SEISMIC NOISE ATTENUATION USING AN IMPROVED VARIATIONAL MODE DECOMPOSITION METHOD

Seismic noise suppression is an important step in the seismic imaging community. We propose a dip-separated denoising method to attenuate spatially incoherent random noise. The variational mode decomposition (VIVID) method is used to decompose the seismic data into different dip bands. It has a solid theoretical foundation of mathematics and high calculation efficiency. Besides, compared with the recursive mode decomposition algorithms, e.g., the EMD and EEMD methods, it has advantages in solving the mode mixing problem and more powerful anti-noise performance. The VIVID method can adaptively decompose a seismic signal into several intrinsic mode functions (IMF). Decomposing the seismic data into oscillating IMF components is equivalent of decomposing the seismic data into different dipping components. To automatically define the optimal number of most oscillating components, we design the Kurtosis method. To eliminate the errors caused by end effect, we use a waveform matching extension algorithm to improve the VIVID. The singular spectrum analysis (SSA) method is used to approximate the low-rank components in each separated dip band. In this paper, a simulated seismic dataset and a real seismic dataset are analyzed by the proposed algorithm. The results indicate that the proposed algorithm is robust to noise and has high de-noising precision.