Articles | Volume 18, issue 3
https://doi.org/10.5194/tc-18-1241-2024
https://doi.org/10.5194/tc-18-1241-2024
Research article
 | 
19 Mar 2024
Research article |  | 19 Mar 2024

Deep clustering in subglacial radar reflectance reveals subglacial lakes

Sheng Dong, Lei Fu, Xueyuan Tang, Zefeng Li, and Xiaofei Chen

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2023-62', Michael Wolovick, 17 Jul 2023
    • AC1: 'Response to Michael Wolovick (RC1)', Sheng Dong, 12 Sep 2023
  • RC2: 'Comment on tc-2023-62', Veronica Tollenaar, 18 Jul 2023
    • AC2: 'Response to Veronica Tollenaar (RC2)', Sheng Dong, 12 Sep 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to minor revisions (review by editor) (24 Oct 2023) by Huw Horgan
AR by Sheng Dong on behalf of the Authors (12 Nov 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (07 Dec 2023) by Huw Horgan
AR by Sheng Dong on behalf of the Authors (17 Dec 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (16 Jan 2024) by Huw Horgan
AR by Sheng Dong on behalf of the Authors (21 Jan 2024)
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Short summary
Subglacial lakes are a unique environment at the bottom of ice sheets, and they have distinct features in radar echo images that allow for visual detection. In this study, we use machine learning to analyze radar reflection waveforms and identify candidate subglacial lakes. Our approach detects more lakes than known inventories and can be used to expand the subglacial lake inventory. Additionally, this analysis may also provide insights into interpreting other subglacial conditions.