Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis

By V. Oropeza, Mauricio D. Sacchi. 2011

688 citations recorded by the source corpus.

Subfields: Applied Mathematics, Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/5e60571f99f7bb4be473296de6df786a69bec7d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Mathematics

In Computer Vision and Pattern Recognition

In Geophysics

Citations in the corpus

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].”
Tensor completion based on nuclear norm minimization for 5D seismic data reconstruction (2013, methodology): “Other type of methods utilize the lowrank nature of seismic data embedded in Hankel matrices (Trickett et al., 2010; Oropeza and Sacchi, 2011).”
Seismic data reconstruction via matrix completion (2013, methodology): “The technology of rank reduction has been used for data reconstruction in MSSA based methods in [15], but here we start from different theory and algorithms.”
5D seismic data completion and denoising using a novel class of tensor decompositions (2015, methodology): “Other variations enforce the low rank through less expensive matrix decomposition, completely avoiding the calculation of the SVD (Gao et al., 2011, 2013; Kumar et al., 2013) or applying the SVD to a randomized subset of the original matrix (Oropeza and Sacchi, 2010, 2011).”
Seismic data restoration via data-driven tight frame (2014, positive): “Oropeza and Sacchi (2011) reorganize the seismic data into a Hankel matrix and then use multichannel singular spectrum analysis to solve the rank-reduction problem.”
Multidimensional simultaneous random plus erratic noise attenuation and interpolation for seismic data by joint low-rank and sparse inversion (2015, methodology): “…Lanczos iteration (Golub and Kahan, 1965; Golub and Van Loan, 2013) such as in Trickett (2003), Korobeynikov (2010), Gao et al. (2013), or randomized methods on which we will focus, already used in Oropeza and Sacchi (2011), although without the advantages provided by fast matrixvector products.”
Damped multichannel singular spectrum analysis for 3D random noise attenuation (2015, methodology): “The MSSA (Oropeza and Sacchi, 2011) operates on the data in the following way: first, MSSA transforms Dtime(x, y, t) into Dfreq(x, y, w)(w = 1 · · ·Nw) with complex values in the frequency domain.”
Structure-oriented singular value decomposition for random noise attenuation of seismic data (2015, methodology): “They require an eigen-decomposition of the spectral matrix of data, which is connected with Cadzow filtering (Cadzow, 1988) and multichannel singular spectrum analysis (MSSA) (Vautard et al., 1992; Oropeza and Sacchi, 2011).”