中文摘要：

拾取微地震信号到时对事件定位研究至关重要,传统方法直接拾取所有采集信号到时后,再通过人工手动判别出微地震事件,工作量大且效率低。针对这一问题,提出了一种自动识别有效微震信号方法——能量极值法（Energy Extreme Value,EEV）。通过移动时窗计算信号能量比Ratio变化曲线,分析不同信号的区别,提出在Ratio变化曲线上寻找与右侧点之间的偏差大于临界值Diff的特征极值点作为判别条件,研究分析了该方法的主要影响因素为移动时窗长度M和临界值Diff,并优化确定了最佳参数。采用MATLAB对冬瓜山铜矿采集的实际信号数据进行分析处理,结果表明：该算法能够精确识别噪声和微地震信号,与人工手动判别结果对比,准确率达96%以上,极大地缩短了数据处理时间,提高了工作效率,对微震信号处理具有重要的指导意义。

英文摘要：

Microseismic events arrival-pick is important for events location and other research analysis.Traditional method picked all collected signals and recognized the microseismic events manually,was not only heavy workload,but also low efficiency.This paper proposed an automatic method of recognizing the efficient microseismic signals,EEV（Energy Extreme Value）.Calculated the ratio of the energy value between front and rear window through the specified moving time window,analyzed the different signals characteristics,put forward a method,through finding extreme value point,which the deviation between the right point is greater than the threshold diff,as the discrimination standard.Meanwhile,studied the main influence factors of this method,namely,the length of time window M,and the threshold diff,optimized and determined the optimal parameters.Using MATLAB Software to process the real datas of Dongguashan copper mine,the results show that the algorithm can accurately recognize nosie and seismic signal,the accuracy rate is up to 96%,compared with manual results,greatly shortens the time of data processing and improves the work efficiency,meanwhile,provide an important guidance for seismic signal process.