地震地磁观测与研究2024，Vol.45Issue(2)：20-27,8.DOI:10.3969/j.issn.1003-3246.2024.02.003
基于迁移学习的岩石边坡微地震事件检测算法
A landslide microseismicity detection method based on transfer learning
摘要
Abstract
In this article,we introduce a transfer learning-based landslide microseismicity detection model,which can automatically pick up microseismicity occurring on the slopes in more accurate means.The deep learning model is first trained using a huge amount of manually labeled seismic events to obtain a well pre-trained model,then,the pre-trained model is fine-tuned by a small number of manually labeled microseismic events that have occurred on the slope to account for landslide microseismicity detection.The results suggest that our model achieves a rate of 0.884 and 0.91 in recall and precision test using unknown events that occurred on the slope,respectively.The proposed transfer learning-based training procedure not only significantly reduces the demand on the labeled training data on the slope,but also achieves a more robust and accurate model using a small number of integrations when applied to slopes.We open source the main function of the model,which can also be applied to other slopes.关键词
迁移学习/微地震事件检测/深度学习/边坡Key words
transfer learning/microseismic event detection/deep learning/slope引用本文复制引用
蔡育埼,于子叶..基于迁移学习的岩石边坡微地震事件检测算法[J].地震地磁观测与研究,2024,45(2):20-27,8.基金项目
基于深度神经网络的体波面波联合反演算法,中央级公益性科研院所基本科研业务费(项目编号:DQJB23R31) （项目编号:DQJB23R31）
