Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 9
Articles | Volume 18, issue 9
https://doi.org/10.5194/gmd-18-2521-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-2521-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 9
Development and technical paper
 | 
08 May 2025
Development and technical paper |  | 08 May 2025

NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks

Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann

Viewed

Total article views: 1,213 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
581 277 355 1,213 33 39
  • HTML: 581
  • PDF: 277
  • XML: 355
  • Total: 1,213
  • BibTeX: 33
  • EndNote: 39
Views and downloads (calculated since 04 Jun 2024)
Cumulative views and downloads (calculated since 04 Jun 2024)

Viewed (geographical distribution)

Total article views: 1,213 (including HTML, PDF, and XML) Thereof 1,213 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 09 May 2025
Download
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Read more
Share