The blind separation denoising method for surface array micro-seismic data
DIAO Rui1,2, WU Guo-Chen1, SHANG Xin-Min2, RUI Yong-Jun2, CUI Qing-Hui2
1.School of Geosciences,China University of Petroleum(East China),Qingdao 266555,China; 2.Geophysical Research Institute of Shengli Oil Field Branch,SINOPEC,Dongying 257022,China
Abstract：Due to the ground noise interference,the signal-to-noise ratio(S/N ) of original micro seismic monitoring data is relatively low,and the data quality determines the positioning accuracy of micro seismic events.Therefore,it is very important to improve the S/N of monitoring data in the processing of micro-seismic event.Based on the advantages of more monitoring sites,short intervals and wide distribution in ground array,the method of blind source separation based on cross-correlation method can denoise random noise.Blind source separation method is based on cross-correlation method,which uses negentropy as the objective function,with particle swarm optimization method for high efficiency solving.Through the cross correlation coefficient to solve the uncertainty problem of blind source separation,the method can realize effective separation of the ground array micro seismic events components and noise interference components.Through the forward simulation signal and the actual ground array micro-seismic data processing,the method was effectively applied to enhancing S/N of the ground array micro seismic data,which confirms that the method is feasible and effective.
