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Geological model automatic reconstruction based on conditioning Wasserstein generative adversarial network with gradient penalty

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Abstract

Due to the structure complexity and heterogeneity of the geological models, it is difficult for traditional methods to characterize the corresponding anisotropic and structural features. Therefore, one of the generative models called Generative Adversarial Network (GAN) are introduced to the geological modeling fields, which describes the complex structural features effectively according to fitting the high-order statistical characteristics. However, the traditional GAN might cause gradient explosion or vanishment, insufficient model diversity, resulting the network cannot capture the spatial pattern and characteristics of geological models so that the reconstruction always has a bad performance. For this issue, this paper introduced the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), which can better measure the distribution discrepancy between the generative data and real data and provide a meaningful gradient for the training process. In addition, the gradient penalty term can make the objective function conform with Lipschitz constraints, which ensures the training process more stable and the correlation between the generative and real samples. Meanwhile, the conditioning loss function can make the reconstruction conform with the conditioning constraints. The 2D and 3D categorical facies model were introduced to perform experimental verification. The results show that the CWGAN-GP ensure the conditioning constraints and the reconstruction diversity simultaneously. In addition, for the network finished training, through inputting different kinds of conditioning data, a variety of stochastic simulation results can be generated, thereby realizing rapid and automatic geological model reconstruction.

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Data availability

The analysis datasets during the current study are available from the corresponding author on reasonable request (liugang@cug.edu.cn).

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (42172333, 41902304, U1711267), the fund of State key Laboratory of Biogeology and Environmental Geology (2021), Science and Technology Strategic Prospecting Project of Guizhou Province ([2022]ZD003) and the Knowledge Innovation Program of Wuhan-Shuguang Project (2022010801020206). Meanwhile, we are grateful to the editors, and the anonymous referee for their insightful comments and suggestions towards improving the research enclosed in this paper.

Funding

This work is supported by the National Natural Science Foundation of China (42172333, 41902304, U1711267), the fund of State key Laboratory of Biogeology and Environmental Geology (2021), Science and Technology Strategic Prospecting Project of Guizhou Province ([2022]ZD003) and the Knowledge Innovation Program of Wuhan-Shuguang Project (2022010801020206).

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, China

    Wenyao Fan, Gang Liu, Qiyu Chen, Zhesi Cui, Zixiao Yang, Qianhong Huang & Xuechao Wu

  2. State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan, 430074, China

    Wenyao Fan, Gang Liu, Qiyu Chen, Zhesi Cui, Zixiao Yang, Qianhong Huang & Xuechao Wu

  3. Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430074, China

    Wenyao Fan, Gang Liu, Qiyu Chen, Zhesi Cui, Zixiao Yang, Qianhong Huang & Xuechao Wu

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  1. Wenyao Fan
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Contributions

Conceptualization: Wenyao Fan, Gang Liu; Methodology: Wenyao Fan, Gang Liu, Qiyu Chen; Formal analysis and investigation: Gang Liu, Qiyu Chen, Zhesi Cui, Zixiao Yang, Qianhong Huang; Writing-original draft preparation: Wenyao Fan; Writing-review and editing: Gang Liu, Qiyu Chen; Funding acquisition: Gang Liu, Xuechao Wu; Supervision: Gang Liu, Qiyu Chen, Xuechao Wu.

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Correspondence to Gang Liu.

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The authors declare no competing interests.

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Communicated by: H. Babaie

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Appendix

Appendix

Table 4 The parameter settings for the Generator of 2D CWGAN-GP
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Table 5 The parameter settings for the Discriminator of 2D CWGAN-GP
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Table 6 The parameter settings for the Generator of 3D CWGAN-GP
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Table 7 The parameter settings for the Discriminator of 3D CWGAN-GP
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Fan, W., Liu, G., Chen, Q. et al. Geological model automatic reconstruction based on conditioning Wasserstein generative adversarial network with gradient penalty. Earth Sci Inform 16, 2825–2843 (2023). https://doi.org/10.1007/s12145-023-01012-9

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