Articles | Volume 15, issue 17
Geosci. Model Dev., 15, 6841–6861, 2022
Geosci. Model Dev., 15, 6841–6861, 2022
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 et al.

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Cited articles

Alon, U. and Yahav, E.: On the bottleneck of graph neural networks and its practical implications, arXiv [preprint],, 9 June 2020. a
Bi, Z., Wu, X., Geng, Z., and Li, H.: Deep relative geologic time: a deep learning method for simultaneously interpreting 3-D seismic horizons and faults, J. Geophys. Res.-Sol. Ea., 126, e2021JB021882,, 2021. a
Bi, Z., Wu, X., Li, Z., Chang, D., and Yong, X.: Training and validation datasets for “Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network”, Zenodo [data set],, 2022a. a
Bi, Z., Wu, X., Li, Z., Chang, D., and Yong, X.: zfbi/DeepISMNet: DeepISMNet: Three-Dimensional Implicit Structural Modeling with Convolutional Neural Network, Zenodo [code],, 2022b. a
Calcagno, P., Chilès, J.-P., Courrioux, G., and Guillen, A.: Geological modelling from field data and geological knowledge: Part I. Modelling method coupling 3D potential-field interpolation and geological rules, Phys. Earth Planet. In., 171, 147–157, 2008. a, b, c
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.