Random noise attenuation via the randomized canonical polyadic decomposition

ABSTRACT Tensor algebra provides a robust framework for multi‐dimensional seismic data processing. A low‐rank tensor can represent a noise‐free seismic data volume. Additive random noise will increase the rank of the tensor. Hence, tensor rank‐reduction techniques can be used to filter random noise....

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Bibliographic Details
Published in:	Geophysical Prospecting Vol. 68; no. 3; pp. 872 - 891
Main Authors:	Gao, Wenlei, Sacchi, Mauricio D.
Format:	Journal Article
Language:	English
Published:	Houten Wiley Subscription Services, Inc 01.03.2020
Subjects:	Additives Algorithms Attenuation Computing costs Data processing Decomposition Dimensional analysis Filtration Least squares method Mathematical analysis Multi‐linear algebra Noise Noise reduction Random noise Randomization Seismic data processing Signal processing Singular value decomposition Spectrum analysis Tensors
ISSN:	0016-8025, 1365-2478
Online Access:	Get full text
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