Articles | Volume 14, issue 1
https://doi.org/10.5194/essd-14-381-2022
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, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko

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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. 
Athens, N. and Caers, J.: Stochastic Inversion of Gravity Data Accounting for Structural Uncertainty, Math. Geosci., https://doi.org/10.1007/s11004-021-09978-2, 2021. 
Caumon, G.: Towards stochastic time-varying geological modeling, Math. Geosci., 42, 555–569, 2010. 
Cherpeau, N., Caumon, G., Caers, J., and Levy, B. E.: Method for Stochastic Inverse Modeling of Fault Geometry and Connectivity Using Flow Data, Math. Geosci., 44, 147–168, 2012. 
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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.
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