Damped multichannel singular spectrum analysis for diffraction separation based on the Cook-distance
Diffraction seismic exploration is a crucial technique for enhancing the lateral resolution of small-scale geological structure imaging.Diffracted waves in conventional seismic records can be obscured by stronger energy reflected waves,making it necessary to separate these two wavefields for imaging purposes.Damped multi-channel singular spectrum analysis(DMSSA)is a rank-constrained denoising method that sepa-rates wavefields by decomposing the seismic data into a Hankel matrix and performing singular value decomposi-tion.In this process,reflected and diffracted waves correspond to larger and smaller singular values respec-tively.However,this method relies on manually determining the rank of the reflected wavefield,which is im-practical for processing large volumes of seismic data.To address the issue of manual selection,this pa-per proposes using Cook-distance to automatically calculate the rank of the reflected wavefield.By combining Cook-distance with the DMSSA algorithm,this paper achieved effective separation of reflected and dif-fracted waves.Experiments on common-shot gathers and post-stack data demonstrate that this method can suc-cessfully obtain high-quality diffraction wavefields,highlighting the effectiveness of the proposed approach.