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Инд. авторы: Zamaraev R.Y., Popov S.E., Logov A.B.
Заглавие: The algorithm for classifying seismic events based on the entropy mapping of signals
Библ. ссылка: Zamaraev R.Y., Popov S.E., Logov A.B. The algorithm for classifying seismic events based on the entropy mapping of signals // Izvestiya. Physics of the Solid Earth. - 2016. - Vol.52. - Iss. 3. - P.364-370. - ISSN 1069-3513. - EISSN 1531-8451.
Внешние системы: DOI: 10.1134/S1069351316030137; РИНЦ: 27158054; SCOPUS: 2-s2.0-84971238418; WoS: 000376615000004;
Реферат: eng: The original algorithm for classifying seismic signals is presented. The suggested approach is novel by the preliminary entropy type transformations which enable generalization of the information about the peculiarities of the waveforms of seismic signal components. The shapes of the characteristic functions obtained in the method are used for estimating the mutual similarity of the known and unknown selected events.
Ключевые слова: DISCRIMINATION; AUTOMATIC CLASSIFICATION; ARTIFICIAL NEURAL-NETWORKS;
Издано: 2016
Физ. характеристика: с.364-370
Цитирование:
1. Benbrahim, M., Daoudi, A., Benjelloun, K., and Ibenbrahim, A., Discrimination of seismic signals using artificial neural networks, Proc. World Acad. Sci., Eng. Technol., 2005, vol. 4, pp. 4–7.
2. Diersen, S., Lee, E.-J., Spears, D., Chen, P., and Wang, L., Classification of seismic windows using artificial neural networks, Procedia Comp. Sci., 2011, vol. 4, pp. 1572–1581.
3. Hamer, R.M. and Cunningham, J.W., Cluster analyzing profile data confounded with interrater differences: a comparison of profile association measures, Appl. Psychol. Meas., 1981, vol. 5, pp. 63–72.
4. Kedrov, E.O. and Kedrov, O.K., Spectral time method of identification of seismic events at distances of 15°–40°, Izv., Phys. Solid Earth, 2006, vol. 42, no. 5, pp. 398–415.
5. Kurenkov, N.I. and Anan’ev, S.N., The entropy approach to solving the problems of multidimensional data classification, Inf. Tekhnol., 2006, no. 8, pp. 44–47.
6. Langer, H., Falsaperla, S., Powell, T., and Thompson, G., Automatic classification and a-posteriori analysis of seismic event identification at Soufriére Hills volcano, Montserrat, J. Volcanol. Geotherm. Res., 2006, vol. 15, no. 1, pp. 1–10.
7. Logov, A.B. and Zamaraev, R.Yu., Matematicheskie modeli diagnostiki unikal’nykh obektov (Mathematical Models of Diagnosing the Unique Objects), Novosibirsk: SO RAN, 1999.
8. Logov, A.B., Zamaraev, R.Yu., and Logov, A.A., Analysis of the state of the systems of unique objects, Vychisl. Tekhnol., 2005, vol. 10, no. 5, pp. 49–53.
9. Lyubushin, A.A., Kaláb, Z., and Astová, N., Application of wavelet analysis to the automatic classification of three-component seismic records, Izv., Phys. Solid Earth, 2004, vol. 40, no. 7, pp. 587–593.
10. Musil, M. and Pleginger, A., Discrimination between local microearthquakes and quarry blasts by multi-layer perceptrons and Kohonen maps, Bull. Seismol. Soc. Am., 1996, vol. 86, no. 4, pp. 1077–1090.
11. Ryzhikov, G.A., Biryulina, M.S., and Husebye, E.S., A novel approach to automatic monitoring of regional seismic events, IRIS Newslett., 1996, vol. 15, no. 1, pp. 12–14.
12. Scarpetta, S., Giudicepietro, F., Ezin, E.C., Petrosino, S., Del Pezzo, E., Martini, M., and Marinaro, M., Automatic classification of seismic signals at Mt. Vesuvius volcano, Italy, using neural networks, Bull. Seismol. Soc. Am., 2005, vol. 95, no. 1, pp. 185–196.
13. Shimshoni, Y. and Intrator, N., Classification of seismic signals by integrating ensembles of neural networks, IEEE Trans. Signal Process., 1998, vol. 46, no. 5, pp. 1194–1201.