1. State Key Laboratory of Coal Resources and Safe Ming,China University of Mining and Technology,Xuzhou　 221116,China; 2. School of Mines,China University of Mining and Technology, Xuzhou 　 221116, China; 3. Safety Technology Center, Sichuan Coal Mine Safety Supervision Bureau, Chengdu 　610045,China; 4. School of Earth and Space Sciences,University of Science and Technology of China,Hefei　 230000,China

There occur many acoustic emission (AE) events during the crack process of coal and rock. In order to au- tomatically detect these AE events fast and efficiently,an automatic detection model of acoustic emission events is con- structed. Firstly,a self-adapted updating method of time windows based on STA / LTA method (SSSW) is put forward. Secondly,based on the similar characteristics of AE waveforms generated by coal and rock fracture,the correlation function of waveforms within the given time window is established. And then,through the weighted LS iterative algo- rithm,relative arrival time is given in order to align the AE waveforms. Thirdly,a function is developed to calculate the semblance coefficient and the AE events can be detected by comparing semblance coefficient value with the given threshold. Finally,the automatic detection model of acoustic emission events (WCCADM) is established based on the SSSW,the correlation function,and the semblance coefficient function. The WCCADM not only counts the signal noise ratio but also unifies the similarity and differences of amplitudes and wave durations between noise and AE events of coal and rock fracture. The operation process of and AE data generated by swelling fracture test of limestone indicated that:SSSW can determine the optimal length of time window according to the durations of waveforms and the time in- tervals between neighboring events. And the semblance coefficient given by the WCCADM can be used to detect the valid AE events among the massive quantity of AE data generated by coal and rock,which bases the research on AE source localization,focal mechanics and the fracture inversion both in time and space scales.

国家重点研发计划资助项目(2016YFC0801403)；国家自然科学基金资助项目(51504250)；四川省安全生产科技攻关资助项目(aj20180416142619)；

LI Nan,ZHANG Xin,HUANG Bingxiang,et al. Automatic detection model of acoustic emission events of coal and rock based on waveforms correlation[J]. Journal of China Coal Society,2018,43(7):1893-1901.