基于多尺度特征融合和深度可分离卷积的生成对抗网络地质建模方法

doi:10.3799/dqkx.2026.009

基于多尺度特征融合和深度可分离卷积的生成对抗网络地质建模方法

doi: 10.3799/dqkx.2026.009

刘小波^1,2,3, ,,
何联义^1,2,
黄怀瑾^1,2,
向红波^1,2,
蔡之华⁴,
李长河⁵

1. 中国地质大学（武汉）自动化学院，湖北武汉 430074
2. 复杂系统先进控制与智能自动化湖北省重点实验室，湖北武汉 430074
3. 地球探测智能化技术教育部工程研究中心，湖北武汉 430074
4. 中国地质大学（武汉）计算机学院，湖北武汉 430074
5. 安徽理工大学人工智能学院，安徽淮南 232001

基金项目:

国家自然科学基金项目(No.61973285, 62076226)

湖北省自然科学基金项目(2022CFB438).

详细信息

作者简介:
刘小波（1981—），男，教授，主要从事机器学习、智能建模、遥感图像处理方面的研究工作. ORCID:0000-0001-8298-7715. E-mail: xbliu@cug.edu.cn

通讯作者:
刘小波，E-mail: xbliu@cug.edu.cn

中图分类号: P628
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出版历程
- 收稿日期: 2025-08-09
- 网络出版日期: 2026-01-28

Geological Modeling Method of Generative Adversarial Networks Based on Multi-Scale Feature Fusion and Depthwise Separable Convolutions

Liu Xiaobo^{1,2,3
, ,},
He Lianyi^1,2,
Huang Huaijin^1,2,
Xiang Hongbo^1,2,
Cai Zhihua⁴,
Li Changhe⁵

1. School of Automation, China University of Geosciences, Wuhan 430074, China
2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
4. School of Computer Science, China University of Geosciences, Wuhan, 430074, China
5. School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001, China

摘要

摘要: 复杂地质结构建模在资源勘查、地下工程设计与地质灾害预测等领域具有重要意义. 生成对抗网络（GAN）在地质建模中展现出较强的非线性建模能力和模式迁移能力，但在处理复杂地质约束及精细结构重建时，其在建模精度、结构连通性及建模效率方面仍面临一些挑战. 针对上述问题，本文提出一种基于多尺度特征融合和深度可分离卷积的生成对抗网络地质建模方法，通过设计多尺度特征融合模块强化地质结构的细节表达与整体一致性，并引入深度可分离卷积以降低模型参数量和计算成本，提升建模效率. 同时，结合条件特征融合与渐进式分辨率生成策略，增强模型对条件数据的感知能力. 为验证方法有效性，选取二维河流相、多属性冰楔和三维褶皱构造等典型数据，从空间变异性、连通性、属性一致性与条件点重建准确率等方面进行系统评估，并与多点统计方法（QS）和改进型生成对抗网络（CWGAN-GP）进行对比分析. 结果表明，在64×64和64×64×64的分辨率下，二维和三维四个数据集生成的模型MS-SWD指标分别为0.016、0.025、0.0079、0.0087，均显著低于对比方法；同时所生成模型的平均连通区域大小最接近参考模型（二维河流数据为300.59像素，三维褶皱数据为17814.17像素）；在整体准确度方面，本文方法的准确率和MSE指标均优于对比方法（分别为73.24%、69.48%和0.024、0.047），并通过效率分析和消融实验证明了该方法在效率和参数量方面的优势. 实验表明所提方法在保证合理与高保真性的同时，显着提升了建模效率，适用于复杂非平稳地质体的高效建模任务，具有广阔的工程应用前景.
- 地质建模 /
- 生成对抗网络 /
- 多尺度特征融合 /
- 深度可分离卷积
Abstract: Complex geological structure modeling holds significant importance in fields such as resource exploration, underground engineering design, and geological hazard prediction. Generative adversarial networks (GANs) have demonstrated strong nonlinear modeling capabilities and pattern transfer abilities in geological modeling. However, when dealing with complex geological constraints and the reconstruction of fine structures, they still face challenges in modeling accuracy, structural connectivity, and modeling efficiency. To address these issues, this paper proposes a GAN-based geological modeling method incorporating multi-scale feature fusion and deep separable convolutions. A multi-scale feature fusion module enhances the expression of geological structure details and overall consistency, while deep separable convolutions reduce model parameters and computational costs, improving modeling efficiency. Additionally, a conditional feature adaptive fusion and progressive resolution generation strategy enhances the model's sensitivity to conditional data. To validate the method's effectiveness, typical models including two-dimensional river phases, multi-attribute ice wedges, and three-dimensional fold structures were selected. Systematic evaluations were conducted across spatial variability, connectivity, attribute consistency, and conditional point reconstruction accuracy. Comparative analyses were performed against multi-point statistical methods (QS) and an improved generative adversarial network (CWGAN-GP). The results show that at resolutions of 64×64 and 64×64×64, the MS-SWD indicators of the generated models for the two-dimensional and three-dimensional datasets are 0.016, 0.025, 0.0079, and 0.0087 respectively, which are significantly lower than those of the comparison methods. At the same time, the average connected region size of the generated models is closest to that of the reference model (300.59 pixels for the two-dimensional river data and 17814.17 pixels for the three-dimensional fold data). In terms of overall accuracy, the accuracy rate and MSE indicators of the proposed method are superior to those of the comparison method (73.24%, 69.48% and 0.024, 0.047 respectively), and the advantages in efficiency and parameter quantity are proved through efficiency analysis and ablation experiments. The experiments show that the proposed method significantly improves the modeling efficiency while ensuring reasonable and high fidelity, and is suitable for efficient modeling tasks of complex non-stationary geological bodies, with broad engineering application prospects.
- geological modeling /
- generative adversarial networks /
- multi-scale feature fusion /
- depthwise separable convolutions

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参考文献(34)

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基于多尺度特征融合和深度可分离卷积的生成对抗网络地质建模方法

doi: 10.3799/dqkx.2026.009

作者简介:
刘小波（1981—），男，教授，主要从事机器学习、智能建模、遥感图像处理方面的研究工作. ORCID:0000-0001-8298-7715. E-mail: xbliu@cug.edu.cn

通讯作者:
刘小波，E-mail: xbliu@cug.edu.cn

计量

Geological Modeling Method of Generative Adversarial Networks Based on Multi-Scale Feature Fusion and Depthwise Separable Convolutions

计量

目录

留言板

基于多尺度特征融合和深度可分离卷积的生成对抗网络地质建模方法

doi: 10.3799/dqkx.2026.009

作者简介: 刘小波（1981—），男，教授，主要从事机器学习、智能建模、遥感图像处理方面的研究工作. ORCID:0000-0001-8298-7715. E-mail: xbliu@cug.edu.cn

通讯作者: 刘小波，E-mail: xbliu@cug.edu.cn

计量

出版历程

Geological Modeling Method of Generative Adversarial Networks Based on Multi-Scale Feature Fusion and Depthwise Separable Convolutions

计量

出版历程

目录

作者简介:
刘小波（1981—），男，教授，主要从事机器学习、智能建模、遥感图像处理方面的研究工作. ORCID:0000-0001-8298-7715. E-mail: xbliu@cug.edu.cn

通讯作者:
刘小波，E-mail: xbliu@cug.edu.cn