EMD denoising method based on frequency domain constrained independent component analysis

Noise pollution is an important issue to be solved in the application of coal or rock dynamic disasters elec- tromagnetic monitoring. Denoising effect directly affect the disaster prediction accuracy. Empirical Mode Decomposition (EMD) is the most widely method used in electromagnetic signal denoising. But when the frequency characteristics of the signal and noise are similar,the algorithm exists serious noise aliasing of intrinsic mode function ( IMF). Some IMFs are the combination of signal and noise. To solve this problem,this paper proposes a denoising method based on EMD and frequency domain constrained independent component analysis. Firstly,the noisy signal is decomposed to a series of IMFs by EMD. Secondly,calculate the correlation coefficients of each IMF and the original signal,identify the transition IMF between noise and signal. Then the high frequency noise IMFs above the transition IMF are removed. Thirdly,independent component analysis of the transition IMF based on frequency-domain constraints( the frequency domain of transition IMF follow-up component) to remove noise. Finally,the denoised transition IMF and its subse- quent IMFs are reconstructed to the denoised signal. Take noisy Ricker wavelet and field electromagnetic signals as the example,use signal-noise ratio quantitatively verify the validity of the denoising method described above to process electromagnetic signal mode aliasing problem. Frequency domain constrained independent component analysis has the advantages of denoising fast convergence and high efficiency. The denoising method is suitable for mass rapid real-time monitoring signals.