Abstract：Combining the complete ensemble empirical mode decomposition (CEEMD) and the K singular value decomposition (KSVD) dictionary algorithm,a seismic denoising is proposed in this paper.A signal with random noise is decomposed by CEEMD into a series of inherent modal functions (IMF) of different scales.The IMF components are arranged from high to low frequency and their autocorrelation eliminates noise-dominant IMF components.Accumulated transitional components are superimposed and reconstructed by CEEMD decomposition again and noise-dominant components are removed again by the autocorrelation.The second remaining IMF components and the first remaining IMF components are superposed to get two new noisy signals which are sparsely represented by KSVD learning dictionary respectively.In the other words,sparse coefficients reconstruct denoised signals.Finally,two sparse denoising signals are reconstructed.Experimental results show that the proposed algorithm can better remove noise than conventional methods such as F-X,wavelet threshold,and KSVD.
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