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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-304', Anonymous Referee #1, 26 Oct 2021
    • AC1: 'Reply on RC1', Mark Jessell, 17 Dec 2021
  • RC2: 'Comment on essd-2021-304', Jiajia Sun, 01 Nov 2021
    • AC2: 'Reply on RC2', Mark Jessell, 17 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Mark Jessell on behalf of the Authors (17 Dec 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (23 Dec 2021) by Jens Klump
AR by Mark Jessell on behalf of the Authors (23 Dec 2021)
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