Abstract:

According to the morphology and sparse signal theory, morphological component analysis (MCA) method is used for random noise attenuation in seismic data. The key of MCA is to select the appropriate dictionaries. In view of the characteristics of seismic data and computational complexity, UWT dictionary and Curvelet dictionary are selected.One sparsely represents for local singular part of the seismic data, the other sparsely represents for smooth and linear part of seismic data. BCR algorithm is used to solve objective function, and the denoised results are obtained by decomposing the seismic data into two morphologically different components and discarding the random noise which can’t be sparsely represented in dictionaries efficiently. As a 2D denoising method, MCA denoising method can efficiently suppress random noise both in time and spatial directions; Because the sparse representation abilities of UWT dictionary and Curvelet dictionary are stronger than traditional wavelet transform, MCA denoising is an amplitude and fidelity preserved denoising method, its damage to effective information is quite smaller. Theoretical and real data processing verified the efficiency of MCA method.

Key words: seismic data denoising, morphological component analysis, sparse representation, curvelet transform, wavelet transform