Abstract
Two multilayer perceptron (MLP) methodologies are developed to tackle the problems of earthquake seismic arrival picking and identification. The arrival picking is achieved using the vector modulus of three-component (3-C) seismic recording or the absolute value of amplitude of single-component recording, as the input to an MLP. A discriminate function, determined from the output of the trained MLP, is then employed to define the arrival onset. Arrival identification is achieved using the degree of polarization of the arrival segment of the 3-C recordings as the input to an MLP; the output of the trained MLP indicates the arrival type. The results are encouraging. This work demonstrates significant potential in the application of MLPs to automatic earthquake analysis and other geophysical problems. In principle, it should be possible to develop a complete automatic seismic analysis system based on the two methodologies described herein.
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References
Anderson, K. R, 1978, Automatic processing of local earthquake data: PhD thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Basham, P. W., and Ellis, R. M., 1969, The composition of Pcode using magnetic tape seismograms: Bull., Seis. Soc. Am., 59, 473–486.
Cichowicz, A., 1993, An automatic S-phase picker: Bull., Seis. Soc. Am., 83, 180–189.
Dai, H. C., 1995, Analysis of local earthquake data using artificial neural networks: Ph.D. thesis, Univ. of Edinburgh, Scotland.
Dai, H. C., and MacBeth, C., 1995, Automatic picking of seismic arrivals in local earthquake data using an artificial neural network: Geophys. J. Internat., 120, 758–774.
Dai, H. C., and MacBeth, C., 1997a, The application of back-propagation neural network to automatic picking seismic arrivals from single-component recordings: J. Geophys. Res., 102, 15105–15113.
Dai, H. C., and MacBeth, C., 1997b, Application of back-propagation neural network to identification of seismic arrival types: Phys. Earth Plan. Int., 101, 177–188.
Fausett, L. V., 1994, Fundamentals of neural networks: architectures, algorithms and applications: Prentice-Hall, Inc. Haykin, S., 1994, Neural networks, a comprehensive foundation: Macmillan College Publ. Co.
Lomax, A J., and Michelin, A, 1988, The use of spherical coordinates in the interpretation of seismograms: Geophys. J. Internat, 93, 405–412.
Lovell, J. H.,1989, Source parameters of a microearthquake swann in Turkey: M.Sc. thesis, Univ. of Edinburgh, Scotland. Rumelhart, D., Hinton, G. E., and Williams, R J., 1986, Learning representations by back-propagating errors: Nature, 323, 533–536.
Samson, J. C., 1977, Matrix and Stokes vector representations of detectors for polarized waveforms: theory, with some applications to teleseismic waves: Geophys. J. Roy. Astr. Soc., 51, 433–452.
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Dai, H. (2003). Application of Multilayer Perceptrons to Earthquake Seismic Analysis. In: Sandham, W.A., Leggett, M. (eds) Geophysical Applications of Artificial Neural Networks and Fuzzy Logic. Modern Approaches in Geophysics, vol 21. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0271-3_18
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DOI: https://doi.org/10.1007/978-94-017-0271-3_18
Publisher Name: Springer, Dordrecht
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