Research On De-noising Of Microseismic Signal Based On ICA And EMD Method

The microseismic signal is a complex nonlinear nonstationary signal,and the effective signal is usually lower than the noise energy,and most of the time will be drowned by the noise.This paper combines two kinds of signals from Empirical mode decomposition: EMD)and independent component analysis(Independent component analysis: ICA).A strong adaptive method is used to denoise the microseismic data,and the algorithm is optimized by using the characteristics of the high order cross cumulant to calculate the time difference.Finally,the simulation and actual microseismic data are tested.This paper first introduces the basic concepts and sieving iterations of the EMD algorithm and its improved algorithm,which includes the empirical mode decomposition EEMD and the complete set of empirical mode decomposition(CEEMD),and uses the synthetic signal to test the three algorithms.The EEMD algorithm introduces white noise to denoise on the basis of EMD algorithm,and improves the shortcomings of modal aliasing in EMD algorithm,but it will introduce new noise and cause interference to the original signal.On the basis of EEMD,CEEMD is proposed to add positive and negative Gauss white noise to the original data for EMD decomposition,which is not only a good solution to the problem.The problem of modal aliasing,which can be eliminated when the noise is added,will not introduce new noise and can reconstruct the original signal accurately,and the calculation efficiency is higher.Next,we mainly introduce and apply the fixed point ICA(FastICA)algorithm to separate the noise source from the signal source of the microseismic data,and the separation effect is better when the actual data of the single channel is not complicated.In the actual multichannel microseismic data,the signal transmission meets the different resistance and the effective signal phase is not consistent,and it is directly ICA blind source separation,noise source and signal source mixed together,the effect is very poor,the current ICA separation technology does not take into account the difference in the receiving time of the geophone in the actual microseismic signal,and its accuracy can not be used in actual production.Based on this,this paper selects the high order cross cumulant algorithm to optimize it.The algorithm can quickly and accurately pick up the effective signal,and carry out the time difference estimation according to the effective signal location picked up.According to the result,the time difference migration is carried out to the effective signal,and the offset is offset to the same phase point,and the offset data is ICA Blind source separation can effectively separate signal sources from noise sources,and the optimization effect is significantly improved.After that,the basic concept and application of ICA and EMD combined denoising algorithm are introduced.Through the blind source separation of the IMF component of modal aliasing,the signal source is reconstructed and compared with the EMD algorithm.The accuracy of the reconstructed signal can keep the characteristic of the effective signal and the denoising effect is better.The improved ICA-CEEMD algorithm combined with denoising algorithm is compared with the traditional ICA-EMD joint denoising algorithm.The improved algorithm has higher amplitude preservation.Finally,based on the in-depth study of high-order cross cumulants,a high-order cross cumulant based location algorithm is proposed.Compared with the traditional SET source location,the algorithm has a higher positioning accuracy and less influence on the complex noise than the traditional location of the source,and with the increase of the mutual accumulation order,the location of the source can be accurately located with few detection points,but the actual microseismic data is too complex and the direct application effect is not ideal,the algorithm is not ideal.The theory remains to be perfected.