scholarly journals Five-dimensional seismic data reconstruction using the optimally damped rank-reduction method

2019 ◽  
Vol 218 (1) ◽  
pp. 224-246 ◽  
Author(s):  
Yangkang Chen ◽  
Min Bai ◽  
Zhe Guan ◽  
Qingchen Zhang ◽  
Mi Zhang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reduction Method ◽  
Data Reconstruction ◽  
Rank Reduction ◽  
Seismic Data Reconstruction

scholarly journals Five-dimensional seismic data reconstruction using the optimally damped rank-reduction method

2019 ◽  
Vol 218 (2) ◽  
pp. 1226-1226 ◽  
Author(s):  
Yangkang Chen ◽  
Min Bai ◽  
Zhe Guan ◽  
Qingchen Zhang ◽  
Mi Zhang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reduction Method ◽  
Data Reconstruction ◽  
Rank Reduction ◽  
Seismic Data Reconstruction

scholarly journals Expression of Concern: Five-dimensional seismic data reconstruction using the optimally damped rank-reduction method

2020 ◽  
Vol 221 (3) ◽  
pp. 2049-2049
Author(s):  
Yangkang Chen ◽  
Min Bai ◽  
Zhe Guan ◽  
Qingchen Zhang ◽  
Mi Zhang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Reduction Method ◽  
Data Reconstruction ◽  
Rank Reduction ◽  
Seismic Data Reconstruction

scholarly journals Adaptive rank-reduction method for seismic data reconstruction

2018 ◽  
Vol 15 (4) ◽  
pp. 1688-1703 ◽  
Author(s):  
Juan Wu ◽  
Min Bai
Keyword(s):  
Seismic Data ◽  
Reduction Method ◽  
Singular Values ◽  
Data Reconstruction ◽  
Rank Reduction ◽  
Practical Applications ◽  
Field Environment ◽  
Local Window ◽  
Seismic Data Reconstruction ◽  
Automatic Algorithm

Abstract Seismic data reconstruction plays an important role in the whole seismic data processing and imaging workflow, especially for those data that are acquired from severe field environment and are missing a large portion of the reflection signals. The rank-reduction method is considered to be a very effective method for interpolating data that are of small curvature, e.g. the post-stack data. However, when the data are more complicated, the rank-reduction method may fail to achieve acceptable performance. A useful strategy is to use local windows to process the data so that the data in each local window satisfy the plane-wave assumption of the rank-reduction method. However, the rank in each window requires a careful selection. Traditional methods select a global rank for all windows. We have proposed an automatic algorithm to select the rank in each processing window. The energy ratio between two consecutive singular values is chosen as the criterion to define the optimal rank. We apply this strategy to seismic data interpolation and use both synthetic and field data examples to demonstrate its potential in practical applications.

Tubal-Sampling: Bridging Tensor and Matrix Completion in 3-D Seismic Data Reconstruction

2020 ◽  
pp. 1-17
Author(s):  
Feng Qian ◽  
Cangcang Zhang ◽  
Lingtian Feng ◽  
Cai Lu ◽  
Gulan Zhang ◽  
...  
Keyword(s):  
Seismic Data ◽  
Matrix Completion ◽  
Data Reconstruction ◽  
Seismic Data Reconstruction
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