Data for learning-based prediction of the particles catchment area of deep ocean sediment traps

In this study, we conducted a series of numerical Lagrangian experiments in the Porcupine Abyssal Plain region of the North Atlantic and developed a machine learning approach to predict the surface origin of particles trapped in a deep sediment trap. The data contain :

- I. Probability density function of the particles position from the Lagrangian experiments.

-II. The dynamic variables (temperature, vorticity, u, v, sea surface height) associated with each Lagrangian experiments and used for the training/ testing.

-III. The saved parameters and logs of the machine learning models.

-IV. Some processed data such as kinetic energy and okubo-weiss parameter used for analysis.

In this study, we conducted a series of numerical Lagrangian experiments in the Porcupine Abyssal Plain region of the North Atlantic and developed a machine learning approach to predict the surface origin of particles trapped in a deep sediment trap. The data contain :

- I. Probability density function of the particles position from the Lagrangian experiments.

-II. The dynamic variables (temperature, vorticity, u, v, sea surface height) associated with each Lagrangian experiments and used for the training/ testing.

-III. The saved parameters and logs of the machine learning models.

-IV. Some processed data such as kinetic energy and okubo-weiss parameter used for analysis.

Disciplines

Physical oceanography, Biological oceanography

Keywords

Biological carbon pump, Sediment trap, Machine learning, Lagrangian particles, Porcupine Abyssal Plain

Location

52N, 45S, -24.5E, -8.5W

Devices

Numerical ocean simulation : CROCO

Lagrangian experiment : Pyticles

Machine-learning : Pytorch

Data

FileSizeFormatProcessingAccess
data
48 GoNetCDFQuality controlled data
How to cite
Picard Théo, Gula Jonathan, Fablet Ronan, Memery Laurent, Collin Jéremy (2023). Data for learning-based prediction of the particles catchment area of deep ocean sediment traps. SEANOE. https://doi.org/10.17882/97556
In addition to properly cite this dataset, it would be appreciated that the following work(s) be cited too, when using this dataset in a publication :
Picard Théo, Gula Jonathan, Fablet Ronan, Collin Jeremy, Mémery Laurent (2023). Learning-based prediction of the particles catchment area of deep ocean sediment traps. https://doi.org/10.5194/egusphere-2023-2777

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