华南地震2025，Vol.45Issue(1)：12-18,7.DOI:10.13512/j.hndz.2025.01.02
基于PRIME-DP预训练模型的滑坡微地震等非天然地震分类研究
Classification of Non-Natural Seismic Events with Landslide Microseismicity as Representative Based on PRIME-DP Pre-Training Model
摘要
Abstract
Classification of natural earthquakes and non-natural seismic events is of great significance for earthquake cataloguing,earthquake early warning,and landslide disaster analysis.The existing traditional research methods need to construct parameters such as the P/S amplitude ratio and spectrum ratio,which limits the possibility of the algorithm to classify complex categories.Although the algorithms based on deep neural networks can classify complex events,most of the current research focuses on the identification of blasting and collapse events,and there is less research on the classification of more types such as landslide microseismicity.More importantly,deep neural network training requires a large amount of manually labeled data,which is relatively scarce for non-natural seismic event,so it is difficult to train models with high precision and high generalization capabilities.Therefore,this paper trained multi-classification models for natural earthquakes,blasting,collapse,and landslide microseismicity events based on the open-source PRIME-DP seismic data processing pre-training model.The transfer learning and training were performed by using the pre-training model data and 262 microseismicity events.The training results show that compared with the deep learning classification model based on STFT features,the accuracy of the proposed model is improved from 79.4%to 94.8%.This indicates that seismic event classification based on the pre-training model can effectively improve the final accuracy.关键词
地震信号分类/地震预训练模型/地震大模型Key words
Classification of seismic signal/Pre-training seismic model/Seismic large model分类
地球科学引用本文复制引用
蔡育埼,邵博,于子叶,姚翔龙,刘路,欧阳金恵..基于PRIME-DP预训练模型的滑坡微地震等非天然地震分类研究[J].华南地震,2025,45(1):12-18,7.基金项目
中国长江三峡集团科学技术研究院相关基金(WWKY-2021-0273,202203016) （WWKY-2021-0273,202203016）
