Microseismic early warning for roof disaster in excavated coal roadways often suffers from low pertinence and a high false positive rate. This study establishes an intelligent early warning process based on unsupervised learning and a voting mechanism. True triaxial compression and drilling tests were conducted to characterize the acoustic emission responses of coal and rock during fracture. Using 720 h of field microseismic data from a high-gas mine in Shanxi, high-weight precursor features were extracted from time–frequency indicators. Kernel principal component analysis (KPCA) was used to optimize the indicator system, and 49 indicators with weights above 0.08 were selected as model inputs. Five unsupervised clustering algorithms were integrated to establish an ensemble decision-making early warning model. The results show that the model eliminates the drawbacks of single algorithms, achieves accurate roof disaster warning, and correctly distinguishes disaster events from non-disaster high-energy events. The false positive rate is zero on the 720 h field dataset, and the reliability of early warning is significantly improved. This study enhances the reliability of mine roof microseismic warning, enriches roof disaster prediction theories, provides a complete intelligent early warning process for mine roof disaster, and offers important references for deep mining dynamic disaster warning research.
Zhang et al. (Thu,) studied this question.