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

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.