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.
Data description paper
| 
01 Feb 2022
Data description paper |
| 01 Feb 2022
| 01 Feb 2022
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
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Cited
10 citations as recorded by crossref.
- Volcano Monitoring With Magnetic Measurements: A Simulation of Eruptions at Axial Seamount, Kīlauea, Bárðarbunga, and Mount Saint Helens J. Biasi et al. 10.1029/2022GL100006
- DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network Z. Bi et al. 10.5194/gmd-15-6841-2022
- Automatic reconstruction method of 3D geological models based on deep convolutional generative adversarial networks Z. Yang et al. 10.1007/s10596-022-10152-8
- Towards automatic and rapid 3D geological modelling of urban sedimentary strata from a large amount of borehole data using a parallel solution of implicit equations X. Wang et al. 10.1007/s12145-023-01164-8
- The 4D reconstruction of dynamic geological evolution processes for renowned geological features J. Guo et al. 10.5194/gmd-17-847-2024
- ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation H. Gao et al. 10.5194/gmd-16-2495-2023
- Stochastic inversion of geophysical data by a conditional variational autoencoder W. McAliley & Y. Li 10.1190/geo2023-0147.1
- Integration of automatic implicit geological modelling in deterministic geophysical inversion J. Giraud et al. 10.5194/se-15-63-2024
- Sensing prior constraints in deep neural networks for solving exploration geophysical problems X. Wu et al. 10.1073/pnas.2219573120
- 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 J. Guo et al. 10.5194/gmd-17-957-2024
10 citations as recorded by crossref.
- Volcano Monitoring With Magnetic Measurements: A Simulation of Eruptions at Axial Seamount, Kīlauea, Bárðarbunga, and Mount Saint Helens J. Biasi et al. 10.1029/2022GL100006
- DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network Z. Bi et al. 10.5194/gmd-15-6841-2022
- Automatic reconstruction method of 3D geological models based on deep convolutional generative adversarial networks Z. Yang et al. 10.1007/s10596-022-10152-8
- Towards automatic and rapid 3D geological modelling of urban sedimentary strata from a large amount of borehole data using a parallel solution of implicit equations X. Wang et al. 10.1007/s12145-023-01164-8
- The 4D reconstruction of dynamic geological evolution processes for renowned geological features J. Guo et al. 10.5194/gmd-17-847-2024
- ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation H. Gao et al. 10.5194/gmd-16-2495-2023
- Stochastic inversion of geophysical data by a conditional variational autoencoder W. McAliley & Y. Li 10.1190/geo2023-0147.1
- Integration of automatic implicit geological modelling in deterministic geophysical inversion J. Giraud et al. 10.5194/se-15-63-2024
- Sensing prior constraints in deep neural networks for solving exploration geophysical problems X. Wu et al. 10.1073/pnas.2219573120
- 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 J. Guo et al. 10.5194/gmd-17-957-2024
Latest update: 23 Apr 2024
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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