Articles | Volume 14, issue 8
https://doi.org/10.5194/gmd-14-5063-2021
© Author(s) 2021. 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-14-5063-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
16 Aug 2021
Development and technical paper |
| 16 Aug 2021
| 16 Aug 2021
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
Vitaliy Ogarko
International Centre for Radio Astronomy Research, The University of
Western Australia, Perth, Australia
ARC Centre of Excellence for All-Sky Astrophysics in 3 Dimensions
(ASTRO 3D), Perth, Australia
Yohan de Rose
School of Earth, Atmosphere and Environment, Monash University, Clayton, Australia
Mark Lindsay
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
Ranee Joshi
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
Agnieszka Piechocka
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
CSIRO, Mineral Resources – Discovery, ARRC, Kensington,
Australia
Lachlan Grose
School of Earth, Atmosphere and Environment, Monash University, Clayton, Australia
Miguel de la Varga
Computational Geoscience and Reservoir Engineering, RWTH Aachen, Aachen,
Germany
Laurent Ailleres
School of Earth, Atmosphere and Environment, Monash University, Clayton, Australia
Guillaume Pirot
Mineral Exploration Cooperative Research Centre, Centre for
Exploration Targeting, School of Earth Sciences, The University of Western
Australia, Perth, Australia
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Cited
19 citations as recorded by crossref.
- GIS Approach for Expressing Structural Landforms: Forms, Elements, and Relationships Y. Liu et al. 10.3390/app132312872
- loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification G. Pirot et al. 10.5194/gmd-15-4689-2022
- Spatial agents for geological surface modelling E. de Kemp 10.5194/gmd-14-6661-2021
- Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code J. Giraud et al. 10.5194/gmd-14-6681-2021
- Geological symbol recognition on geological map using convolutional recurrent neural network with augmented data Q. Qiu et al. 10.1016/j.oregeorev.2022.105262
- 3D structural modeling for seismic exploration based on knowledge graphs X. Zhan et al. 10.1190/geo2020-0924.1
- Structural Modeling Based on Human–Computer Knowledge Interaction X. Zhan et al. 10.1007/s11770-023-1017-z
- Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications M. Jessell et al. 10.5194/essd-14-381-2022
- Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions M. Lindsay et al. 10.1016/j.gsf.2022.101435
- Machine learning-based field geological mapping: A new exploration of geological survey data acquisition strategy W. Wang et al. 10.1016/j.oregeorev.2024.105959
- Assessing the Uncertainty in Lithology, Grades and Recoverable Resources in an Iron Deposit in Southern Cameroon F. Ekolle Essoh et al. 10.1007/s11053-023-10276-3
- Multiple-Point Geostatistics-Based Three-Dimensional Automatic Geological Modeling and Uncertainty Analysis for Borehole Data J. Guo et al. 10.1007/s11053-022-10071-6
- Regional-scale 3D modelling in metamorphic belts: An implicit model-driven workflow applied in the Pennine Alps G. Arienti et al. 10.1016/j.jsg.2023.105045
- Automated extraction of orientation and stratigraphic thickness from geological maps L. Nibourel et al. 10.1016/j.jsg.2023.104865
- Semi-automated classification of layered rock slopes using digital elevation model and geological map H. Shang et al. 10.1515/geo-2022-0526
- GemGIS - Spatial Data Processing for Geomodeling A. Jüstel et al. 10.21105/joss.03709
- Three-dimensional modeling of fault geological structure using generalized triangular prism element reconstruction H. Liu et al. 10.1007/s10064-023-03166-8
- Research on Automatic Construction Method of Three-Dimensional Complex Fault Model C. Zhang et al. 10.3390/min11080893
- LoopStructural 1.0: time-aware geological modelling L. Grose et al. 10.5194/gmd-14-3915-2021
17 citations as recorded by crossref.
- GIS Approach for Expressing Structural Landforms: Forms, Elements, and Relationships Y. Liu et al. 10.3390/app132312872
- loopUI-0.1: indicators to support needs and practices in 3D geological modelling uncertainty quantification G. Pirot et al. 10.5194/gmd-15-4689-2022
- Spatial agents for geological surface modelling E. de Kemp 10.5194/gmd-14-6661-2021
- Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code J. Giraud et al. 10.5194/gmd-14-6681-2021
- Geological symbol recognition on geological map using convolutional recurrent neural network with augmented data Q. Qiu et al. 10.1016/j.oregeorev.2022.105262
- 3D structural modeling for seismic exploration based on knowledge graphs X. Zhan et al. 10.1190/geo2020-0924.1
- Structural Modeling Based on Human–Computer Knowledge Interaction X. Zhan et al. 10.1007/s11770-023-1017-z
- Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications M. Jessell et al. 10.5194/essd-14-381-2022
- Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions M. Lindsay et al. 10.1016/j.gsf.2022.101435
- Machine learning-based field geological mapping: A new exploration of geological survey data acquisition strategy W. Wang et al. 10.1016/j.oregeorev.2024.105959
- Assessing the Uncertainty in Lithology, Grades and Recoverable Resources in an Iron Deposit in Southern Cameroon F. Ekolle Essoh et al. 10.1007/s11053-023-10276-3
- Multiple-Point Geostatistics-Based Three-Dimensional Automatic Geological Modeling and Uncertainty Analysis for Borehole Data J. Guo et al. 10.1007/s11053-022-10071-6
- Regional-scale 3D modelling in metamorphic belts: An implicit model-driven workflow applied in the Pennine Alps G. Arienti et al. 10.1016/j.jsg.2023.105045
- Automated extraction of orientation and stratigraphic thickness from geological maps L. Nibourel et al. 10.1016/j.jsg.2023.104865
- Semi-automated classification of layered rock slopes using digital elevation model and geological map H. Shang et al. 10.1515/geo-2022-0526
- GemGIS - Spatial Data Processing for Geomodeling A. Jüstel et al. 10.21105/joss.03709
- Three-dimensional modeling of fault geological structure using generalized triangular prism element reconstruction H. Liu et al. 10.1007/s10064-023-03166-8
Latest update: 26 Apr 2024
Short summary
We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
We have developed software that allows the user to extract sufficient information from...
