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

Data sets

Catchment areas of PAP sediment traps at 3000m depth from 2000 to 2022 T. Picard https://doi.org/10.17882/102535

Model code and software

SPARO: v2.0.0 T. Picard https://doi.org/10.5281/zenodo.13899396

Video supplement

Video for learning-based prediction of the particles catchment area of PAP sediment traps Théo Picard https://doi.org/10.5281/zenodo.10261827

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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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