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
27 citations as recorded by crossref.
- A high-accuracy ionospheric foF2 critical frequency forecast using long short-term memory LSTM A. Denisenko-Floyd et al.
- An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience Z. Wenxue et al.
- A Multi-Task Learning Method for Relative Geologic Time, Horizons, and Faults With Prior Information and Transformer J. Yang et al.
- Uncertainty quantification using Hamiltonian Monte Carlo for structural geological modelling with implicit neural representations (INR) K. Gao et al.
- Kolmogorov-Arnold Networks for Semi-Supervised Impedance Inversion M. Liu et al.
- Intelligent Ghost Wave Suppression Based on Quadratic Convolution Kernel and Attention Mechanism D. Bao et al.
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann
- SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces M. Michalak et al.
- Two-Dimensional Stochastic Structural Geomodeling with Deep Generative Adversarial Networks C. Garayt et al.
- Deep learning for high-resolution multichannel seismic impedance inversion Y. Gao et al.
- GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling M. Hillier et al.
- GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds D. Oakley et al.
- Seismic property prediction using deep learning in LN area, Tarim Basin, China J. Li et al.
- Transformer-Model-Based Automatic Aquifer Generalization Using Borehole Logs: A Case Study in a Mining Area in Xingtai, Hebei Province, China Y. Du et al.
- A 3D Geological Modeling Method Using the Transformer Model: A Solution for Sparse Borehole Data Z. Hang et al.
- Structurally-Constrained Unsupervised Deep Learning for Seismic High-Resolution Reconstruction Y. Wang et al.
- Rapid Implicit 3D geological modelling from a large quantity of boreholes via a divide-and-conquer strategy based on Voronoi diagrams and R-functions X. Wang et al.
- Automatic mud diapir detection using ANFIS expert systems algorithm; a case study in the Gorgan plain, Iran B. Hedayat et al.
- A deep learning method for 3D geological modeling using ET4DD with offset-attention mechanism A. Ren et al.
- A deep learning-driven three-dimensional geological modeling method using sparse borehole sampling data Z. He et al.
- Deep Learning Vertical Resolution Enhancement Considering Features of Seismic Data Y. Gao et al.
- Sensing prior constraints in deep neural networks for solving exploration geophysical problems X. Wu et al.
- Three-dimensional modeling of loose layers based on stratum development law Y. Shen et al.
- Application and Validation of PSO-k-Means Clustering for Small-Scale Fault Identification in Coal Mines: A Case Study of the Huainan Coalfield B. Wang et al.
- CurvRBF: Mean Curvature-Controllable Radial Basis Functions for Implicit Geological Modeling Y. Chen et al.
- DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network Z. Bi et al.
- Multisource Borehole Data Fusion for Stochastic Inversion of Seabed Stratigraphic Architecture Based on the Fuzzy Markov Chain Y. Zhang et al.
27 citations as recorded by crossref.
- A high-accuracy ionospheric foF2 critical frequency forecast using long short-term memory LSTM A. Denisenko-Floyd et al.
- An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience Z. Wenxue et al.
- A Multi-Task Learning Method for Relative Geologic Time, Horizons, and Faults With Prior Information and Transformer J. Yang et al.
- Uncertainty quantification using Hamiltonian Monte Carlo for structural geological modelling with implicit neural representations (INR) K. Gao et al.
- Kolmogorov-Arnold Networks for Semi-Supervised Impedance Inversion M. Liu et al.
- Intelligent Ghost Wave Suppression Based on Quadratic Convolution Kernel and Attention Mechanism D. Bao et al.
- Fault representation in structural modelling with implicit neural representations K. Gao & F. Wellmann
- SubsurfaceBreaks v. 1.0: a supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces M. Michalak et al.
- Two-Dimensional Stochastic Structural Geomodeling with Deep Generative Adversarial Networks C. Garayt et al.
- Deep learning for high-resolution multichannel seismic impedance inversion Y. Gao et al.
- GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling M. Hillier et al.
- GEOMAPLEARN 1.2: detecting structures from geological maps with machine learning – the case of geological folds D. Oakley et al.
- Seismic property prediction using deep learning in LN area, Tarim Basin, China J. Li et al.
- Transformer-Model-Based Automatic Aquifer Generalization Using Borehole Logs: A Case Study in a Mining Area in Xingtai, Hebei Province, China Y. Du et al.
- A 3D Geological Modeling Method Using the Transformer Model: A Solution for Sparse Borehole Data Z. Hang et al.
- Structurally-Constrained Unsupervised Deep Learning for Seismic High-Resolution Reconstruction Y. Wang et al.
- Rapid Implicit 3D geological modelling from a large quantity of boreholes via a divide-and-conquer strategy based on Voronoi diagrams and R-functions X. Wang et al.
- Automatic mud diapir detection using ANFIS expert systems algorithm; a case study in the Gorgan plain, Iran B. Hedayat et al.
- A deep learning method for 3D geological modeling using ET4DD with offset-attention mechanism A. Ren et al.
- A deep learning-driven three-dimensional geological modeling method using sparse borehole sampling data Z. He et al.
- Deep Learning Vertical Resolution Enhancement Considering Features of Seismic Data Y. Gao et al.
- Sensing prior constraints in deep neural networks for solving exploration geophysical problems X. Wu et al.
- Three-dimensional modeling of loose layers based on stratum development law Y. Shen et al.
- Application and Validation of PSO-k-Means Clustering for Small-Scale Fault Identification in Coal Mines: A Case Study of the Huainan Coalfield B. Wang et al.
- CurvRBF: Mean Curvature-Controllable Radial Basis Functions for Implicit Geological Modeling Y. Chen et al.
- DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network Z. Bi et al.
- Multisource Borehole Data Fusion for Stochastic Inversion of Seabed Stratigraphic Architecture Based on the Fuzzy Markov Chain Y. Zhang et al.
Saved (final revised paper)
Latest update: 30 Apr 2026
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...
