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.

With the development of deep learning technology, attackers can obtain potentially sensitive information from public geological data through classification, prediction, and other methods, which could lead to the leakage of important geological data. To solve the above problems, we propose a geological drilling data protection model based on graph adversarial attack Gcntack.Based on the degree properties of geological data topology, we first generate attacks that satisfy the same power-law distribution as tiny node disturbance. It can ensure that the adversarial attacks are not easy to be found, and while can change the classification result of the target node. Secondly, we introduce an attention mechanism. Using a graph attention network model based on interpretability, we analyze the properties of key nodes that directly affect the results of the adversarial attacks, so as to verify the rationality of the selecting adversarial nodes in the Gcntack model. Finally, a comprehensive evaluation, based on the benchmark dataset and geological drilling dataset, is presented to show this proposed scheme can reduce the prediction accuracy of attackers and achieve the purpose of protecting important geological drilling data.