Research On The Method Of Suppressing Random Noise In Mine Microseismic Signal

Microseismic monitoring technology has been widely used in many engineering fields,such as mine disaster monitoring,oilfield fracturing,slope stability analysis,etc.Owing to technical limitations of seismic geophone and external monitoring of environmental impact,the microseismic data collected in actual are received a lot of noise interference during collection and transmission.These noises have seriously affected the measurable analysis of microseismic signal and brought great challenge to the source location,energy calculation,Catastrophic monitoring and other subsequent work.Since the frequency bands of microseismic signal and external random noise are totally or partly overlapped,it is difficult to separate microseismic signal from non-stationary random noise by using the traditional methods,such as linear filtering and spectrum analysis.Therefore,it has important theory and practice significances to study the de-noising method of microseismic signals.At present,there are many effective methods of signal denoising.However,each denoising algorithm has its advantages and disadvantages and application environment.Aiming at the non-stationary and nonlinear characteristics of mine micro seismic signals and the disadvantages of traditional algorithms.In this paper,an efficient approach combining NLMS adaptive filter and complete ensemble empirical mode decomposition(CEEMD)is proposed.Firstly,NLMS adaptive filter is employed to filter impulse noise in microseismic signal containing impulse noise.Then the multi-scale decomposition of the pretreated signal based on CEEMD is conducted.Several components of intrinsic mode functions(IMFs)are obtained and they are arranged in descending order according to their frequencies.Finally,in order to accurately get the effective signals in these IMF components,the threshold de-noising is introduced.The threshold de-noising is used to suppress the noise in the IMF components.These IMF components are reconstructed to suppress the noise.Finally,the CEEMD decomposition level and wavelet threshold function are discussed.In this paper,we compare and analyze several sets of signals.The experimental results show that when the decomposition level is set to 7,the noise reduction effect is better.The simulation results show that the root mean square error of soft threshold denoising is small,and the SNR is high after noise reduction,and the noise reduction effect is good.We deployed our algorithm in the experiments of microseismic signal de-noising and compared our results with existing algorithms.The result shows that the proposed de-noising method has higher increment of signal-to-noise ratio and energy percentage,which quantitatively proves the effectiveness and practicability of the proposed de-noising method for microseismic signals.