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 face floor. Cluster analysis, which classifies samples according to data similarity, has remarkable advantages in nonlinear classification. A water inrush early warning method for coal mine floors 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 and field data demonstrate the effectiveness of the proposed method. The analysis of field data clustering results shows that the first kind of events with time change rules is of considerable importance to the early warning of water inrush from the coal mine working face floor.

煤矿生产中煤层破裂、地下构造活化、地下水运动等微震事件的属性存在差异。基于这些差异，对微地震事件进行准确分类和分析，对于煤矿工作面底板突水预警具有重要意义。聚类分析根据数据的相似性对样本进行分类，在非线性分类中具有显着的优势。提出了一种煤矿底板突水预警方法。首先，利用短时平均加长时平均（STA/LTA）方法从连续微震记录中识别有效事件，实现煤矿微震事件的识别。然后，提取微震事件的十个属性，在属性域进行聚类分析，实现微震事件的无监督分类。合成数据和现场数据的聚类结果证明了所提出方法的有效性。现场数据聚类结果分析表明，具有时间变化规律的第一类事件对煤矿工作面底板突水预警具有重要意义。