A tensor higher-order singular value decomposition for prestack seismic data noise reduction and interpolation

By Nadia Kreimer, Mauricio D. Sacchi. 2012

198 citations recorded by the source corpus.

Subfields: Geophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/42a1d21f7cc29ce9d192db051faf87b485cfd7dc

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In Geophysics

In Nuclear and High Energy Physics

Citations in the corpus

Interpolation and denoising of high-dimensional seismic data by learning a tight frame (2015, methodology): “Rank-reduced methods (Trickett et al., 2010; Kreimer and Sacchi, 2012; Gao et al., 2013; Ma, 2013; Yang et al., 2013) assume that the seismic data itself or constant-frequency slices of the fully sampled data can be reformulated to a low-rank matrix for reconstruction purpose.”
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).”
Deep learning for denoising (2018, background): “, 2015), and low-rank assumptions (Trickett, 2008; Kreimer and Sacchi, 2012), such that the data can be distinguished from the noise with specially designed algorithms.”
Parallel matrix factorization for low-rank tensor completion (2013, unknown): “Higher-order tensor arises in many applications such as 3D image reconstruction [22], video inpainting [19], hyperspectral data recovery [14, 28], higher-order web link analysis [10], personalized web search [23], and seismic data reconstruction [11].”
Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction (2013, methodology): “We perform some simple tests to visualize the difference between the HOSVD-based reconstruction algorithm of Kreimer and Sacchi (2012) and the presented ADMM-based technique.”
5D seismic data completion and denoising using a novel class of tensor decompositions (2015, methodology): “Some of the current approaches, such as in Trad (2009) and Kreimer and Sacchi (2012b), work the f-x domain in which the data completion is carried out separately over the 4D slices corresponding to each frequency.”
Seismic data restoration via data-driven tight frame (2014, methodology): “Kreimer and Sacchi (2012) propose a tensor rankreduction method by higher-order SVD for prestack seismic data interpolation.”
Efficient matrix completion for seismic data reconstruction (2015, methodology): “More recently, rank-minimization-based techniques have been applied to interpolating seismic data (Trickett et al., 2010; Oropeza and Sacchi, 2011; Kreimer and Sacchi, 2012b,a; Yang et al., 2013).”