基于深度学习的位场边界识别方法

张志厚, 姚禹, 石泽玉, 王虎, 乔中坤, 王生仁, 覃礼貌, 杜世回, 罗锋, 刘慰心. 2022. 基于深度学习的位场边界识别方法. 地球物理学报, 65(5): 1785-1801, doi: 10.6038/cjg2022P0403
引用本文: 张志厚, 姚禹, 石泽玉, 王虎, 乔中坤, 王生仁, 覃礼貌, 杜世回, 罗锋, 刘慰心. 2022. 基于深度学习的位场边界识别方法. 地球物理学报, 65(5): 1785-1801, doi: 10.6038/cjg2022P0403
ZHANG ZhiHou, YAO Yu, SHI ZeYu, WANG Hu, QIAO ZhongKun, WANG ShengRen, QIN LiMao, DU ShiHui, LUO Feng, LIU WeiXin. 2022. Deep learning for potential field edge detection. Chinese Journal of Geophysics (in Chinese), 65(5): 1785-1801, doi: 10.6038/cjg2022P0403
Citation: ZHANG ZhiHou, YAO Yu, SHI ZeYu, WANG Hu, QIAO ZhongKun, WANG ShengRen, QIN LiMao, DU ShiHui, LUO Feng, LIU WeiXin. 2022. Deep learning for potential field edge detection. Chinese Journal of Geophysics (in Chinese), 65(5): 1785-1801, doi: 10.6038/cjg2022P0403

基于深度学习的位场边界识别方法

  • 基金项目:

    四川省科技厅计划项目(2021YJ0031), 中央高校基本科研业务费(2682021GF019, 2682020CX14), 西藏自治区科技计划项目(XZ202001ZY0011G), 国家重点研发计划项目(2018YFC1505401)和中国中铁股份有限公司科技研究开发计划项目(CZ01-重点-05)联合资助

详细信息
    作者简介:

    张志厚, 男, 博士, 副教授, 硕士生导师, 主要从事地球物理研究. E-mail: logicprimer@163.com

  • 中图分类号: P631

Deep learning for potential field edge detection

  • 边界识别是位场数据处理中极为重要的一种技术, 现有的边界识别方法属于无监督式机器运算, 其识别精度与地质体的空间分布存在很大关系, 尤其是对深部复杂异常体的识别存在边界模糊的特点.为了进一步提高边界识别的精度, 受深度学习卓越非线性映射能力和监督式学习优点的启发, 本文提出了基于深度学习的位场边界识别方法, 深度学习网络结构是一种融合了多尺度特征和全局注意力机制的密集跳跃连接网络(PFD-Net).该网络结构首先以改进的U-net为骨干网络获取位场边界特征信息, 然后在嵌套的标准卷积模块之间进行密集跳跃连接来缩减编码阶段到解码阶段的语义鸿沟, 以及减少训练阶段梯度消失等问题, 随后再采用全局注意力机制模块将多尺度的高低层特征信息进行融合, 以此进一步加强边界的全局及细节定位.模型试验表明, PFD-Net网络能够准确识别出异常体的边界信息, 且对于含噪声数据, 其预测结果的质量不会降低, 该网络表现出较强的泛化性和鲁棒性.最后将本文方法应用于藏东南某铁路隧道西段的航空磁测数据, 取得了良好的边界识别结果并能够获得更多的构造信息.

  • 加载中
  • 图 1 

    CNN端到端示意图

    Figure 1. 

    Schematic diagram of CNN end-to-end

    图 2 

    位场边界识别PFD-Net总体结构图

    Figure 2. 

    Schematic diagram of potential field edge detection PFD-Net

    图 3 

    骨干网络结构—改进的U-Net

    Figure 3. 

    Backbone network structure——the improved U-Net

    图 4 

    PFD-Net网络结构

    Figure 4. 

    PFD-Net network structure

    图 5 

    密集跳跃连接结构

    Figure 5. 

    Dense skip connection structure

    图 6 

    全局注意力的上采样模块结构

    Figure 6. 

    Global attention upsample module structure

    图 7 

    PFD-Net训练误差及验证误差

    Figure 7. 

    Training error and validation error of PFD-Net

    图 8 

    三维模型透视图及正演结果

    Figure 8. 

    Perspective drawing of three-dimensional model and forward calculation

    图 9 

    四种模型边界识别结果

    Figure 9. 

    Forward data and edge detection result of four kinds of models

    图 10 

    THDR识别结果

    Figure 10. 

    Edge detection result of THDR

    图 11 

    EASTA识别结果

    Figure 11. 

    Edge detection result of EASTA

    图 12 

    NSED识别结果

    Figure 12. 

    Edge detection result of NSED

    图 13 

    NVSED识别结果

    Figure 13. 

    Edge detection result of NVSED

    图 14 

    磁性体三维模型透视图

    Figure 14. 

    3D perspective view of the magnetic body model

    图 15 

    未化极磁性体模型检验效果

    Figure 15. 

    Test effect of unpolarized magnetic model

    图 16 

    藏东南某铁路隧道西段地质地形图(a)、航磁异常(b)和本文方法的识别结果(c)

    Figure 16. 

    Geological topographic map of the western section of a certain railway tunnel in southeastern Tibet (a), aeromagnetic anomalies (b), and the detection results of this method (c)

    表 1 

    长方体模型参数

    Table 1. 

    Cuboid model parameters

    模型编号 编号 中心点坐标/(km, km, km) 模型大小/(km×km×km) 物性参数
    模型一 1 (0.7, 0.9, 0.45) 0.4×0.6×0.3 1.0 g·cm-3
    2 (2.2, 0.7, 0.45) 0.6×0.4×0.3 1.0 g·cm-3
    3 (0.9, 2.4, 0.45) 0.6×0.4×0.3 1.0 g·cm-3
    4 (2.4, 2.2, 0.45) 0.4×0.6×0.3 1.0 g·cm-3
    模型二 1 (0.6, 1.5, 0.3) 0.6×1.0×0.2 0.2 A·m-1
    2 (2.5, 1.5, 0.9) 0.6×1.0×0.2 1.0 A·m-1
    模型三 1 (1.55, 1.55, 0.2) 0.3×0.3×0.3 1.5 g·cm-3
    2 (1.55, 1.55, 0.6) 1.9×1.9×0.5 1.0 g·cm-3
    下载: 导出CSV

    表 2 

    未化极磁性体模型参数

    Table 2. 

    Model parameters of unpolarized magnetic body

    编号 中心点坐标/(km, km, km) 模型大小/(km×km×km) 物性参数/(A·m-1) 磁化偏角/(°)
    1 (0.9, 2.0, 0.8) 0.6×1.4×0.4 1.0 15
    2 (2.5, 2.35, 0.55) 0.6×0.7×0.3 1.0 15
    3 (2.2, 0.9, 0.55) 0.4×0.4×0.3 1.0 15
    下载: 导出CSV
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出版历程
收稿日期:  2021-06-11
修回日期:  2022-01-17
上线日期:  2022-05-10

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