Abstract:

Generally, a cluster of seismic events which share similar source locations and focal mechanisms will show similar waveforms on the record. Based on this assumption, a method have been developed for microseismic event detection and arrival picking based on waveform cross-correlation. This method achieves moveout correction for the seismic records based on cross-correlation functions, then calculates a multi-channel semblance coefficient to identify the microseismic events. Meanwhile, the seismic records after moveout correction are superposed. The STA/LTA method is adopt to pick the arrivals for the stacked traces, the arrival times of the microseismic events are then automatically obtained. The performance of the method is evaluated using both synthetic and real datasets. Analysis of the results demonstrates that the proposed method can not only detect the microseismic events, but also obtain relatively accurate arrival picks at the same time.

Key words: microseismic, event detection, arrival picking, cross-correlation function, moveout correction

摘要：

根据具有相似震源位置及破裂机理的微地震事件通常在地震记录上表现出相似波形特征的原理, 提出一种基于波形互相关的微地震事件自动识别及初至拾取方法。首先, 通过计算道间互相关函数, 对微地震记录进行时差校正; 然后, 通过计算道间相似系数的方法识别连续地震记录中的微地震事件。在完成事件识别的同时, 对时差校正后的多道记录进行叠加, 并采用STA/LTA方法对叠加道进行初至拾取, 结合各道间的时差信息即可获得各道微地震事件的初至到时。为了检验所提方法的可行性和有效性, 分别对模型数据和实际资料进行处理,结果表明利用所提方法可以在有效地识别微地震事件的同时, 得到较为准确的初至拾取结果。

关键词: 微地震, 事件识别, 初至拾取, 互相关函数, 时差校正