Research On Key Technologies Of Microseismic Monitoring Under Complex Noise Background In Coal Mine

Microseismic monitoring technology mainly includes microseismic signal picking and source location.However,due to the harsh conditions of underground environment,random noise signal is often included in microseismic signal collected by detector.A large number of random noises and effective microseismic signals are mixed together,it seriously interferes with the P-wave time pick of microseismic signal and the location of the source.Therefore,rapid and accurate identification of rock burst microseismic signals is particularly important.In this thesis the noise reduction method of microseismic signal is studied firstly.Then,the joint algorithm is used to pick up P wave after obtaining high signal-to-noise ratio data.Finally,based on Time difference of Arrival(TDOA)combined with the improved particle swarm optimization algorithm(IPSO),the location of the source was optimized and the accurate location was obtained.The details are as follows:(1)Microseismic signals actually collected downhole are often accompanied by a large number of random noises.In this thesis,CEEMD-SVD combined with The shortterm average/long-term average algorithm(STA/LTA)is used to process the measured microseismic signals.The complementary set empirical mode decomposition(CEEMD)algorithm was used to obtain the intrinsic mode function(IMF),and STA/LTA values of each intrinsic mode function(IMF)component were calculated respectively,so as to judge whether singular inherent modal component(SVD)is needed,so as to reduce noise.Finally,the total average IMF is collected to obtain the signal after noise reduction.The proposed algorithm can effectively retain the features of the original signal and reduce the noise,which provides high signal-to-noise ratio data for the analysis of the characteristics and rules of microseismic activity.(2)Aiming at the poor accuracy of P wave picking of microseismic signals by STA/LTA algorithm,this thesis proposes a P wave picking algorithm combining with Bayes by comparing Akaike information criterion(AIC)and Bayesian Information Criterion(BIC).Firstly,STA/LTA algorithm is used to pick up the approximate time of P wave.Then,appropriate time window is selected before and after the approximate time of P wave,BIC is used to pick up the exact time of P wave.The algorithm ensures the picking accuracy,reduces the operation time greatly,and improves the picking efficiency of P wave.(3)TDOA-IPSO algorithm is proposed to solve the problem of low positioning accuracy of TDOA algorithm.Firstly,by analyzing the principle of particle swarm optimization algorithm,inertial weight value is introduced,nonlinear variable value is used to balance the local search function and global search function,and the method of dynamically adjusting the step size factor according to the average evaluation value is used to optimize the flight step size,and the particle swarm optimization algorithm(IPSO)is used to improve the local solution problem.Finally,the influence factors of focal location are analyzed,and the experimental parameters are determined.