Coherent and random noise attenuation using the curvelet transform

By Ramesh Neelamani, A. Baumstein, D. Gillard, M. Hadidi, W. Soroka. 2008

253 citations recorded by the source corpus.

Subfields: Geophysics, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/2638638c37ea33a902d3e135ec60d46288855976

How researchers see this paper

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

In Geophysics

1. Jianwei Ma
2. S. M. Mousavi
3. Gregory C. Beroza
4. Yangkang Chen
5. Weiqiang Zhu
6. Yang Liu
7. Cai Liu
8. Shuwei Gan

In Ocean Engineering

1. Yangkang Chen
2. Yang Liu
3. Cai Liu
4. Weilin Huang
5. Shuwei Gan
6. Jianwei Ma
7. S. M. Mousavi
8. Gregory C. Beroza

Citations in the corpus

• Structure-oriented singular value decomposition for random noise attenuation of seismic data (2015, background): “The fourth is based on a transformed domain thresholding strategy (Neelamani et al., 2008; Fomel and Liu, 2010; Chen et al., 2014a).”
• The Curvelet Transform (2010, methodology): “[52] proposed a 3-D curvelet-based effective approach to attenuate random and coherent noise in a 3-D data”
• Signal and noise separation in prestack seismic data using velocity-dependent seislet transform (2015, methodology): “Some transform methods were also used to deal with seismic random noise, e.g., the discrete cosine transform (Lu and Liu, 2007), the curvelet transform (Neelamani et al., 2008), and the seislet transform (Fomel and Liu, 2010).”
• Damped multichannel singular spectrum analysis for 3D random noise attenuation (2015, background): “…2013; Chen, 2015; Gan et al., 2016), Karhunen-Loeve 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…”
• Seismic Signal Denoising and Decomposition Using Deep Neural Networks (2018, methodology): “…transform, such as a wavelet transform [22, 23, 24, 25, 26, 27], Short Time Fourier Transform (STFT) [28], S-transform [29], curvelet transform [30, 31, 32], dreamlet transform [33], contourlet zhuwq@stanford.edu transform [34], shearlet transform [35], empirical mode decomposition [36, 37, 38,…”