Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction

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Robust reduced-rank filtering for erratic seismic noise attenuation (2015, background): “…io n su bj ec t t o SE G li ce ns e or c op yr ig ht ; s ee T er m s of U se a t h ttp :// lib ra ry .s eg .o rg / explored for matrix and tensor completion in applied mathematics (Candès et al., 2011; Gandy et al., 2011; Tao and Yuan, 2011) and in seismic data processing (Kreimer et al., 2013).”
Efficient matrix completion for seismic data reconstruction (2015, methodology): “We demonstrate the superior computational performances of both of these approaches compared to the tensor-based interpolation of Kreimer et al. (2013) as well as traditional curvelet-based approaches on realistic 2D and 3D seismic data sets.”
5D seismic data completion and denoising using a novel class of tensor decompositions (2015, methodology): “Nevertheless, it is important to note that because we do not compare our method with that in Kreimer et al. (2013), we cannot claim that our approach is superior to theirs.”
Multidimensional simultaneous random plus erratic noise attenuation and interpolation for seismic data by joint low-rank and sparse inversion (2015, methodology): “In the last category, we can differentiate eigenimage filtering (Trickett, 2003), Cadzow/singular spectrum analysis (SSA) filtering (Trickett, 2008; Sacchi, 2009), and tensor methods (Kreimer and Sacchi, 2012; Kreimer et al., 2013; Trickett et al., 2013; Da Silva and Herrmann, 2014).”