Study On Denoising And Statistical Identification Of Microseismic Monitoring Signal Of Mine

It is very important to monitor and forewarn the stability of mining area.The digital information microseismic monitoring technology is one of the most advanced monitoring and early warning technologies in mine safety production.However,there are still many problems to be studied and solved in microseismic monitoring technology,such as noise removal of complex microseismic monitoring signals and differential identification of microseismic monitoring signals.The main research contents and conclusions are as follows:(1)Study on mine micro seismic signal denoising by wavelet threshold denoising method.Select DB4 wavelet,add white noise to block,Dopler and Bumpus signals in Matlab environment,then carry out wavelet threshold denoising simulation research,introduce root mean square and signal-to-noise ratio to evaluate the denoising effect.It is found that the DB4 wavelet base combination rigrsure can achieve better denoising effect.The simulation results are applied to the de-noising and extraction of the micro earthquake rock fracture signal in the mine.It is found that the wavelet threshold denoising method has a very good effect on the de-noising and extraction of the micro earthquake signal.After the DB4 small wavelet combination rig,the noise can be effectively eliminated and the rock fracture signal can be extracted.(2)Based on the background of microseismic monitoring in Xianglushan tungsten mine,1900 microseismic monitoring signals were collected,and the amplitude,rise time,attenuation time,duration,interval time,number of trigger sensors and main frequency of the signals were counted after the signal was de-noising.The statistical quality chart was drawn,and the statistical results were made to provide reference for the identification of microseismic signals and the extraction of useful signals Database.(3)The second recognition of irregular artificial knocking and microseismic signals which are difficult to identify by waveform and single parameter is studied.Six characteristic parameters of signal amplitude,rise time,decay time,duration,interval time,dominant frequency and relative energy E-value are selected to calculate the discrimination equation and threshold value of the two kinds of signals in combination with Fisher linear discrimination theory.The linear discrimination mode of the two kinds of signals is established.The discrimination model is used to check back the training set and detection set,and the result shows the discrimination accuracy It is 91.50%and more than 90.50%.It can be seen that Fisher discriminant method can effectively identify irregular artificial knocking and microseismic signals.(4)This paper selects a typical case of microseismic location event signal identification and analyzes it,shows the use of the database,reflects the necessity of the existence of the database,its application value and engineering significance.Finally,the typical artificial knocking signal and rock fracture signal are selected,and the established Fisher discriminant model is used to verify their discriminant results,which are consistent with the actual situation,and the accuracy of the discriminant model is verified.