中文摘要：

为提高工程噪音环境中低信噪比微震信号的自动识别率及其P波自动拾取准确率,结合Allen算法能快速自动拾取震动信号的优点及Bear算法善于拾取低信噪比震动信号P波初至的优势,在Allen算法的基础上,引入Bear算法的加权因子和特征函数,对Allen算法进行改进,提出适用于工程尺度的微震信号及P波初至自动识别的AB（Allen coupled with Bear algorithm）算法。分析AB算法对信号振幅或频率变化的敏感性以及拾取效果,结果表明：（1）AB算法能准确识别微震信号也能同时准确自动拾取信号的P波初至;（2）AB算法的加权因子K、特征函数CF,ε值对频率和振幅变化的敏感性高于Allen算法;（3）AB算法对振幅变化比对频率变化敏感;（4）工程尺度下AB算法微震信号的拾取率高于Allen算法,且P波自动拾取准确率也高于Allen算法。将AB算法用于分析锦屏深部地下实验室实测微震信号：对于弱信号,基于AB算法拾取结果进行微震源定位,定位结果具有更高的可靠性与稳定性;AB算法是一种行之有效,计算简单,适合实时监测微震信号识别及其P波初至拾取。

英文摘要：

To improve the recognition rate of microseismic signal with low SNR and the pickup accuracy of P wave in the engineering noise environment, Allen algorithm which can pick up microseismic signal automatically and quickly and Bear algorithm which is good at picking up the microseismic signal with low SNR at the beginning of the P wave were combined to form an AB algorithm with the introduction of Bear weighted factor and characteristic function on the basis of Allen algorithm. The AB algorithm can identify the microseismic signals accurately and accurately pick up the changed P wave automatically. The weighting factor K, characteristic function CF and e value of AB algorithm have higher sensitivity to the changes of frequency and amplitude than Allen algorithm. The AB algorithm is more susceptive to the change of amplitude than frequency. The pickup rate of the seismic signal and the pickup accuracy of the automatic P wave in the AB algorithm are better than the Allen algorithm at the project scales. The analysis of the microseismic signal from the deep underground laboratory at Jinping shows that the positioning results of the microseismic sources exhibit higher reliability and stability based on the AB algorithm for the weak signal. The AB algorithm is confirmed to be effective, simple and suitable for the real time monitoring of microseismic signal and the pickup of first arrival of P wave.