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采用人工识别方法,依托微震监测系统,建立矿山爆破与微震事件样本数据库。统计分析数据库内各事件地震力矩、事件总能量、事件P波能量与S波能量比、事件的静压力降、事件的发生时间、传感器触发数量和拐角频率等震源参数特征;对比分析首次峰值到时、首次峰值幅值、最大峰值到时及最大峰值幅值的概率密度分布特征;通过FFT变换,统计分析2类事件信号的主频分布规律。依据各参数的概率密度分布及其识别效果,结合特征参数获取的难易程度,最终选取事件的地震力矩对数,事件的能量对数,事件的传感器触发数量,首次峰值幅值对数及最大峰值到时对数和信号的主频为特征参数,建立矿山微震与爆破事件自动识别的统计学模型。该模型对样本的回检结果显示,50组建模样本的准确率为100%,50组测试样本的准确率为94%。将该模型应用于采场大块矿石的二次破碎事件识别中,识别结果与实际相符,解决了单纯依靠信号特征识别导致该类事件极易与微震事件混淆的问题。该方法误判率低,特征参数较易获取,是矿山微震与爆破事件识别的一种有效方法,可在实际工程中推广应用。
Using artificial identification method and relying on microseismic monitoring system, a sample database of mine blasting and microseismic events is established. Statistical analysis of seismic events, total event energy, incident P-wave energy and S-wave energy ratio, event hydrostatic pressure drop, event occurrence time, sensor triggering number and corner frequency were all statistically analyzed. The first peak to The first peak amplitude, the maximum peak to peak and the maximum peak amplitude probability density distribution; through FFT transform, statistical analysis of two kinds of event signal frequency distribution. Based on the distribution of probability density of each parameter and its recognition effect, combining with the difficulty of acquiring characteristic parameters, the logarithm of earthquake moment, the logarithm of event torque, the logarithm of event energy, the sensor triggering number of event, the logarithm of first peak amplitude and the largest The peak-to-logarithm and the dominant frequency of the signal are the characteristic parameters, and a statistical model of the mine microseismic and explosive event automatic identification is established. The test results of the model show that the accuracy of the 50 sets of modeling samples is 100% and the accuracy of the 50 sets of testing samples is 94%. The model is applied to the identification of secondary crushing events in large ores of stope. The recognition results are in accordance with the actual ones. The problem of easily confusing the microseismic events with such events can be solved by simply relying on signal feature recognition. The method has low false-positive rate and easy to obtain characteristic parameters, which is an effective method to identify microseismic and blasting events in mines. It can be widely applied in practical engineering.