Articles | Volume 14, issue 1
Data description paper
01 Feb 2022
Data description paper |  | 01 Feb 2022

Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications

Mark Jessell, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko


Total article views: 3,145 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,243 822 80 3,145 62 57
  • HTML: 2,243
  • PDF: 822
  • XML: 80
  • Total: 3,145
  • BibTeX: 62
  • EndNote: 57
Views and downloads (calculated since 28 Sep 2021)
Cumulative views and downloads (calculated since 28 Sep 2021)

Viewed (geographical distribution)

Total article views: 3,145 (including HTML, PDF, and XML) Thereof 2,965 with geography defined and 180 with unknown origin.
Country # Views %
  • 1


Latest update: 02 Mar 2024
Short summary
To robustly train and test automated methods in the geosciences, we need to have access to large numbers of examples where we know the answer. We present a suite of synthetic 3D geological models with their gravity and magnetic responses that allow researchers to test their methods on a whole range of geologically plausible models, thus overcoming one of the fundamental limitations of automation studies.
Final-revised paper