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提出一种基于直达P波信号和其它背景噪声在能量、非高斯性、非线性和偏振特性的不同而进行区域地震事件实时检测的新方法信噪综合差异特征量方法(简写为EFGLP方法),同时对比分析了应用信号的不同统计特性来精细识别震相初至的3种有效方法,其中的TOC-AIC方法是新提出的.应用山东数字地震波资料处理的结果表明:①与常规的STA/LTA地震事件触发算法相比,EFGLP方法能够有效降低地震事件的错误报警率和漏报率;②与人机交互震相识别结果相比,当信噪比比较低、震相初至比较模糊时,3种震相精细识别方法中的TOC-AIC方法识别精度最高;当信噪比比较高、震相初至比较清晰时,基于VAR-AIC和TOC-AIC方法所测量得到的震相初至识别基本一致.
A new method of signal-noise integrated feature difference (EFGLP method) is proposed for the real-time detection of regional seismic events based on the direct P wave signals and other background noises under different energy, non-Gaussian, nonlinear and polarization characteristics. At the same time, three effective methods, which are used to identify the first arrival of seismic facies, are comparatively analyzed, and the TOC-AIC method is newly proposed.The results of data processing using Shandong digital seismic data show that: ① Compared with the conventional STA / Compared with LTA earthquake event trigger algorithm, EFGLP method can effectively reduce the false alarm rate and false negative rate of earthquake events; ② Compared with the results of human-computer interaction phase identification, when the signal to noise ratio is relatively low, , The accuracy of the TOC-AIC method is the highest among the three methods of fine phase identification. When the signal to noise ratio is relatively high and the phase of the first phase is clearer, the initial phase to the first phase of the phase sequence based on the VAR-AIC and TOC-AIC methods Recognition is basically the same.