Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6841-2022
https://doi.org/10.5194/gmd-15-6841-2022
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, Xinming Wu, Zhaoliang Li, Dekuan Chang, and Xueshan Yong

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Cited articles

Alon, U. and Yahav, E.: On the bottleneck of graph neural networks and its practical implications, arXiv [preprint], https://doi.org/10.48550/arXiv.2006.05205, 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, https://doi.org/10.1029/2021JB021882, 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], https://doi.org/10.5281/zenodo.6480165, 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], https://doi.org/10.5281/zenodo.6684269, 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
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We present an implicit modeling method based on deep learning to produce a geologically valid...
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