Biogeosciences
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Articles | Volume 22, issue 17
Articles | Volume 22, issue 17
https://doi.org/10.5194/bg-22-4309-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-4309-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 22, issue 17
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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Latest update: 10 May 2026
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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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