Seismic random noise attenuation using effective and efficient dictionary learning

Abstract

Seismic data is often corrupted with random noise and thus may be of poor quality because of the low signal-to-noise ratio. The random noise may not only affect the velocity inversion and seismic migration, but also cause potential risks in post-stack processing and interpretation because of the artifacts. In this paper, we propose to apply the sparse dictionary learning algorithm to attenuate random noise in a transform-based denoising framework. The seismic data is first transformed to the sparse domain via a forward sparsity-promoting transform, then thresholded by a thresholding strategy, and finally transformed back to the time-space domain. The sparse dictionary can adapt to the complexity of the input seismic data and vary the basis functions to make the transform domain optimally sparse. Considering the challenges of low computational efficiency of the dictionary learning method because of the computational-intensive orthogonal matching pursuit (OMP) and K-singular value decomposition (K-SVD), we propose an accelerated scheme to make the processing much faster. On the one hand, we propose a fast algorithm for the sparse coding process. On the other hand, we also substitute the K-SVD method with a SVD-free strategy to make the dictionary updating process efficient. The overall efficiency of the dictionary learning method is much improved and thus is suitable for practical applications. We use synthetic and field data examples to demonstrate the performance and efficiency of the presented method.