Denoising seismic data using the nonlocal means algorithm

By D. Bonar, Mauricio D. Sacchi. 2012

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Structure-oriented singular value decomposition for random noise attenuation of seismic data (2015, background): “The second is based on the statistical property of seismic profile, such as the mean filter (Bonar and Sacchi, 2012), median filter (Liu, 2013; Chen, 2014; Chen et al., 2014b).”
Deep learning for denoising (2018, methodology): “2006), and nonlocal mean (NLM) (Bonar and Sacchi, 2012) methods for comparison.”
Interpolation and denoising of high-dimensional seismic data by learning a tight frame (2015, background): “For instance, assuming data redundancy, non-local means algorithm (David and Sacchi, 2012) denoises each sample within an image by utilizing other similar samples.”
Seismic Signal Denoising and Decomposition Using Deep Neural Networks (2018, background): “Due to these limitations, numerous efforts have been made to develop more effective noise suppression in seismic data e.g. [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19].”