Improving the signal-to-noise ratio of original data is the primary task in seismic processing. This paper studies an effective denoised method in 3D seismic data based on sparse representations over learned sparse dictionaries. Compared with the common dictionary training algorithm, the sparse dictionary learning method can solve the large dictionaries training problems in high dimensional seismic data.

Besides, the combination of dictionary training and seismic denoising make it possible to find the sparse representation of the signal and suppress the random noise at the same time. Apply this method to 3D synthetic and real data, then compare the denoised results with the threshold method based on 3D curvelet transform. The final outcome shows that this method has better performance. It can protect the weak signal and cause less artifacts.


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