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Cluster Analysis of the Domain of Microseismic Event Attributes for Floor Water Inrush Warning in the Working Face by Guo-Jun Shang; Xiao-Fei Liu; Li Li; Li-Song Zhao; Jin-Song Shen; Wei-Lin Huang is a Earth and Planetary Sciences article available to read on EtoBox.

What is Cluster Analysis of the Domain of Microseismic Event Attributes for Floor Water Inrush Warning in the Working Face about?

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 diff erences, accurate classifi cation and analysis of microseismic events are important for the water inrush warning of the coal mine working face fl oor. Cluster analysis, which classifi es samples according to data similarity, has remarkable advantages in nonlinear classifi cation. A water inrush early warning method for coal mine fl oors is proposed in this paper. First, the short time average over long time average (STA/LTA) method is used to identify eff ective events from continuous microseismic records to realize the identifi cation 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 classifi cation of microseismic events. Clustering results of synthetic and fi eld data demonstrate the eff ectiveness of the proposed method. The analysis of fi eld data clustering results shows that the fi rst kind of events with time change rules is of considerable importance to

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Author

Guo-Jun Shang; Xiao-Fei Liu; Li Li; Li-Song Zhao; Jin-Song Shen; Wei-Lin Huang

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Earth and Planetary Sciences (Physical Sciences)