Nonequispaced curvelet transform for seismic data reconstruction: A sparsity-promoting approach

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Multidimensional simultaneous random plus erratic noise attenuation and interpolation for seismic data by joint low-rank and sparse inversion (2015, methodology): “…the real spatial coordinates (Xu et al., 2005, 2010; Poole, 2010), projection onto convex sets (POCS) (Abma and Kabir, 2006), and techniques based on local transforms such as the Radon transform (Trad et al., 2002) or the curvelet transform (Herrmann and Hennenfent, 2008; Hennenfent et al., 2010).”
Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction (2013, methodology): “Transform-based methods exploit the characteristics of the signal in an auxiliary domain, sparsity being the most common (Liu and Sacchi, 2004; Hennenfent et al., 2010).”
A fast reduced-rank interpolation method for prestack seismic volumes that depend on four spatial dimensions (2013, background): “…(Duijndam et al., 1999; Hindriks and Duijndam 2000; Liu and Sacchi, 2004; Xu et al., 2005; Zwartjes and Sacchi, 2007; Trad, 2009; Curry, 2010), and localized transforms (Herrmann andHennenfent, 2008; Sacchi et al., 2009; Hennenfent et al., 2010; Naghizadeh and Sacchi, 2010; Wang et al., 2010).”