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

Related authors

GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973,,, 2024
Short summary
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data, to recover density, susceptibility and magnetisation vector, with topography and wavelet compression
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
EGUsphere,,, 2023
Short summary
Utilisation of probabilistic magnetotelluric modelling to constrain magnetic data inversion: proof-of-concept and field application
Jérémie Giraud, Hoël Seillé, Mark D. Lindsay, Gerhard Visser, Vitaliy Ogarko, and Mark W. Jessell
Solid Earth, 14, 43–68,,, 2023
Short summary
loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification
Guillaume Pirot, Ranee Joshi, Jérémie Giraud, Mark Douglas Lindsay, and Mark Walter Jessell
Geosci. Model Dev., 15, 4689–4708,,, 2022
Short summary
Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662,,, 2022
Short summary

Related subject area

Geophysics and geodesy
TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)
Wenbin Tang, Ji Zhou, Jin Ma, Ziwei Wang, Lirong Ding, Xiaodong Zhang, and Xu Zhang
Earth Syst. Sci. Data, 16, 387–419,,, 2024
Short summary
Comprehensive data set of in situ hydraulic stimulation experiments for geothermal purposes at the Äspö Hard Rock Laboratory (Sweden)
Arno Zang, Peter Niemz, Sebastian von Specht, Günter Zimmermann, Claus Milkereit, Katrin Plenkers, and Gerd Klee
Earth Syst. Sci. Data, 16, 295–310,,, 2024
Short summary
An earthquake focal mechanism catalog for source and tectonic studies in Mexico from February 1928 to July 2022
Quetzalcoatl Rodríguez-Pérez and F. Ramón Zúñiga
Earth Syst. Sci. Data, 15, 4781–4801,,, 2023
Short summary
GPS displacement dataset for study of elastic surface mass variations
Athina Peidou, Donald Argus, Felix Landerer, David Wiese, and Matthias Ellmer
Earth Syst. Sci. Data Discuss.,,, 2023
Revised manuscript accepted for ESSD
Short summary
Global physics-based database of injection-induced seismicity
Iman R. Kivi, Auregan Boyet, Haiqing Wu, Linus Walter, Sara Hanson-Hedgecock, Francesco Parisio, and Victor Vilarrasa
Earth Syst. Sci. Data, 15, 3163–3182,,, 2023
Short summary

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.,, 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. 
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