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

Xiangjin Ran, Linfu Xue (), Xuejia Sang, Yao Pei and Yanyan Zhang
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Xiangjin Ran: College of Earth Science, Jilin University, Changchun 130061, China
Linfu Xue: College of Earth Science, Jilin University, 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
Yanyan Zhang: School of Economy and Trade, Jilin Business and Technology College, Changchun 130507, China

Mathematics, 2022, vol. 10, issue 24, 1-17

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.

Keywords: generation of cross section; conditional generation adversarial network; 3D geological model; arithmetic mean deviation of section; coincidence rate of point coordinates (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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