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Intelligent Generation of Cross Sections Using a Conditional Generative Adversarial Network and Application to Regional 3D Geological Modeling

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  • Xiangjin Ran

    (College of Earth Science, Jilin University, Changchun 130061, China
    Technology Innovation Center of Big Data Analysis and Application of Earth Science, Ministry of Natural Resources, Changchun 130061, China)

  • Linfu Xue

    (College of Earth Science, Jilin University, Changchun 130061, China
    Technology Innovation Center of Big Data Analysis and Application of Earth Science, Ministry of Natural Resources, Changchun 130061, China)

  • Xuejia Sang

    (College of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China)

  • Yao Pei

    (Technology Innovation Center of Big Data Analysis and Application of Earth Science, Ministry of Natural Resources, Changchun 130061, China
    College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China)

  • Yanyan Zhang

    (School of Economy and Trade, Jilin Business and Technology College, Changchun 130507, China)

Abstract

The cross section is the basic data for building 3D geological models. It is inefficient to draw a large number of cross sections to build an accurate model. This paper reports the use of multi-source and heterogeneous geological data, such as geological maps, gravity and aeromagnetic data, by a conditional generative adversarial network (CGAN) and implements an intelligent generation method of cross sections to overcome the problem of inefficient modeling data based on CGAN. Intelligent generation of cross sections and 3D geological modeling are carried out in three different areas in Liaoning Province. The results show that: (a) the accuracy of the proposed method is higher than the GAN and Variational AutoEncoder (VAE) models, achieving 87%, 45% and 68%, respectively; (b) the 3D geological model constructed by the generated cross sections in our study is consistent with manual creation in terms of stratum continuity and thickness. This study suggests that the proposed method is significant for surmounting the difficulty in data processing involved in regional 3D geological modeling.

Suggested Citation

  • Xiangjin Ran & Linfu Xue & Xuejia Sang & Yao Pei & Yanyan Zhang, 2022. "Intelligent Generation of Cross Sections Using a Conditional Generative Adversarial Network and Application to Regional 3D Geological Modeling," Mathematics, MDPI, vol. 10(24), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4677-:d:999084
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    References listed on IDEAS

    as
    1. Xiangjin Ran & Linfu Xue & Yanyan Zhang & Zeyu Liu & Xuejia Sang & Jinxin He, 2019. "Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network," Mathematics, MDPI, vol. 7(8), pages 1-16, August.
    2. Qingbin Liu & Wenling Liu & Jianpeng Yao & Yuyang Liu & Mao Pan, 2021. "An Improved Method of Reservoir Facies Modeling Based on Generative Adversarial Networks," Energies, MDPI, vol. 14(13), pages 1-16, June.
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    Cited by:

    1. Yong Liu & Qingjie Xu & Xingrui Li & Weiwen Zhan & Jingkai Guo & Jun Xiao, 2023. "A Small-Sample Borehole Fluvial Facies Identification Method Using Generative Adversarial Networks in the Context of Gas-Fired Power Generation, with the Hangjinqi Gas Field in the Ordos Basin as an E," Energies, MDPI, vol. 16(3), pages 1-15, January.

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