Journal of Micropalaeontology
Journal of Micropalaeontology
JM
Articles | Volume 39, issue 2
Articles | Volume 39, issue 2
https://doi.org/10.5194/jm-39-183-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/jm-39-183-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 39, issue 2
Research article
 | 
15 Oct 2020
Research article |  | 15 Oct 2020

Automated analysis of foraminifera fossil records by image classification using a convolutional neural network

Ross Marchant, Martin Tetard, Adnya Pratiwi, Michael Adebayo, and Thibault de Garidel-Thoron

Viewed

Total article views: 4,921 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,832 1,010 79 4,921 90 137
  • HTML: 3,832
  • PDF: 1,010
  • XML: 79
  • Total: 4,921
  • BibTeX: 90
  • EndNote: 137
Views and downloads (calculated since 15 Oct 2020)
Cumulative views and downloads (calculated since 15 Oct 2020)

Viewed (geographical distribution)

Total article views: 4,580 (including HTML, PDF, and XML) Thereof 4,571 with geography defined and 9 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Nov 2025
Download
Short summary
Foraminifera are marine microorganisms with a calcium carbonate shell. Their fossil remains build up on the seafloor, forming kilometres of sediment over time. From analysis of the foraminiferal record we can estimate past climate conditions and the geological history of the Earth. We have developed an artificial intelligence system for automatically identifying foraminifera species, replacing the time-consuming manual approach and thus helping to make these analyses more efficient and accurate.
Read more
Share