Seismic Data Interpolation Based on Simultaneously Sparse and Low-Rank Matrix Recovery

Seismic data interpolation is a highly ill-posed problem. Therefore, designing an appropriate regulating method, aiming to reduce multi-solutions, is of utmost importance. Sparse and low-rank priors or constraints, which consider certain kinds of redundant data structures from different viewpoints,...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-13
Main Authors: Niu, Xiao, Fu, Lihua, Zhang, Wanjuan, Li, Yanyan
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Data models
Data recovery
Data structures
Ill posed problems
Interpolation
Low-rank
Mathematical model
Objective function
Seismic data
seismic data reconstruction
Solid modeling
sparse
Sparse matrices
Spectrum analysis
Tensors
Transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Seismic data interpolation is a highly ill-posed problem. Therefore, designing an appropriate regulating method, aiming to reduce multi-solutions, is of utmost importance. Sparse and low-rank priors or constraints, which consider certain kinds of redundant data structures from different viewpoints, are widely used to constrain recovered seismic data to achieve a better fit. Considering that additional information enables us to obtain more accurate reconstructions, we formulated a seismic data interpolation model with irregular missing traces as a joint sparse and low-rank matrix approximation problem. The result is a solution that fits the given data, while simultaneously being sparse and low-rank. Subsequently, an efficient alternating algorithm is developed to solve the proposed objective function. Our proposed model called joint sparse and low-rank priors (JSLRP) model performs better on synthetic and field 3-D seismic data when compared to the classic low-rank methods, such as multichannel singular spectrum analysis (MSSA) and damped MSSA.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3110600