Low-Rank Tensor Minimization Method for Seismic Denoising Based on Variational Mode Decomposition
Jun Feng, Xi Liu, LI Xiao-qin, Wenxi Xu, Bin Liu
Abstract
Seismic data contain a lot of information in both spatial (<i>t-x</i>) and frequency domains. In order to make full use of the effective information in multiple domains, this letter proposes a low-rank tensor minimization method for seismic data denoising. This model first uses variational mode decomposition (VMD) to decompose the seismic data in the frequency domain and constructs a seismic tensor to highlight the frequency information of the seismic data; then to make use of the spatial similarity and frequency correlation of the seismic tensor, a low rank seismic tensor is built through block matching and a low-rank tensor minimization model is established to attenuate the noise. Finally, the denoised seismic tensor is reconstructed into a seismic section. Experimental results show that compared with several denoising methods, the method proposed in this letter can obtain higher signal-to-noise ratio (SNR) and structural similarity (SSIM) and achieve better denoising effects.