曲保安, 刘希强, 蔡寅, 范晓勇, 林秀娜, 于庆民, 赵瑞, 李铂, 周彦文. 2014: 近震S波震相实时自动识别方法研究. 地震学报, 36(2): 200-208. DOI: 10.3969/j.issn.0253-3782.2014.02.005
引用本文: 曲保安, 刘希强, 蔡寅, 范晓勇, 林秀娜, 于庆民, 赵瑞, 李铂, 周彦文. 2014: 近震S波震相实时自动识别方法研究. 地震学报, 36(2): 200-208. DOI: 10.3969/j.issn.0253-3782.2014.02.005
Qu Baoan, Liu Xiqiang, Cai Yin, Fan Xiaoyong, Lin Xiuna, Yu Qingmin, Zhao Rui, Li Bo, Zhou Yanwen. 2014: Method for real-time automatic identification of S-phase: Application to local seismicity. Acta Seismologica Sinica, 36(2): 200-208. DOI: 10.3969/j.issn.0253-3782.2014.02.005
Citation: Qu Baoan, Liu Xiqiang, Cai Yin, Fan Xiaoyong, Lin Xiuna, Yu Qingmin, Zhao Rui, Li Bo, Zhou Yanwen. 2014: Method for real-time automatic identification of S-phase: Application to local seismicity. Acta Seismologica Sinica, 36(2): 200-208. DOI: 10.3969/j.issn.0253-3782.2014.02.005

近震S波震相实时自动识别方法研究

Method for real-time automatic identification of S-phase: Application to local seismicity

  • 摘要: 提出了一种用于地震早期预警的S波震相实时自动识别方法. 该方法不对原始信号进行任何滤波处理, 直接对三分向记录进行计算分析. 首先根据P波前0.5 s数据的卓越频率计算适用于该三分向记录的窗长, 采用由偏斜角和水平能量与总能量比值的平方积作为确定S波识别区间的特征函数, 将特征函数已有数据的5倍均值和5倍方差之和作为识别区间的触发阈值; 然后采用VAR-AIC方法对两个水平分向识别区间的数据分别计算分析, 对两个识别结果进行判断, 最终确定S波初动时刻. 经过对118个三分向记录的实际应用验证, 通过自动识别结果与人机交互震相识别结果相比, 本文方法对于S波相对P波尾波信噪比大于5 dB的地震记录, 其识别误差小于0.1 s的概率高达89.39%.

     

    Abstract: This paper proposes a real-time automatic method for S-phase identification in earthquake early warning. The proposed method directly analyzes three-component data without any filtering processing to the original signals. Firstly, the proper window length to identify the seismic phase is determined based on the predominant frequency of the first 0.5 s data prior to the P phase. Secondly, a characteristic function is applied to estimate the identification interval of S-wave. The function is defined as the product of the square of deflection angle and the square of ratio of horizontal seismic wave energy to total seismic wave energy. Trigger threshold of the characteristic function is taken as the sum of 5 times the average of the calculated characteristic function values and 5 times the variance of the calculated characteristic function values. Then VAR-AIC (variance-Akaike information criterion) method is applied to the two horizontal component data in the identification interval. The first break time of S-phase is determined after judgments on the two identification results. We apply the picking method to 118 three-component earthquake records with various magnitudes and compare the results from the automatic identification method and the interactive phase picking method. The results show that 89.39% of all tests have identification time errors less than 0.1 s from the proposed method for seismic recordings with signal to noise ratio of S waves to P wave coda higher than 5 dB.

     

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