Three-Dimensional Seismic Data Reconstruction Based on Fully Connected Tensor Network Decomposition

作者：Yuejiao Xu, Lihua Fu, Xiao Niu, Xingrong Chen, Meng Zhang · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2023 · DOI：10.1109/tgrs.2023.3272583 · 被引用次数：7 · 研究领域：Seismic Imaging and Inversion Techniques、NMR spectroscopy and applications、Geophysics and Gravity Measurements

Rank-reduction approaches assume that seismic data in the frequency-space domain is of low-rank after a specific pre-transformation. The presence of noise or missing traces will increase the rank; therefore, seismic data can be denoised and recovered via rank-reduction techniques. The iterative weighted project onto the convex set (POCS) framework can be used for noise attenuation and data reconstruction simultaneously. Multichannel singular spectrum analysis (MSSA) is a classic 3D seismic data reconstruction algorithm that rearranges the temporal frequency slices of the data with missing traces into a block Hankel matrix, and then uses randomized singular value decomposition (RSVD) to interpolate slices. To further improve the efficiency and precision of 3D seismic data reconstruction, we introduce the fully connected tensor network (FCTN) decomposition over the Hankel tensor of the frequency slices. We show that our novel rank-reduction method estimates fewer parameters than MSSA, yielding more accurate and robust results. Moreover, FCTN decomposes a fourth-order tensor into four factor contractions, which breaks the limitations that traditional tensor decomposition methods such as CANDECOMP/PARAFAC (CP) and Tucker decomposition cannot establish the connections between different factors, and are less effective at characterizing relationships. The newly proposed approach does not require singular value decomposition (SVD), leading to an overall reduction in computational com...