Classification of Non-Natural Seismic Events with Landslide Microseismicity as Representative Based on PRIME-DP Pre-Training Model
CAI Yuqi
SHAO Bo
YU Ziye
YAO Xianglong
LIU Lu
OUYANG Jinhui
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.
Keywords：Classification of seismic signalPre-training seismic modelSeismic large model
Publication Date：2025-03-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 12-18 )