Paper Info
Title
Separation and Reconstruction of Nonuniform Simultaneous Source Data via a Robust and Sparse Radon Transform
Abstract
In recent years, simultaneous source seismic data acquisition attracts much attention because of its higher efficiency and lower cost. However, the presence of blending noise reduces the accuracy of subsequent traditional seismic data processing steps, necessitating the separation of simultaneous source data. Conventional separation methods assume that seismic data are distributed on uniform grids, but in field cases, firing shots are always distributed on nonuniform grids. The binning strategy assigns a nonuniform sample onto its nearest uniform sample, introducing unavoidable errors and lowering the separation accuracy of simultaneous source data. As a result, irregularity influences must be taken into account during simultaneous source separation. To separate nonuniform simultaneous source data, a robust and sparse Radon transform (RSRT) is introduced because the Radon transform can deal with nonuniform grids along the space adaptively. The effectiveness of the proposed method in attenuating blending noise and reconstructing seismic data to uniform grids is demonstrated by synthetic and field data analysis.
Year
DOI
Venue
2022
10.1109/LGRS.2022.3192261
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Keywords
DocType
Volume
Radon, Transforms, Mathematical models, Data models, Symbols, Iterative algorithms, Time-frequency analysis, Nonuniform simultaneous source data, robust and sparse Radon transform (RSRT), separation and reconstruction
Journal
19
ISSN
Citations 
PageRank 
1545-598X
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Jie Wang100.34
Benfeng Wang247.52