Iterative deblending of simultaneous-source seismic data using seislet-domain shaping regularization
By Yangkang Chen, Sergey Fomel, Jingwei Hu. 2014
259 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Geophysics
View Paper: https://www.semanticscholar.org/paper/4e661fd7c03c638ea4d0fa1da1df7f7f7af0737b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Xiaohong Chen
- Jinkun Cheng
- Mauricio D. Sacchi
- Weilin Huang
- Yangkang Chen
- Shaohuan Zu
- Shuwei Gan
- Run Wang
In Geophysics
- Xiaohong Chen
- Yangkang Chen
- Jinkun Cheng
- Shaohuan Zu
- Weilin Huang
- Mauricio D. Sacchi
- Shuwei Gan
- Run Wang
Citations in the corpus
- Fast dual-domain reduced-rank algorithm for 3D deblending via randomized QR decomposition (2016, background): “The coherence constraint can also be implemented by sparsity-promoting inversion techniques that operate in different domains for better separation (Abma et al., 2010; Mahdad et al., 2011; Mansour et al., 2012; Li et al., 2013; Chen et al., 2014).”
- Damped multichannel singular spectrum analysis for 3D random noise attenuation (2015, background): “…transform (Jones and Levy, 1987), sparse transform domain thresholding strategies (Donoho, 1995; Neelamani et al., 2008; Fomel and Liu, 2010; Chen et al., 2014, 2016) and Cadzow filtering (Trickett, 2008; Trickett and Burroughs, 2009) or multichannel singular spectrum analysis (Oropeza…”
- Structure-oriented singular value decomposition for random noise attenuation of seismic data (2015, background): “The third is based on extracting the principal components of seismic data, such as multichannel singular spectrum analysis (MSSA) (Oropeza and Sacchi, 2011; Chen et al., 2015) and EMD based approaches (Chen et al., 2014c).”
- Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform (2016, methodology): “The inverse transform starts with the coarsest scale and goes back to the finest scale (Chen et al., 2014b).”