3D interpolation of irregular data with a POCS algorithm

By R. Abma, N. Kabir. 2006

432 citations recorded by the source corpus.

Subfields: Computational Mechanics, Geophysics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/aa6c73cb2d2f57f257cca8c485c469a6fe9c736f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Jianwei Ma
2. Xiaohong Chen
3. Siwei Yu
4. Xiaoqun Zhang
5. Yangkang Chen
6. Jingwei Liang
7. Felix J. Herrmann
8. Gilles Hennenfent

In Geophysics

1. Jianwei Ma
2. Yangkang Chen
3. Siwei Yu
4. Xiaohong Chen
5. Xiaoqun Zhang
6. Shuwei Gan
7. Weilin Huang
8. Jingwei Liang

In Ocean Engineering

1. Yangkang Chen
2. Xiaohong Chen
3. Siwei Yu
4. Jianwei Ma
5. Weilin Huang
6. Shuwei Gan
7. Hanming Chen
8. Shoudong Wang

Citations in the corpus

• Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform (2016, background): “…migration, multiple suppression, amplitude-versus-offset (AVO) or amplitude-versus-azimuth (AVAZ) analysis, and time-lapse studies (Trad et al., 2002; Liu and Sacchi, 2004; Abma and Kabir, 2005, 2006; Wang et al., 2010; Naghizadeh and Sacchi, 2010; Chen et al., 2014a; Zhong et al., 2015).”
• Non-parametric seismic data recovery with curvelet frames (2008, methodology): “, 1992) to transform-based sparse inversion methods based on the Fourier or other transforms (Sacchi and Ulrych, 1996; Duijndan and Schoneville, 1999; Zwartjes and Gisolf, 2006; Abma and Kabir, 2006).”
• Seismic data restoration via data-driven tight frame (2014, background): “…(Kabir and Verschuur, 1995; Trad et al., 2002), Fourier transform (Sacchi et al., 1998; Gulunay, 2003; Liu and Sacchi, 2004; Xu et al., 2005; Abma and Kabir, 2006; Trad, 2009), seislet transform (Fomel and Liu, 2010; Liu and Fomel, 2010), and curvelet transform (Hennenfent and Herrmann,…”
• Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “We can YU AND MA 10.1029/2021RG000742 14 of 36 borrow training data from a natural image data set to train DnCNN and then embed it in the traditional project onto a convex set (POCS, Abma & Kabir, 2006) framework (Zhang, Yang, et al., 2020).”
• Dreamlet-based interpolation using POCS method (2014, methodology): “The POCS method was introduced into irregular seismic data interpolation by Abma and Kabir (Abma and Kabir, 2006), however his method is time of Petroleum Resources and , China.”
• Multidimensional simultaneous random plus erratic noise attenuation and interpolation for seismic data by joint low-rank and sparse inversion (2015, methodology): “More specific methods addressing the problem of recovery from incomplete data include Fourier-based techniques such as the minimum weighted-norm interpolation (MWNI) (Liu and Sacchi, 2004; Trad, 2009) and the antileakage Fourier transform (ALFT), which handles irregularly sampled data by honoring th”
• Simply denoise: Wavefield reconstruction via jittered undersampling (2008, background): “Jittered undersampling differentiates itself from random undersampling according to a discrete uniform distribution, which also creates favorable recovery conditions (Xu et al., 2005; Abma and Kabir, 2006; Zwartjes and Sacchi, 2007), by controlling the maximum gap in the acquired data.”
• Seislet transform and seislet frame (2010, methodology): “…for seismic data regularization, such as the anti-leakage Fourier transform (Xu et al., 2005), the Fourier reconstruction method (Zwartjes and Gisolf, 2006, 2007; Zwartjes and Sacchi, 2007), or POCS (Abma and Kabir, 2006) rely on the ability to represent signals sparsely in the transform domain.”