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Abstrakty
In this study we concentrate on neural networks in the form of algorithms, and on their property to learn by examples. The possibility of learning by examples in-cludes also an enormous ability of information extraction. As the input data we have used the regional seismograms which record the ground particle velocities. Application of the networks for evaluation of the two seismic parameters has been demonstrated: first to calculate the magnitude (or seismic moment) of the seismic event directly from the velocity seismograms, without prior converting them to the displacement seismograms, and second, to locate seismic source by using only a single seismogram. Both applications are very convenient due to their speed of cal-culation and the lack of spectral methods - in the first case, and the good precision despite the unusual approach in the second case. To prepare the input data for the neural networks a preprocessing has been applied, which can be described as filter-ing of a seismogram by the filters bank.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
143--153
Opis fizyczny
Bibligor. 10 poz.
Twórcy
autor
- Institute of Geophysics, Polish Academy of Sciences, ul. Księcia Janusza 64, 01-452 Warszawa
Bibliografia
- 1. Brune, J.N., 1970, Tectonic stress and the spectra of seismic shear waves from earthquakes, J. Geophys. Res. 72,4997-5009.
- 2. Brune J.N., 1971, Correction, J. Geophys. Res. 76, 5002.
- 3. Hanks, T.C., and H. Kanamori, 1979, A moment magnitude scale, J. Geophys. Res. 84, 2348-2350.
- 4. Joswig, M., 1990, Pattern recognition for earthquake detection, Bull. Seism. Soc. Am. 80, 170-186.
- 5. Kung, S.Y., 1993, Digital Neural Networks, Prentice Hall, Englewood Cliffs, N. J.
- 6. Niewiadomski, J., 2002, Magnitude and neural networks. In: H. Ogasavara, T. Yanagidani and M. Ando (eds.), Seismogenic Process Monitoring", Balkema Publisher, Tokio, 355-363.
- 7. Park, J., C.R. Lindberg and F.L. Vernon III, 1987, Multitaper spectral analysis of high frequency seismograms, J. Geophys. Res. 92, 12664-12674.
- 8. Simpson, P.K., 1990, Artificial Neural Systems: Foundations, Paradigms, Applications and Implementations, Pergamon Press, New York.
- 9. Thompson, D.J., 1982, Spectral estimation and harmonic analysis, IEEE Proc. 70, 1055-1096.
- 10. Wickerkerhauser, M.V. 1998, Adaptive Wavelet Analysis from Theory to Software, IEEE Press, New York.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSL7-0008-0002