田优平, 赵爱华. 2016: 基于小波包和峰度赤池信息量准则的P波震相自动识别方法. 地震学报, 38(1): 71-85. DOI: 10.11939/jass.2016.01.007
引用本文: 田优平, 赵爱华. 2016: 基于小波包和峰度赤池信息量准则的P波震相自动识别方法. 地震学报, 38(1): 71-85. DOI: 10.11939/jass.2016.01.007
Tian Youping, Zhao Aihua. 2016: Automatic identification of P-phase based on wavelet packet and Kurtosis-AIC method. Acta Seismologica Sinica, 38(1): 71-85. DOI: 10.11939/jass.2016.01.007
Citation: Tian Youping, Zhao Aihua. 2016: Automatic identification of P-phase based on wavelet packet and Kurtosis-AIC method. Acta Seismologica Sinica, 38(1): 71-85. DOI: 10.11939/jass.2016.01.007

基于小波包和峰度赤池信息量准则的P波震相自动识别方法

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

  • 摘要: 基于小波包变换和峰度赤池信息量准则(AIC), 提出了一种新的自动识别P波震相的综合方法, 即小波包-峰度AIC方法. 首先对由加权长短时窗平均比(STA/LTA)法粗略确定的P波到时前后3 s的记录进行小波包三尺度的分解与重构, 分别计算每个尺度重构信号的峰度AIC曲线并将其叠加, 叠加曲线的最小值则为P波震相到时; 然后对原始地震记录进行有限冲激响应自适应滤波以提高信噪比和识别精度; 最后将小波包-峰度AIC方法应用到合成理论地震图及实际地震记录的P波初至自动识别中. 结果表明: 初至清晰度对识别精度的影响比信噪比对其影响更大; 与单独使用加权STA/LTA方法和峰度AIC法相比, 小波包-峰度AIC法具有更强的抗噪能力, 识别精度更高; 当初至清晰时, 小波包-峰度AIC法自动识别与人工识别的P波到时平均绝对差值为(0.077±0.075) s.

     

    Abstract: 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.

     

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