Title
Self-Supervised Ground-Roll Noise Attenuation Using Self-Labeling and Paired Data Synthesis
Abstract
Seismic exploration is a complex process that depends on different sources of information. An essential one is seismic imaging, and much of its interpretation performance relies on high-quality processing, which is currently still very dependent on prone-to-error human mediation. Automation of such processing steps is necessary to reduce the amount of time to treat seismic data—usually months—and ...
Year
DOI
Venue
2021
10.1109/TGRS.2020.3029914
IEEE Transactions on Geoscience and Remote Sensing
Keywords
DocType
Volume
Training,Attenuation,Noise reduction,Pipelines,Noise measurement,Data models,Geology
Journal
59
Issue
ISSN
Citations 
8
0196-2892
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Dário Augusto B. Oliveira1236.45
Daniil G. Semin200.34
Semen Zaytsev300.34