Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-3367-2020
https://doi.org/10.5194/essd-12-3367-2020
Data description paper
 | 
11 Dec 2020
Data description paper |  | 11 Dec 2020

Deep-sea sediments of the global ocean

Markus Diesing

Data sets

Deep-sea sediments of the global ocean mapped with Random Forest machine learning algorithm Markus Diesing https://doi.org/10.1594/PANGAEA.911692

Executable research compendium (ERC)

Global Deep-Sea Sediments Markus Diesing and Daniel Nüst https://o2r.uni-muenster.de/#/erc/GWME2voTDb5oeaQFuTWMCEMveKS1MiXm

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Short summary
A new digital map of the sediment types covering the bottom of the ocean has been created. Direct observations of the seafloor sediments are few and far apart. Therefore, machine learning was used to fill those gaps between observations. This was possible because known relationships between sediment types and the environment in which they form (e.g. water depth, temperature, and salt content) could be exploited. The results are expected to provide important information for marine research.
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