Two-dimensional complex wavelet transform for linear noise attenuation and image decomposition

For developing a high-fidelity, high-resolution seismic denoising method, we use the two-dimensional complex wavelet transform (2D CWT) to analyze noise and signals. By investigating a surface wave's features and evaluating factors affecting the fidelity of the method, the best practice for the wavelet transform-based denoising has been established. First, static and normal moveout correction are applied on shot gathers. Then, 2D CWT is used to attenuate linear noises. The results demonstrate that the proposed method and practice significantly attenuate noises and preserve the signal's amplitudes and frequency band. In addition to denoising, we also apply the 2D CWT to decompose a seismic image into multiscale images with different resolutions. Multiscale decomposed images derive more detailed information for subsurface structures and fault networks. The decomposed images depict sharper structures and reveal detailed features of faults more significantly than the original images.