Articles | Volume 11, issue 2
https://doi.org/10.5194/se-11-419-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-11-419-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2020
Research article |
| 31 Mar 2020
| 31 Mar 2020
Towards plausible lithological classification from geophysical inversion: honouring geological principles in subsurface imaging
Centre for Exploration Targeting (School of Earth Sciences),
University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia
Mark Lindsay
Centre for Exploration Targeting (School of Earth Sciences),
University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia
Mark Jessell
Centre for Exploration Targeting (School of Earth Sciences),
University of Western Australia, 35 Stirling Highway, 6009 Crawley, Australia
Vitaliy Ogarko
The International Centre for Radio Astronomy Research, University
of Western Australia, 7 Fairway, 6009 Crawley, Australia
ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)
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Cited
18 citations as recorded by crossref.
- An integrated machine learning framework with uncertainty quantification for three-dimensional lithological modeling from multi-source geophysical data and drilling data Z. Zhang et al. 10.1016/j.enggeo.2023.107255
- Geology differentiation by applying unsupervised machine learning to multiple independent geophysical inversions A. Melo & Y. Li 10.1093/gji/ggab316
- Generalization of level-set inversion to an arbitrary number of geologic units in a regularized least-squares framework J. Giraud et al. 10.1190/geo2020-0263.1
- Constraining 3D geometric gravity inversion with a 2D reflection seismic profile using a generalized level set approach: application to the eastern Yilgarn Craton M. Rashidifard et al. 10.5194/se-12-2387-2021
- Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions M. Lindsay et al. 10.1016/j.gsf.2022.101435
- Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan N. Ali et al. 10.1016/j.geogeo.2022.100123
- Prior geological knowledge enhanced Markov random field for development of geological cross-sections from sparse data Z. Qian & C. Shi 10.1016/j.compgeo.2024.106587
- 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
- Mapping critical mineral resources using airborne geophysics, 3D joint inversion and geology differentiation: A case study of a buried niobium deposit in the Elk Creek carbonatite, Nebraska, USA X. Wei et al. 10.1111/1365-2478.13280
- Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression V. Ogarko et al. 10.5194/gmd-17-2325-2024
- 3D probabilistic geology differentiation based on airborne geophysics, mixed Lp norm joint inversion, and physical property measurements X. Wei & J. Sun 10.1190/geo2021-0833.1
- 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
- Disjoint interval bound constraints using the alternating direction method of multipliers for geologically constrained inversion: Application to gravity data V. Ogarko et al. 10.1190/geo2019-0633.1
- 3D Monte Carlo geometry inversion using gravity data X. Wei et al. 10.1190/geo2023-0498.1
- Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees J. Giraud et al. 10.1093/gji/ggae192
- Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin M. Lindsay et al. 10.5194/se-11-1053-2020
- Three-Dimensional Pseudo-Lithologic Modeling Via Adaptive Feature Weighted k-Means Algorithm from Multi-Source Geophysical Datasets, Qingchengzi Pb–Zn–Ag–Au District, China Z. Zhang et al. 10.1007/s11053-021-09927-0
- Geophysical inversion for 3D contact surface geometry C. Galley et al. 10.1190/geo2019-0614.1
17 citations as recorded by crossref.
- An integrated machine learning framework with uncertainty quantification for three-dimensional lithological modeling from multi-source geophysical data and drilling data Z. Zhang et al. 10.1016/j.enggeo.2023.107255
- Geology differentiation by applying unsupervised machine learning to multiple independent geophysical inversions A. Melo & Y. Li 10.1093/gji/ggab316
- Generalization of level-set inversion to an arbitrary number of geologic units in a regularized least-squares framework J. Giraud et al. 10.1190/geo2020-0263.1
- Constraining 3D geometric gravity inversion with a 2D reflection seismic profile using a generalized level set approach: application to the eastern Yilgarn Craton M. Rashidifard et al. 10.5194/se-12-2387-2021
- Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions M. Lindsay et al. 10.1016/j.gsf.2022.101435
- Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan N. Ali et al. 10.1016/j.geogeo.2022.100123
- Prior geological knowledge enhanced Markov random field for development of geological cross-sections from sparse data Z. Qian & C. Shi 10.1016/j.compgeo.2024.106587
- 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
- Mapping critical mineral resources using airborne geophysics, 3D joint inversion and geology differentiation: A case study of a buried niobium deposit in the Elk Creek carbonatite, Nebraska, USA X. Wei et al. 10.1111/1365-2478.13280
- Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression V. Ogarko et al. 10.5194/gmd-17-2325-2024
- 3D probabilistic geology differentiation based on airborne geophysics, mixed Lp norm joint inversion, and physical property measurements X. Wei & J. Sun 10.1190/geo2021-0833.1
- 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
- Disjoint interval bound constraints using the alternating direction method of multipliers for geologically constrained inversion: Application to gravity data V. Ogarko et al. 10.1190/geo2019-0633.1
- 3D Monte Carlo geometry inversion using gravity data X. Wei et al. 10.1190/geo2023-0498.1
- Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees J. Giraud et al. 10.1093/gji/ggae192
- Mapping undercover: integrated geoscientific interpretation and 3D modelling of a Proterozoic basin M. Lindsay et al. 10.5194/se-11-1053-2020
- Three-Dimensional Pseudo-Lithologic Modeling Via Adaptive Feature Weighted k-Means Algorithm from Multi-Source Geophysical Datasets, Qingchengzi Pb–Zn–Ag–Au District, China Z. Zhang et al. 10.1007/s11053-021-09927-0
1 citations as recorded by crossref.
Latest update: 20 Nov 2024
Short summary
We propose a methodology for the identification of rock types using geophysical and geological information. It relies on an algorithm used in machine learning called
self-organizing maps, to which we add plausibility filters to ensure that the results respect base geological rules and geophysical measurements. Application in the Yerrida Basin (Western Australia) reveals that the thinning of prospective greenstone belts at depth could be due to deep structures not seen from surface.
We propose a methodology for the identification of rock types using geophysical and geological...
