可靠的地质灾害预警依赖于准确的传感数据。论文针对地质监测传感数据噪声大和长时序特征难以捕捉的等问题，提出一种基于生成对抗网络的地质灾害监测异常数据识别方法。该方法首先引入随机数据增强策略，丰富了训练数据的多样性，提升了模型对噪声的鲁棒性；其次，采用多头自注意力机制提取长时序特征，并通过对抗训练机制提高模型预警性能的稳定性。通过在陕西省地质灾害隐患点提取的4个真实时序传感数据流的实验表明，文章提出的方法在AUROC和F1指标上较现有的机器学习对比方法有5%~10%的提升。

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.