Seismic Random Noise Attenuation via Block Matching Local SVD Operator Based Sparsity and TV Regularization
O. Shao, L. Wang, X. Hu and Z. Long
Event name: 81st EAGE Conference and Exhibition 2019
Session: Poster: Full Waveform Inversion C
Publication date: 03 June 2019
Info: Extended abstract, PDF ( 772.21Kb )
Price: € 20
Block Matching Local SVD Operator Based Sparsity and TV Regularization (BMLSVDTV) method is a novel and effective denoising scheme in the field of image processing, which integrates group sparsity and global TV regularization in a unified framework and works well in attenuating random noise in image. The group sparsity is able to recover structures with more details clearly by exploiting the repetitiveness of the valid structures globally in images, and the global TV can suppress pseudo-Gibbs artifacts in the restoration. Because the reflection seismic data generally has a degree of redundancy due to the repetition of geological structures which satisfies the application conditions of this method, we propose to adopt the BMLSVDTV method to suppress the seismic random noise. Tests on synthetic and real seismic data demonstrate that the proposed method can suppress random noise much more effectively than the classical f-x deconvolution method and the Curvelet transform based method, and it preserves the seismic structures well without inducing pseudo-Gibbs artifacts.