Articles | Volume 20, issue 5
https://doi.org/10.5194/os-20-1149-2024
https://doi.org/10.5194/os-20-1149-2024
Research article
 | 
19 Sep 2024
Research article |  | 19 Sep 2024

Predicting particle catchment areas of deep-ocean sediment traps using machine learning

Théo Picard, Jonathan Gula, Ronan Fablet, Jeremy Collin, and Laurent Mémery

Data sets

Data for learning-based prediction of the particles catchment area of deep ocean sediment traps Théo Picard et al. https://doi.org/10.17882/97556

Model code and software

SPARO Théo Picard https://github.com/TheoPcrd/SPARO

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Short summary
The biological carbon pump plays a key role in the climate system. Plankton absorb and transform CO2 into organic carbon, forming particles that sink to the ocean floor. Sediment traps catch these particles and measure the carbon stored in the abyss. However, the particles' surface origin is unknown as ocean currents alter their paths. Here, we train an AI model to predict the origin of these particles. This new tool enables a better link between deep-ocean observations and satellite images.