LAUSR

Multichannel singular spectrum analysis (MSSA) is an effective tool for processing multidimensional time-series such as the reconstruction of high-dimensional seismic data. Low-rank estimation is a key stage in MSSA algorithm that can affect the recovery process. Truncated singular value decomposition (TSVD) and singular value thresholding (SVT) are two conventional options for rank reduction, which, however, do not result in satisfactory outcomes, especially in low signal-to-noise-ratio cases. In this letter, we propose to leverage the optimal low-rank estimator that emerges from random matrix theory known as OptShrink. The OptShrink can obtain more robust low-rank estimation in comparison with TSVD and SVT. In addition, we propose to constrain the singular values using a damping factor. The proposed damped OptShrink method is applied on real and synthetic 3-D seismic data. The comprehensive experiments and discussion verify the superior reconstruction ability of the proposed method in comparison with MSSA.