Articles | Volume 22, issue 17
https://doi.org/10.5194/bg-22-4309-2025
https://doi.org/10.5194/bg-22-4309-2025
Research article
 | 
01 Sep 2025
Research article |  | 01 Sep 2025

Estimating the variability of deep-ocean particle flux collected by sediment traps using satellite data and machine learning

Théo Picard, Chelsey A. Baker, Jonathan Gula, Ronan Fablet, Laurent Mémery, and Richard Lampitt

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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 egusphere-2024-3292', Anonymous Referee #1, 20 Jan 2025
  • RC2: 'Comment on egusphere-2024-3292', Anonymous Referee #2, 10 Mar 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (25 Apr 2025) by Peter Landschützer
AR by Théo Picard on behalf of the Authors (16 May 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (26 May 2025) by Peter Landschützer
RR by Anonymous Referee #2 (06 Jun 2025)
ED: Publish as is (10 Jun 2025) by Peter Landschützer
AR by Théo Picard on behalf of the Authors (10 Jun 2025)
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Short summary
Ocean sediment traps measure the sequestrated sinking organic carbon. While sinking, the particles are affected by local currents, which presents a challenge with regard to linking the deep flux with the surface. We present a machine learning tool that predicts the source location of the sinking particles based on satellite data. The predictions demonstrate a stronger correlation between surface and deep carbon fluxes, allowing a more comprehensive understanding of the deep carbon sequestration drivers.
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