Articles | Volume 14, issue 1
Earth Syst. Sci. Data, 14, 381–392, 2022
https://doi.org/10.5194/essd-14-381-2022
Earth Syst. Sci. Data, 14, 381–392, 2022
https://doi.org/10.5194/essd-14-381-2022

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 et al.

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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
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
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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.