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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。
In order to improve the automatic recognition rate of low signal-to-noise microseismic signal and its P-wave automatic pick-up accuracy in engineering noise environment, combining with Allen’s algorithm, it can quickly and automatically pick up the vibration signal and Bear algorithm. On the basis of Allen’s algorithm, this paper introduces the weighting factor and eigenfunction of Bear algorithm to improve Allen’s algorithm, and proposes the micro-seismic signal suitable for engineering scale and AB (Allen coupled with Bear algorithm) )algorithm. The results show that: (1) The AB algorithm can accurately identify the microseismic signals and accurately pick up the first arrival of the P wave of the signal at the same time; (2) the weighting factor of the AB algorithm K, and the eigenfunctions CF and ε are more sensitive to the change of frequency and amplitude than Allen algorithm. (3) The AB algorithm is sensitive to the change of amplitude compared with the frequency. (4) The pick-up rate of ABA Allen algorithm, and P wave automatic pick-up accuracy is also higher than the Allen algorithm. The AB algorithm is used to analyze microseismic signals measured in the deep underground laboratory of Jinping: For the weak signal, the microseismic source localization is based on the pick-up result of the AB algorithm, and the positioning result has higher reliability and stability. The AB algorithm is effective , The calculation is simple, suitable for real-time monitoring of microseismic signal recognition and P wave to the first pick.