Articles | Volume 17, issue 3
https://doi.org/10.5194/gmd-17-957-2024
© Author(s) 2024. 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-17-957-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Feb 2024
Development and technical paper |
| 05 Feb 2024
| 05 Feb 2024
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
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
Xuechuang Xu
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
Luyuan Wang
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
Xulei Wang
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
School of Geosciences and Info-Physics, Central South University, Lushan Nanlu 932, Yuelu district, Changsha 410012, China
Mark Jessell
The Centre for Exploration Targeting, School of Earth Sciences, 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
Vitaliy Ogarko
The Centre for Exploration Targeting, School of Earth Sciences, 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
Zhibin Liu
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
Yufei Zheng
School of Resources and Civil Engineering, Northeastern University, No. 3–11, Wenhua Road, Heping district, Shenyang 110819, China
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Cited
11 citations as recorded by crossref.
- 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization M. Lyu et al. 10.1515/geo-2025-0785
- A deep learning method for 3D geological modeling using ET4DD with offset-attention mechanism A. Ren et al. 10.1016/j.cageo.2025.105929
- Research on 3D geological modeling based on boosting integration strategy Y. Feng et al. 10.1016/j.oregeorev.2024.106157
- 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
- An ensemble learning paradigm for subsurface stratigraphy from sparse measurements and augmented training images C. Shi et al. 10.1016/j.tust.2024.105972
- Exploration of 3D Coal Seam Geological Modeling Visualization and Gas Content Prediction Technology Based on Borehole Data X. Zhao et al. 10.1002/ese3.2048
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann 10.1016/j.cageo.2025.105911
- A deep learning-driven three-dimensional geological modeling method using sparse borehole sampling data Z. He et al. 10.1016/j.measurement.2025.118461
- A 3D Geological Modeling Method Using the Transformer Model: A Solution for Sparse Borehole Data Z. Hang et al. 10.3390/min15030301
- SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces M. Michalak et al. 10.5194/gmd-18-4469-2025
- 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.
- 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization M. Lyu et al. 10.1515/geo-2025-0785
- A deep learning method for 3D geological modeling using ET4DD with offset-attention mechanism A. Ren et al. 10.1016/j.cageo.2025.105929
- Research on 3D geological modeling based on boosting integration strategy Y. Feng et al. 10.1016/j.oregeorev.2024.106157
- 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
- An ensemble learning paradigm for subsurface stratigraphy from sparse measurements and augmented training images C. Shi et al. 10.1016/j.tust.2024.105972
- Exploration of 3D Coal Seam Geological Modeling Visualization and Gas Content Prediction Technology Based on Borehole Data X. Zhao et al. 10.1002/ese3.2048
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann 10.1016/j.cageo.2025.105911
- A deep learning-driven three-dimensional geological modeling method using sparse borehole sampling data Z. He et al. 10.1016/j.measurement.2025.118461
- A 3D Geological Modeling Method Using the Transformer Model: A Solution for Sparse Borehole Data Z. Hang et al. 10.3390/min15030301
- SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces M. Michalak et al. 10.5194/gmd-18-4469-2025
Latest update: 29 Jul 2025
Short summary
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological...
