Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 17
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.
https://doi.org/10.5194/gmd-15-6841-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 17
Development and technical paper
 | 
08 Sep 2022
Development and technical paper |  | 08 Sep 2022

DeepISMNet: three-dimensional implicit structural modeling with convolutional neural network

Zhengfa Bi, Xinming Wu, Zhaoliang Li, Dekuan Chang, and Xueshan Yong

Data sets

Training and validation datasets for "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network" Zhengfa Bi, Xinming Wu, Zhaoliang Li, Dekuan Chang, and Xueshan Yong https://doi.org/10.5281/zenodo.6480165

Model code and software

DeepISMNet: Three-Dimensional Implicit Structural Modeling with Convolutional Neural Network Zhengfa Bi, Xinming Wu, Zhaoliang Li, Dekuan Chang, and Xueshan Yong https://doi.org/10.5281/zenodo.6684269

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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.
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