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    一种基于图神经网络的地质钻孔数据保护方案

    尚浩 朱恒华 李双 宋晓媚 夏雨 刘惠 杨帆

    尚浩, 朱恒华, 李双, 宋晓媚, 夏雨, 刘惠, 杨帆, 2023. 一种基于图神经网络的地质钻孔数据保护方案. 地球科学, 48(8): 3151-3161. doi: 10.3799/dqkx.2021.232
    引用本文: 尚浩, 朱恒华, 李双, 宋晓媚, 夏雨, 刘惠, 杨帆, 2023. 一种基于图神经网络的地质钻孔数据保护方案. 地球科学, 48(8): 3151-3161. doi: 10.3799/dqkx.2021.232
    Shang Hao, Zhu Henghua, Li Shuang, Song Xiaomei, Xia Yu, Liu Hui, Yang Fan, 2023. A Geological Borehole Data Protection Based on Graph Neural Networks. Earth Science, 48(8): 3151-3161. doi: 10.3799/dqkx.2021.232
    Citation: Shang Hao, Zhu Henghua, Li Shuang, Song Xiaomei, Xia Yu, Liu Hui, Yang Fan, 2023. A Geological Borehole Data Protection Based on Graph Neural Networks. Earth Science, 48(8): 3151-3161. doi: 10.3799/dqkx.2021.232

    一种基于图神经网络的地质钻孔数据保护方案

    doi: 10.3799/dqkx.2021.232
    基金项目: 

    济南市科技创新发展计划(社会民生专项)项目《数字孪生城市四维可视化信息系统及在济南城区的应用》 232131001

    详细信息
      作者简介:

      尚浩(1983-),男,高级工程师,硕士,从事水工环地质,城市地质及地质环境信息化等工作.ORCID:0000-0002-2221-3774.E-mail:181909920@qq.com

    • 中图分类号: P628

    A Geological Borehole Data Protection Based on Graph Neural Networks

    • 摘要: 随着深度学习技术的日益成熟,攻击者可以对公开的地质钻孔数据通过分类、预测等方法获取潜在的敏感信息,从而造成重要地质数据的泄露. 针对上述问题,提出了一种基于图对抗攻击的地质钻孔数据保护模型Gcntack. 一方面,基于地质数据拓扑图的度特征,产生满足同一幂律分布的攻击作为微小节点扰动,确保对抗性攻击不易被发现,同时改变了目标节点的分类结果. 另一方面,引入注意力机制,使用基于可解释性的图注意力网络模型分析影响对抗攻击结果的关键节点特性,验证Gcntack模型中选取对抗性节点的合理性. 最后,通过在基准数据集和地质钻孔数据集进行的综合实验和分析,证实了提出的地质钻孔数据保护方案能够基于较少的图结构或节点特征的对抗扰动,达到保护重要地质钻孔数据的目的.

       

    • 图  1  基于图对抗攻击和可解释性的地质钻孔数据保护方案

      Fig.  1.  Geological borehole data protection scheme based on graph adversarial attack and interpretability

      图  2  研究区钻孔节点分布图

      Fig.  2.  Distribution map of borehole nodes in the study area

      图  3  按照主要矿种-工作区构建的钻孔散点图

      Fig.  3.  Borehole scatter diagram constructed according to the main ore-working area

      图  4  按照终孔深度-钻孔类型构建的钻孔散点图

      Fig.  4.  Borehole Scatter Diagram constructed according to final hole depth-type of borehole

      图  5  直接攻击与间接攻击的分类准确率

      Fig.  5.  Classification accuracy of direct and indirect attacks

      图  6  结构攻击和特征攻击的分类准确率

      Fig.  6.  Classification accuracy of structural and feature attacks

      图  7  约束对图检验统计量的影响

      Fig.  7.  Influence of constraints on graph test statistics

      图  8  7个钻孔类型中注意力权重高的节点与对抗性节点对应的命中率

      Fig.  8.  The hit ratio of the nodes with high attention weight and the adversarial nodes in the seven drilling types

      图  9  排名前10的地质钻孔注意力权重统计结果

      Fig.  9.  Statistical results of the top 10 geological borehole attention weights

      表  1  数据集统计信息

      Table  1.   Data set statistics

      名称 节点数量 边缘数量 特征 类别
      Cora 2 708 5 429 1 433 7
      Citeseer 3 327 4 732 3 703 6
      Drilling-1 670 35 836 106 7
      Drilling-2 670 95 472 106 7
      下载: 导出CSV

      表  2  数据集上节点分类的准确性(百分比)

      Table  2.   Accuracy of node classification on dataset (percentage)

      Cora Citeseer Drilling-1 Drilling-2
      GCN 0.815 0.701 0.709 0.803
      GAT 0.845 0.725 0.728 0.826
      Gcntack 0.471 0.423 0.410 0.432
      下载: 导出CSV
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    出版历程
    • 收稿日期:  2021-11-23
    • 刊出日期:  2023-08-25

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