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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Cited articles

Astfalck, L., Cripps, E., Gosling, J. P., Hodkiewicz, M., and Milne, I.: Expert elicitation of directional metocean parameters, Ocean Eng., 161, 268–276, 2018. 
Astfalck, L., Cripps, E., Gosling, J. P., and Milne, I.: Emulation of vessel motion simulators for computationally efficient uncertainty quantification, Ocean Eng., 172, 726–736, 2019. 
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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.