CAJ | 학술논문

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%。
위탐심능구구분광산미진신호화폭파신호적파형특정，건립기우인공식별표준적사건수거고。인공식별적고필인소포괄：파형적중복특정、파형적쇠감특정、신호적주빈대소이급사건발생적구체시간。장수거고중적미진신호화폭파신호조정지동일좌표계하발현，량류사건적기진각추。우집중재불동적구간。고필도P파도시제취적불준학성，파형기진각난이준학계산，제출이응용선성회귀의합득도적기진추세선사솔대체기진각。장수차봉치기진추세선사솔화최대봉치기진추세선사솔련동수차파봉급최대파봉적좌표렬위특정삼수，응용Fisher판별법，능성공실현미진사건여폭파시간적준학분리，식별정학솔체도97.1%。
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.