Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4689-2022
© 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/gmd-15-4689-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
20 Jun 2022
Methods for assessment of models |
| 20 Jun 2022
| 20 Jun 2022
loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification
Guillaume Pirot
CORRESPONDING AUTHOR
The Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Perth, Australia
Mineral Exploration Cooperative Research Centre (MinEx CRC), School of Earth Sciences, University of Western Australia, Perth, Australia
Ranee Joshi
The Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Perth, Australia
Mineral Exploration Cooperative Research Centre (MinEx CRC), School of Earth Sciences, University of Western Australia, Perth, Australia
Jérémie Giraud
The Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Perth, Australia
Mineral Exploration Cooperative Research Centre (MinEx CRC), School of Earth Sciences, University of Western Australia, Perth, Australia
GeoRessources Lab, University of Lorraine, Nancy, France
Mark Douglas Lindsay
The Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Perth, Australia
Mineral Exploration Cooperative Research Centre (MinEx CRC), School of Earth Sciences, University of Western Australia, Perth, Australia
ARC Industrial Transformation and Training Centre
in Data Analytics for Resources and the Environment (DARE), Sydney, Australia
CSIRO Mineral Resources, Perth, Australia
Mark Walter Jessell
The Centre for Exploration Targeting, School of Earth Sciences, The University of Western Australia, Perth, Australia
Mineral Exploration Cooperative Research Centre (MinEx CRC), School of Earth Sciences, University of Western Australia, Perth, Australia
ARC Industrial Transformation and Training Centre
in Data Analytics for Resources and the Environment (DARE), Sydney, Australia
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Cited
17 citations as recorded by crossref.
- DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network Z. Bi et al. https://doi.org/10.5194/gmd-15-6841-2022
- Geological model calibration based on gradual deformation and connectivity function J. Jin et al. https://doi.org/10.1038/s41598-024-80363-9
- Enhanced Markov-type Categorical Prediction with geophysical soft constraints for hydrostratigraphic modeling L. Guo et al. https://doi.org/10.5194/hess-30-1421-2026
- Three-dimensional modeling of fault geological structure using generalized triangular prism element reconstruction H. Liu et al. https://doi.org/10.1007/s10064-023-03166-8
- A real-time geological sections generation method for geological 3D models based on per-pixel linked lists S. Li et al. https://doi.org/10.1080/17538947.2025.2501770
- Efficient Construction of Voxel Models for Ore Bodies Using an Improved Winding Number Algorithm and CUDA Parallel Computing L. Liu et al. https://doi.org/10.3390/ijgi12120473
- Co-simulation of continuous and categorical variables: application in the Shuiyindong gold deposit modeling J. Zeng et al. https://doi.org/10.3389/feart.2026.1749476
- Subsurface geometry of the Revell Batholith by constrained geophysical modelling, NW Ontario, Canada M. Mushayandebvu et al. https://doi.org/10.1016/j.acags.2023.100121
- 数据和知识融合的Bayesian-MCMC三维地质建模 L. Wang et al. https://doi.org/10.3799/dqkx.2023.069
- Variability of geological interpretations of a mineral deposit: Quantitative study and implications P. Marchal et al. https://doi.org/10.1016/j.oregeorev.2025.107010
- Pseudo trans-dimensional 3-D geometrical inversion: a proof of concept using gravity data J. Giraud et al. https://doi.org/10.1093/gji/ggaf501
- Geological realism in Fluvial facies modelling with GAN under variable depositional conditions C. Sun et al. https://doi.org/10.1007/s10596-023-10190-w
- Assessing geometrical uncertainties in geological interface models using Markov chain Monte Carlo sampling via abstract graph J. Huang et al. https://doi.org/10.1016/j.tecto.2023.230032
- Estimating relative uncertainty of geological 3D models with low density of input data in geologically complex regions F. Staněk et al. https://doi.org/10.1007/s12145-025-01778-0
- Visualization facilitates uncertainty evaluation of multiple-point geostatistical stochastic simulation Q. Huang et al. https://doi.org/10.1007/s44267-023-00016-9
- A Geological Modeling Workflow for Shale Reservoirs: A Case Study of the F2 Member in the Qintong Sag M. Han et al. https://doi.org/10.3390/app16041759
- Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions M. Lindsay et al. https://doi.org/10.1016/j.gsf.2022.101435
17 citations as recorded by crossref.
- DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network Z. Bi et al. https://doi.org/10.5194/gmd-15-6841-2022
- Geological model calibration based on gradual deformation and connectivity function J. Jin et al. https://doi.org/10.1038/s41598-024-80363-9
- Enhanced Markov-type Categorical Prediction with geophysical soft constraints for hydrostratigraphic modeling L. Guo et al. https://doi.org/10.5194/hess-30-1421-2026
- Three-dimensional modeling of fault geological structure using generalized triangular prism element reconstruction H. Liu et al. https://doi.org/10.1007/s10064-023-03166-8
- A real-time geological sections generation method for geological 3D models based on per-pixel linked lists S. Li et al. https://doi.org/10.1080/17538947.2025.2501770
- Efficient Construction of Voxel Models for Ore Bodies Using an Improved Winding Number Algorithm and CUDA Parallel Computing L. Liu et al. https://doi.org/10.3390/ijgi12120473
- Co-simulation of continuous and categorical variables: application in the Shuiyindong gold deposit modeling J. Zeng et al. https://doi.org/10.3389/feart.2026.1749476
- Subsurface geometry of the Revell Batholith by constrained geophysical modelling, NW Ontario, Canada M. Mushayandebvu et al. https://doi.org/10.1016/j.acags.2023.100121
- 数据和知识融合的Bayesian-MCMC三维地质建模 L. Wang et al. https://doi.org/10.3799/dqkx.2023.069
- Variability of geological interpretations of a mineral deposit: Quantitative study and implications P. Marchal et al. https://doi.org/10.1016/j.oregeorev.2025.107010
- Pseudo trans-dimensional 3-D geometrical inversion: a proof of concept using gravity data J. Giraud et al. https://doi.org/10.1093/gji/ggaf501
- Geological realism in Fluvial facies modelling with GAN under variable depositional conditions C. Sun et al. https://doi.org/10.1007/s10596-023-10190-w
- Assessing geometrical uncertainties in geological interface models using Markov chain Monte Carlo sampling via abstract graph J. Huang et al. https://doi.org/10.1016/j.tecto.2023.230032
- Estimating relative uncertainty of geological 3D models with low density of input data in geologically complex regions F. Staněk et al. https://doi.org/10.1007/s12145-025-01778-0
- Visualization facilitates uncertainty evaluation of multiple-point geostatistical stochastic simulation Q. Huang et al. https://doi.org/10.1007/s44267-023-00016-9
- A Geological Modeling Workflow for Shale Reservoirs: A Case Study of the F2 Member in the Qintong Sag M. Han et al. https://doi.org/10.3390/app16041759
- Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions M. Lindsay et al. https://doi.org/10.1016/j.gsf.2022.101435
Saved (final revised paper)
Latest update: 05 Jun 2026
Short summary
Results of a survey launched among practitioners in the mineral industry show that despite recognising the importance of uncertainty quantification it is not very well performed due to lack of data, time requirements, poor tracking of interpretations and relative complexity of uncertainty quantification. To alleviate the latter, we provide an open-source set of local and global indicators to measure geological uncertainty among an ensemble of geological models.
Results of a survey launched among practitioners in the mineral industry show that despite...
