高校地质学报2025，Vol.31Issue(2)：174-184,11.DOI:10.16108/j.issn1006-7493.2024002
基于生成对抗网络的地质灾害监测异常数据识别方法
Anomaly Data Identification Method for Geological Disaster Monitoring Based on Generate Adversarial Network
摘要
Abstract
Reliable geological hazard warning depends on accurate sensing data.In order to solve the problems of large noise and long time sequence characteristics of geological monitoring sensor data,we propose a method to identify abnormal data of geological disaster monitoring based on generative adversarial network.Firstly,the RandAugment algorithm is used to enrich the diversity of training data and improve the robustness to noise.Secondly,multi-head self-attention mechanism is used to extract long time series features,and the stability of early warning performance is improved by adversarial training mechanism.Experiments on four real-time series sensor data streams extracted from hidden geological disaster points in Shaanxi Province show that the proposed method has a 5%-10%improvement in AUROC and F1 indexes,compared to widely used machine learning methods.关键词
地质灾害监测/传感数据/神经网络/生成对抗网络/异常检测Key words
geological disaster monitoring/sensing data/neural network/generative adversarial network(GAN)/anomaly detection分类
计算机与自动化引用本文复制引用
刘将成,郝光耀,陶虹,徐岩岩,王亨,江先晖,陈群..基于生成对抗网络的地质灾害监测异常数据识别方法[J].高校地质学报,2025,31(2):174-184,11.基金项目
国陕西省重点研发计划项目(2022SF-360) （2022SF-360）
国家自然基金项目(62172335)联合资助 （62172335）
