Skip to Main content Skip to Navigation
Journal articles

Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications

Abstract : Unlike some other well-known challenges such as facial recognition, where machine learning and inversion algorithms are widely developed, the geosciences suffer from a lack of large, labelled data sets that can be used to validate or train robust machine learning and inversion schemes. Publicly available 3D geological models are far too restricted in both number and the range of geological scenarios to serve these purposes. With reference to inverting geophysical data this problem is further exacerbated as in most cases real geophysical observations result from unknown 3D geology, and synthetic test data sets are often not particularly geological or geologically diverse. To overcome these limitations, we have used the Noddy modelling platform to generate 1 million models, which represent the first publicly accessible massive training set for 3D geology and resulting gravity and magnetic data sets (https://doi.org/10.5281/zenodo.4589883, Jessell, 2021). This model suite can be used to train machine learning systems and to provide comprehensive test suites for geophysical inversion. We describe the methodology for producing the model suite and discuss the opportunities such a model suite affords, as well as its limitations, and how we can grow and access this resource.
Document type :
Journal articles
Complete list of metadata

https://hal-insu.archives-ouvertes.fr/insu-03665913
Contributor : Nathalie POTHIER Connect in order to contact the contributor
Submitted on : Thursday, May 12, 2022 - 11:00:49 AM
Last modification on : Wednesday, May 25, 2022 - 8:24:38 AM

File

essd-14-381-2022.pdf
Publisher files allowed on an open archive

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Mark Jessell, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, et al.. Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications. Earth System Science Data, 2022, 14, pp.381-392. ⟨10.5194/essd-14-381-2022⟩. ⟨insu-03665913⟩

Share

Metrics

Record views

32

Files downloads

5