基于波形起振特征的矿山微震与爆破信号模式识别

摘要

为探寻能够区分矿山微震信号和爆破信号的波形特征，建立基于人工识别标准的事件数据库。人工识别的考虑因素包括：波形的重复特征、波形的衰减特征、信号的主频大小以及事件发生的具体时间。将数据库中的微震信号和爆破信号调整至同一坐标系下发现，两类事件的起振角趋。于集中在不同的区间。考虑到P波到时提取的不准确性，波形起振角难以准确计算，提出以应用线性回归拟合得到的起振趋势线斜率代替起振角。将首次峰值起振趋势线斜率和最大峰值起振趋势线斜率连同首次波峰及最大波峰的坐标列为特征参数，应用Fisher判别法，能成功实现微震事件与爆破时间的准确分离，识别正确率达到97.1%。%To find discriminating features in seismograms for the classification of mine seismic events, signal databases of blasts and microseismic events were established based on manual identification. Criteria including the repetition of waveforms, tail decreasing, dominant frequency and occurrence time of day were considered in the establishment of the databases. Signals from databases of different types were drawn into a unified coordinate system. It is noticed that the starting-up angles of the two types tend to be concentrated into two different intervals. However, it is difficult to calculate the starting-up angle directly due to the inaccuracy of the P-wave arrival’s picking. The slope value of the starting-up trend line, which was obtained by linear regression, was proposed to substitute the angle. Two slope values associated with the coordinates of the first peak and the maximum peak were extracted as the characteristic parameters. A statistical model with correct discrimination rate of greater than 97.1%was established by applying the Fisher discriminant analysis.