Random Noise Attenuation by Learning-type Overcomplete Dictionary Based on K-singular Value Decomposition Algorithm

Abstract

Summary The transformation of basic function is one of the most commonly used techniques for seismic denoising, which employs sparse representation of seismic data in the transform domain. The choice of transform base functions has an influence on denoising results. A learning-type overcomplete dictionary based on the K-singular value decomposition (K-SVD) algorithm is proposed. To construct the dictionary and use it for random seismic noise attenuation, we replace the fixed transform base functions with an overcomplete redundancy function library. Owing to the adaptability to data characteristics, the learning-type dictionary describes essential data characteristics much better than conventional denoising methods. Therefore, the sparsest representation of signals is obtained by the learning and training of seismic data. By comparing the same seismic data obtained using the learning-type overcomplete dictionary based on K-SVD and other denoising methods, we find that the learning-type overcomplete dictionary based on the K-SVD algorithm is able to represent seismic data more sparsely and suppress the random noise more effectively.