Articles | Volume 16, issue 6
Ocean Sci., 16, 1367–1383, 2020
https://doi.org/10.5194/os-16-1367-2020
Ocean Sci., 16, 1367–1383, 2020
https://doi.org/10.5194/os-16-1367-2020

Research article 11 Nov 2020

Research article | 11 Nov 2020

Random noise attenuation of sparker seismic oceanography data with machine learning

Hyunggu Jun et al.

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Hyeong-Tae Jou on behalf of the Authors (26 Jun 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (03 Jul 2020) by John M. Huthnance
RR by Anonymous Referee #1 (13 Jul 2020)
RR by Richard Hobbs (14 Jul 2020)
ED: Publish subject to minor revisions (review by editor) (15 Jul 2020) by John M. Huthnance
AR by Hyeong-Tae Jou on behalf of the Authors (20 Aug 2020)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (03 Sep 2020) by John M. Huthnance
AR by Hyeong-Tae Jou on behalf of the Authors (11 Sep 2020)  Author's response    Manuscript
ED: Publish subject to technical corrections (25 Sep 2020) by John M. Huthnance
AR by Hyeong-Tae Jou on behalf of the Authors (02 Oct 2020)  Author's response    Manuscript
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Short summary
Seismic oceanography acquires water column reflections by seismic exploration. The use of a high-frequency seismic source such as sparker can enhance the resolution of reflection image but suffers from a low signal-to-noise ratio problem. In this study, we applied a machine learning to remove the random noise in water column seismic section. We constructed appropriate training data and showed that the machine learning can successfully remove the random noise in the water column seismic section.