Cluster analysis of the domain of microseismic event attributes for fl oor water inrush warning in the working face

基于微地震事件的识别以及属性域聚类分析的煤矿工作面底板突水预警方法研究

Differences are found in the attributes of microseismic events caused by coal seam rupture,underground structure activation,and groundwater movement in coal mine production.Based on these differences,accurate classification and analysis of microseismic events are important for the water inrush warning of the coal mine working facefloor.Cluster analysis,which classifies samples according to data similarity,has remarkable advantages in nonlinear classification.A water inrush early warning method for coal minefloors is proposed in this paper.First,the short time average over long time average(STA/LTA)method is used to identify effective events from continuous microseismic records to realize the identification of microseismic events in coal mines.Then,ten attributes of microseismic events are extracted,and cluster analysis is conducted in the attribute domain to realize unsupervised classification of microseismic events.Clustering results of synthetic andfield data demonstrate the effectiveness of the proposed method.The analysis offield data clustering results shows that thefirst kind of events with time change rules is of considerable importance to the early warning of water inrush from the coal mine working facefloor.

煤矿生产中煤层破裂、地下构造活化以及地下水运动等多种诱因产生的微地震事件在属性特征上存在差异,基于这些差异对微地震事件进行准确分类和分析对煤矿工作面底板突水预警具有重要意义。聚类分析根据数据间相似度对样本进行分类,在非线性分类方面具有巨大优势。本文提出了一种煤矿工作面底板突水预警方法,该方法首先利用short time average over long time average(STA/LTA)方法从连续的微地震记录中识别出有效事件,实现煤矿微地震事件的识别;然后提取微震事件的10个属性特征,并属性域进行聚类分析实现微地震事件的无监督分类。模型数据和实际数据的聚类结果都证明了该方法的有效性。实际数据聚类结果的分析表明第一类事件随时间的变化规律对于煤矿工作面底板突水预警有重要意义。