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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。运用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。
When microseismic signal is picked up, it is very important to study the location of the event. The traditional method directly picks up all the collected signals and then identifies the microseismic events manually, which results in heavy workload and low efficiency. In response to this problem, a method of automatically identifying effective microseismic signals-Energy Extreme Value (EEV) is proposed. By comparing the curve of signal-energy ratio Ratio with moving time window, the difference between different signals is analyzed, and the feature extreme point that the deviation between the right and the right points is larger than the critical value Diff is proposed as the discrimination condition. The main influencing factors of the method are the length of moving window M and the critical value Diff, and the optimal parameters are optimized. The actual signal data collected by Dongguashan copper mine are analyzed and processed by MATLAB. The results show that the algorithm can accurately identify the noise and microseismic signals, with the accuracy of more than 96% compared with manual manual discrimination, which greatly shortens the data processing Time, improve work efficiency, and have important guiding significance for microseismic signal processing.