单事件多通道微震波形的特征提取与联合识别研究

Feature extraction and classification of mining microseismic waveforms via multi-channels analysis

通过对矿山现场数据的处理分析,提出一种基于微震单事件的多通道联合识别方法,建立了"初步判断"、"联合识别"及"优化判断"的微震波形识别机制。对矿山微地震信号进行滤波等预处理,采用经典STA/LTA算法拾取波形的到时与终时,截取整个信号中的有效部分,并进行波形校正;建立波形的频谱特征(f)、时长(L)、振幅特征(A)、振幅分布(AD)、门限阈值特征(TS)及互相关特征(R)的定量描述方法,并求取相应的特征值;分层次对有效岩石破裂波形进行有效性判断与识别,优化选取最终定位通道。以山东某矿的一次微震事件为例对该方法进行了验证研究。结果表明,该方法能够有效提取波形的特征,并能实现对单事件多通道波形电磁干扰、底部噪声等的快速分类识别,识别精度满足现场应用需求。

In this paper, the author presented a strategy for classifying local multi-channels MS waveform, triggering by a single event. There were three steps to achieve the goal. Firstly, based on STA/LTA method, the first arrival and terminated time had been picked up, using the MS signal preprocessed;secondly,the author extracted the waveform features, time-frequency （ L ,f）, amplitude （ A）, statistics of amplitude distribution （AD） and threshold algorithm （ TS）, and correlation coefficient（R） ;thirdly,before establishing an effective judgment mechanism, this method employed a hierarchical recognition framework with 3 layers, which integrated preliminary judgment, combined recognition and optimizationjudgment. This method was validated through analyzing a coal mine visual event in Shandong Province, and the result shows that it is successfully used to classify electromagnetic interference wave, background noise and MS events. The result can basically meet the requirements of classification accuracy.