Biogeosciences
Biogeosciences
BG
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

Viewed

Total article views: 1,885 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,673 179 33 1,885 25 50
  • HTML: 1,673
  • PDF: 179
  • XML: 33
  • Total: 1,885
  • BibTeX: 25
  • EndNote: 50
Views and downloads (calculated since 05 Dec 2024)
Cumulative views and downloads (calculated since 05 Dec 2024)

Viewed (geographical distribution)

Total article views: 1,885 (including HTML, PDF, and XML) Thereof 1,870 with geography defined and 15 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Sep 2025
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint