基于对抗网络域自适应的矿井隧道漏水与裂缝识别  

Water Leakage and Cracks in Mine Tunnels Based on Adversarial Domain Adaptation Network

作　　者：张洪明 许金星 ZHANG Hongming;XU Jinxing(Jiangsu Vocational College of Electronics and Information,Huaian 223003,China)

机构地区：[1]江苏电子信息职业学院,江苏淮安223003

出　　处：《煤炭技术》2023年第5期159-161,共3页Coal Technology

摘　　要：我国煤炭所处的地质条件十分恶劣,随着矿井深度的增加,隧道异常发生的概率也在增加。通过引用无监督域自适应的方法来改善传统迁移学习的数据集昂贵和标签数据稀少的问题,采取一种基于对抗网络的域自适应方法,将地表的隧道异常样本迁移到矿井隧道的样本中去。通过实验数据显示,该方法比传统的神经网络算法提升了7.99%的准确率,取得了一定的成效。The geological conditions of coal in my country are very bad.As the depth of the mine increases,the probability of tunnel disease also increases.By citing the method of unsupervised domain adaptation to improve the problems of expensive datasets and sparse labeled data in traditional transfer learning,a domain adaptation method based on adversarial network is adopted to transfer the abnormal samples of tunnels on the surface to the ones of mine tunnels sample.The experimental data shows that this method improves the accuracy by 7.99%compared with the traditional neural network algorithm,and has a certain effect.

关 键 词：隧道异常 矿机隧道 域自适应 对抗网络 

分 类 号：TD742.2[矿业工程—矿井通风与安全] TD263