Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6841-2022
© 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-15-6841-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
08 Sep 2022
Development and technical paper |
| 08 Sep 2022
| 08 Sep 2022
DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network
Zhengfa Bi
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, P. R. China
School of Earth and Space Sciences, University of Science and Technology of China, Hefei, Anhui, P. R. China
Zhaoliang Li
China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing, P. R. China
Dekuan Chang
Research Institute of Petroleum Exploration and Development–Northwest (NWGI), PetroChina, Gansu, Lanzhou, P. R. China
Xueshan Yong
Research Institute of Petroleum Exploration and Development–Northwest (NWGI), PetroChina, Gansu, Lanzhou, P. R. China
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Cited
18 citations as recorded by crossref.
- A 3D Geological Modeling Method Using the Transformer Model: A Solution for Sparse Borehole Data Z. Hang et al. 10.3390/min15030301
- Structurally-Constrained Unsupervised Deep Learning for Seismic High-Resolution Reconstruction Y. Wang et al. 10.1109/TGRS.2023.3340888
- A high-accuracy ionospheric foF2 critical frequency forecast using long short-term memory LSTM A. Denisenko-Floyd et al. 10.1017/eds.2024.20
- A Multi-Task Learning Method for Relative Geologic Time, Horizons, and Faults With Prior Information and Transformer J. Yang et al. 10.1109/TGRS.2023.3264593
- Kolmogorov-Arnold Networks for Semi-Supervised Impedance Inversion M. Liu et al. 10.1109/LGRS.2025.3529024
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann 10.1016/j.cageo.2025.105911
- Automatic mud diapir detection using ANFIS expert systems algorithm; a case study in the Gorgan plain, Iran B. Hedayat et al. 10.1007/s12665-024-11703-1
- A deep learning method for 3D geological modeling using ET4DD with offset-attention mechanism A. Ren et al. 10.1016/j.cageo.2025.105929
- Two-Dimensional Stochastic Structural Geomodeling with Deep Generative Adversarial Networks C. Garayt et al. 10.1007/s11004-025-10188-3
- Deep Learning Vertical Resolution Enhancement Considering Features of Seismic Data Y. Gao et al. 10.1109/TGRS.2023.3234617
- Deep learning for high-resolution multichannel seismic impedance inversion Y. Gao et al. 10.1190/geo2023-0096.1
- GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling M. Hillier et al. 10.5194/gmd-16-6987-2023
- GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds D. Oakley et al. 10.5194/gmd-18-939-2025
- Sensing prior constraints in deep neural networks for solving exploration geophysical problems X. Wu et al. 10.1073/pnas.2219573120
- Seismic property prediction using deep learning in LN area, Tarim Basin, China J. Li et al. 10.1093/jge/gxae099
- Three-dimensional modeling of loose layers based on stratum development law Y. Shen et al. 10.1515/geo-2022-0440
- DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network Z. Bi et al. 10.5194/gmd-15-6841-2022
- DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network Z. Bi et al. 10.5194/gmd-15-6841-2022
17 citations as recorded by crossref.
- A 3D Geological Modeling Method Using the Transformer Model: A Solution for Sparse Borehole Data Z. Hang et al. 10.3390/min15030301
- Structurally-Constrained Unsupervised Deep Learning for Seismic High-Resolution Reconstruction Y. Wang et al. 10.1109/TGRS.2023.3340888
- A high-accuracy ionospheric foF2 critical frequency forecast using long short-term memory LSTM A. Denisenko-Floyd et al. 10.1017/eds.2024.20
- A Multi-Task Learning Method for Relative Geologic Time, Horizons, and Faults With Prior Information and Transformer J. Yang et al. 10.1109/TGRS.2023.3264593
- Kolmogorov-Arnold Networks for Semi-Supervised Impedance Inversion M. Liu et al. 10.1109/LGRS.2025.3529024
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann 10.1016/j.cageo.2025.105911
- Automatic mud diapir detection using ANFIS expert systems algorithm; a case study in the Gorgan plain, Iran B. Hedayat et al. 10.1007/s12665-024-11703-1
- A deep learning method for 3D geological modeling using ET4DD with offset-attention mechanism A. Ren et al. 10.1016/j.cageo.2025.105929
- Two-Dimensional Stochastic Structural Geomodeling with Deep Generative Adversarial Networks C. Garayt et al. 10.1007/s11004-025-10188-3
- Deep Learning Vertical Resolution Enhancement Considering Features of Seismic Data Y. Gao et al. 10.1109/TGRS.2023.3234617
- Deep learning for high-resolution multichannel seismic impedance inversion Y. Gao et al. 10.1190/geo2023-0096.1
- GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling M. Hillier et al. 10.5194/gmd-16-6987-2023
- GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds D. Oakley et al. 10.5194/gmd-18-939-2025
- Sensing prior constraints in deep neural networks for solving exploration geophysical problems X. Wu et al. 10.1073/pnas.2219573120
- Seismic property prediction using deep learning in LN area, Tarim Basin, China J. Li et al. 10.1093/jge/gxae099
- Three-dimensional modeling of loose layers based on stratum development law Y. Shen et al. 10.1515/geo-2022-0440
- DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network Z. Bi et al. 10.5194/gmd-15-6841-2022
1 citations as recorded by crossref.
Latest update: 14 May 2025
Short summary
We present an implicit modeling method based on deep learning to produce a geologically valid and structurally compatible model from unevenly sampled structural data. Trained with automatically generated synthetic data with realistic features, our network can efficiently model geological structures without the need to solve large systems of mathematical equations, opening new opportunities for further leveraging deep learning to improve modeling capacity in many Earth science applications.
We present an implicit modeling method based on deep learning to produce a geologically valid...
