Articles | Volume 14, issue 1
https://doi.org/10.5194/essd-14-381-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-381-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Jiateng Guo
College of Resources and Civil Engineering, Northeastern University,
Shenyang, China
Yunqiang Li
College of Resources and Civil Engineering, Northeastern University,
Shenyang, China
Mark Lindsay
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Mineral Resources, Commonwealth Scientific and Industrial Research Organisation, Australian Resources Research Centre, Kensington, Australia
Richard Scalzo
School of Mathematics and Statistics, University of Sydney, Sydney,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Jérémie Giraud
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
GeoRessources, Université de Lorraine, CNRS, 54000 Nancy, France
Guillaume Pirot
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Ed Cripps
Department of Mathematics and Statistics, The University of Western
Australia, Perth, Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Vitaliy Ogarko
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, The University of Western Australia, Perth,
Australia
ARC Centre for Data Analytics for Resources and Environments (DARE), The University of Western Australia, Perth, Australia
Data sets
Loop3D/noddyverse: Noddyverse 1.0 Mark Jessell https://doi.org/10.5281/zenodo.4589883
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.
To robustly train and test automated methods in the geosciences, we need to have access to large...
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