Seismic data interpolation and denoising in the frequency-wavenumber domain

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Deep learning for denoising (2018, background): “Generally, noise is assumed as Gaussian distributed while the data can satisfy different assumptions, such as linear events (Spitz, 1991; Naghizadeh, 2012), sparsity (Zhang and Ulrych, 2003; Hennenfent and Herrmann, 2006; Fomel and Liu, 2013; Yu et al., 2015), and low-rank assumptions (Trickett,…”
Dreamlet-based interpolation using POCS method (2014, methodology): “…divided into three categories (Gao et al., 2013): First, methods based onmathematical transform and signal analysis (Herrmann and Hennenfent, 2008; Naghizadeh and Innanen, 2011; Naghizadeh and Sacchi, 2010; Trad et al., 2002; Wang et al., 2010); second, prediction filter based methods (Naghizadeh…”
Seismic Signal Denoising and Decomposition Using Deep Neural Networks (2018, background): “ly alter the waveform shape such that it degrades the analysis that follows. 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]. Methods based on time-frequency”