Automatic identification of P-phase based on wavelet packet and Kurtosis-AIC method

Automatic identification of P-phase is of significance to the study on earthquake location, earthquake warning and structure of deep earth. Combining wavelet packet transform with Kurtosis-AIC (Akaike information criterion) technology, this paper puts forward a new synthetic method named wavelet packet and Kurtosis-AIC method for automatic recognition of first P-phase. Three scales of discrete wavelet packet transforms are applied to decompose and reconstructure the original recordings three seconds before and after the rough P-wave arrival time, which is picked up by weighted STA/LTA (short term average/long term average) method, then the Kurtosis-AIC values of the three-scale reconstruction signal are calculated respectively and superposed together, finally the minimum value of the superposed AIC curve is taken as the first P-wave arrival time. In order to test the new method, it is applied to theoretically synthetic seismograms and real seismic recording for automatic P-phase arrival time detection. Adding white Gaussian noise and real seismic noise to synthetic seismograms with different SNR, the optimal frequency band of adaptive FIR (finite impulse response) digital filtering is used to improve the SNR and P-wave recognition accuracy of the original signals. The results show that, with respect to the impact of SNR, the accuracy of P-wave identification is more affected by the clarity of first break; our method has greater noise immunity and higher P-wave recognition accuracy as compared to the weighted STA/LTA algorithm and Kurtosis-AIC method. When the first break of P-wave is clear, average absolute error of P-phase arrival time between automatic identification based on our method and manual identification is (0.077±0.075) seconds.