Seismic data denoising under the morphological component analysis framework combined with adaptive K-SVD and wave atoms dictionary

Many different effective reflection information are often contaminated by exterior and random noise which concealed in the seismic data. Traditional single or fixed transform is not suit for exploiting their complicated characteristics and attenuating the noise. Recent years, a novel method so-called morphological component analysis(MCA) is put forward to separate different geometrical components by amalgamating several irrelevance transforms. According to study the local singular and smooth linear components characteristics of seismic data, we propose a method of suppressing noise by integrating with the advantages of adaptive K-singular value decomposition(K-SVD) and wave atom dictionaries to depict the morphological features diversity of seismic signals. Numerical results indicate that our method can dramatically suppress the undesired noises, preserve the information of geologic body and geological structure and improve the signal-to-noise ratio of the data. We also demonstrate the superior performance of this approach by comparing with other novel dictionaries such as discrete cosine transform(DCT), undecimated discrete wavelet transform(UDWT), or curvelet transform, etc. This algorithm provides new ideas for data processing to advance quality and signal-to-noise ratio of seismic data.