09, 2010.
  • E. Akhouayri, D. Agliz, M. Fadel, and A. Ait Ouahman. Automatic detection and indexation of seismic events. AMSE Periodicals, Advances in Modeling and Analysis, S. C, Vol. 56, 59-67, 2001.
  • M. BishopChristopher, Neural networks for Pattern recognition. Oxford University, New York, 1995. 116-161pp
  • K. Hornick, 1991, Approximation capabilities of multilayer feedforward neural network. Neural network, 4(2), pp. 251-257
  • B. Zhang and H. muhlenbein. Evolving optimal neural networks using genetic algorithms with occam's Razor. Complex system 7(1993) 199-220
  • V. K. Devabhaktuni, M. Yagoub, and Q. J. Zhang, "A robust algorithm for automatic development of neural-network models for microwave applications," IEEE Trans. Microwave Theory Tech. , vol. 49, pp.
  • T. Y. Kwok and D. Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems," IEEE Trans. Neural Networks, vol. 8, pp. 630–645, May 1997
  • Z. Reitermanova, Data Splitting. WDS'10 Proceedings of contributed papers, part I, 31-36, 2010
  • T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning Data Mining, Inference, and Prediction. Springer Series in Statistics, second edition, 2008.