Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach

ABSTRACT Random noise attenuation is an essential step in seismic data processing for improving seismic data quality and signal‐to‐noise ratio. We adopt an unsupervised machine learning approach to attenuate random noise via signal reconstruction strategy. This approach can be accomplished in the fo...

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Bibliographic Details
Published in:	Geophysical Prospecting Vol. 69; no. 5; pp. 984 - 1002
Main Authors:	Gao, Yang, Zhao, Pingqi, Li, Guofa, Li, Hao
Format:	Journal Article
Language:	English
Published:	Houten Wiley Subscription Services, Inc 01.06.2021
Subjects:	Adaptive algorithms Attenuation Data processing Distance Feasibility studies Machine learning Neural networks Noise Noise reduction Objective function Random noise Seismic data processing Signal processing Signal quality Signal reconstruction Unsupervised learning
ISSN:	0016-8025, 1365-2478
Online Access:	Get full text
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