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 funct...

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Bibliographic Details
Main Authors: Picard, Théo, Gula, Jonathan, Fablet, Ronan, Memery, Laurent, Collin, Jéremy
Format: Dataset
Language:unknown
Published: SEANOE 2023
Subjects:
Biological carbon pump
Sediment trap
Machine learning
Lagrangian particles
Porcupine Abyssal Plain
North Atlantic
Online Access:https://dx.doi.org/10.17882/97556
https://www.seanoe.org/data/00864/97556/
Description
Summary: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. ...

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