Research on Rock Burst Monitoring and Early Warning Technology Based on RBF Neural Network

Abstract

China is one of the most serious coal mine accidents inthe countries of the world. All of the accidents, rock burst is one of them. Therock burst in coal and rock mass, refers to the sudden power failure, release alarge number of catastrophic dynamic phenomena of energy. It can be destroy theroadway roof, cause other mine disasters, casualties and so on. In China, themine number with rock burst dangerous accounted for more than 20% of the total,Shandong QufuXing cun coal mine among them. In order to prevent to the happen of accident,the coal mine enterprise had been install all kinds of monitoring system, suchas SOS micro seismic system , Fully mechanized working face resistance ofsupport system and so on. Using sensors measuring and computer technology, thedata had been getting from the underground 1000 meters. According to the internal link ofpressure behavior between the basic regularity and variable, RBF neural networkhad been set up. From the model, it can forecast the risk index of rock burst,reveal the superincumbent stratum roof movement; master the process of stateand changes in the laws of underground pressure. It is important significanceto guide safe production of coal mine enterprises.