A Dynamic Model for the Seismic Signals Processing and Application in Seismic Prediction and Discrimination

Payam NASSERY  Karim FAEZ  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E83-D   No.12   pp.2098-2106
Publication Date: 2000/12/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
Keyword: 
seismic wave,  short period recording,  modeling,  probability functions,  clustering,  

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Summary: 
In this paper we have presented a new method for seismic signal analysis, based on the ARMA modeling and a fuzzy LVQ clustering method. The objective achieved in this work is to sense the changes made naturally or artificially on the seismogram signal, and to detect the sources, which caused these changes (seismic classification). During the study, we have also found out that the model is sometimes capable to alarm the further seismic events just a little time before the onset of those events (seismic prediction). So the application of the proposed method both in seismic classification and seismic prediction are studied through the experimental results. The study is based on the background noise of the teleseismic short period recordings. The ARMA model coefficients are derived for the consecutive overlapped windows. A base model is then generated by clustering the calculated model parameters, using the fuzzy LVQ method proposed by Nassery & Faez in [19]. The time windows, which do not take part in [19] model generation process, are named as the test windows. The model coefficients of the test windows are then compared to the base model coefficients through some pre-defined composition rules. The result of this comparison is a normalized value generated as a measure of similarity. The set of the consecutive similarity measures generate above, produce a curve versus the time windows indices called as the characteristic curves. The numerical results have shown that the characteristic curves often contain much vital seismological information and can be used for source classification and prediction purposes.