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Preprints
https://doi.org/10.5194/essd-2020-22
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2020-22
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 Apr 2020

16 Apr 2020

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A revised version of this preprint is currently under review for the journal ESSD.

Deep-sea sediments of the global ocean

Markus Diesing Markus Diesing
  • Geological Survey of Norway (NGU), Postal Box 6315 Torgarden, 7491 Trondheim, Norway

Abstract. Although the deep-sea floor accounts for more than 70 % of the Earth's surface, there has been little progress in relation to deriving maps of seafloor sediment distribution based on transparent, repeatable and automated methods such as machine learning. A new digital map of the spatial distribution of seafloor lithologies in the deep sea below 500 m water depth is presented to address this shortcoming. The lithology map is accompanied by estimates of the probability of the most probable class, which may be interpreted as a spatially-explicit measure of confidence in the predictions, and probabilities for the occurrence of seven lithology classes (Calcareous sediment, Clay, Diatom ooze, Lithogenous sediment, Mixed calcareous-siliceous ooze, Radiolarian ooze and Siliceous mud). These map products were derived by the application of the Random Forest machine learning algorithm to a homogenised dataset of seafloor lithology samples and global environmental predictor variables that were selected based on the current understanding of the controls on the spatial distribution of deep-sea sediments. The overall accuracy of the lithology map is 69.5 %, with 95 % confidence limits of 67.9 % and 71.1 %. It is expected that the map products are useful for various purposes including, but not limited to, teaching, management, spatial planning, design of marine protected areas and as input for global spatial predictions of marine species distributions and seafloor sediment properties. The map products are available at https://doi.org/10.1594/PANGAEA.911692 (Diesing, 2020).

Markus Diesing

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

Markus Diesing

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Latest update: 30 Sep 2020
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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.
A new digital map of the sediment types covering the bottom of the ocean has been created....
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