微震与爆破事件统计识别方法及工程应用

发布时间：2018-05-17 15:38

本文选题：采矿工程 + 震源参数　；参考：《岩石力学与工程学报》2016年07期

【摘要】：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。
[Abstract]:Based on the microseismic monitoring system, a mine blasting and microseismic event sample database was established by artificial identification method. The seismic moment, the total energy of events, the ratio of P wave energy to S wave energy, the static pressure drop of events, the occurrence time of events, the number of sensor trigger and the frequency of corner are analyzed statistically. The probability density distribution characteristics of the first peak amplitude, the maximum peak arrival and the maximum peak amplitude are compared and analyzed, and the main frequency distribution of the two kinds of event signals is statistically analyzed by FFT transform. According to the probability density distribution of each parameter and its recognition effect, combined with the difficulty degree of obtaining characteristic parameter, the logarithm of seismic moment, the energy logarithm of event, the number of sensor trigger of event are selected. The logarithm of the first peak amplitude and the maximum peak arrival logarithm and the main frequency of the signal are characteristic parameters. A statistical model for automatic identification of mine microearthquakes and blasting events is established. The results of the model show that the accuracy of the model is 100 and the accuracy of the 50 groups of samples is 94. The model is applied to the recognition of secondary crushing events of large ore in stope. The recognition results are in agreement with the actual situation, and the problem that this kind of event is easily confused with microseismic events caused by signal feature recognition alone is solved. This method has low error rate and easy to obtain characteristic parameters. It is an effective method for mine microseismic and blasting event identification and can be widely used in practical engineering.
【作者单位】：中南大学资源与安全工程学院;山东黄金集团有限公司焦家金矿;
【基金】：国家重点基础研究发展计划(973)项目(2015CB060200) 国家自然科学基金资助项目(51504288,41272304)~~
【分类号】：TD326

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