关键词: 微震, 识别算法, 定位算法, 岩爆, 滤噪, 岩石工程

Abstract: It has been popular and difficult for scientists and engineers to acquire more microseismic information, automatically identify microseismic data of rock rupture, locate the location of rock rupture automatically and accurately, and provide theoretical and technical support for disaster analysis, early warning, and prevention and control. In view of these difficulties, an integrated and high precision intelligence microseismic monitoring technology was developed. In the microseismic monitoring technology, several innovation techniques are developed, which include that a sensing-acquisition-transmission technology was integrated; the acquisition technology with noise reduction of 32 bits A/D coupled with electric components was established; integrated identification method of microseismic signal including recursive STA/LTA method and BP neural network method was proposed; the microseismic source location algorithm based on the technique of PTP high-precision time synchronization and the algorithm of velocity model fast matching in database was developed. At present, this technique has been applied in many deep rock engineering at home and abroad. The application results show that the developed technique can be applied not only to monitoring dynamic disasters such as rock burst, but also to monitoring the stability of surrounding rockmass in excavation (mining) of rock engineering, and also to monitoring anti-theft mining of mineral resources. From these application cases, it has been show that the capacity of acquiring microseismic signals, identifing microseismic signals of rock rupture automatically, and locating the location of rock rupture accurately was improved during rock engineering disaster evolution using the developed microseismic technique. The developed technique can promote the rapid development of microseismic monitoring technology towards automatic monitoring, analysis and intelligent early warning of rock engineering disasters.

Key words: microseismic, recognition algorithm, location algorithm, rockburst, de-noising method, rock engineering