基于迁移学习的岩石边坡微地震事件检测算法OA

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.