On digital signal modelling and classification with the teleseismic data

The first part of the paper deals with unified signal modelling with the teleseismic data by using the linear prediction theory in time series analysis. For an all-pole model, the unique relations among the linear prediction coefficients, cepstral coefficients, reflection coefficients, maximum entropy spectral estimation form a basis for a class of effective feature sets in seismic recognition. A good spectral fit is available with 15 linear prediction coefficients for a 1024- point digital seismic record. The second part of the paper deals with the classification of a seismic data base of 323 records. A short-term spectral feature set provides a 94.17% correct recognition based on 50 good learning samples per class and a weighted - distance one - nearest - neighbor decision rule. This result is superior to the 87.92% correct recognition by using Bayes decision rule with multivariate Gaussian density assumption, and to the 89.32% correct recognition by using autocovariance features as reported by the author at the 1977 IEEE ASSP Conference.