现代信息科技2025，Vol.9Issue(8)：41-45,5.DOI:10.19850/j.cnki.2096-4706.2025.08.009
基于CNN-LSTM-CBAM模型的地震前兆重力异常检测研究
Research on Anomaly Detection of Earthquake Precursor Gravity Based on CNN-LSTM-CBAM Model
摘要
Abstract
This research proposes an anomaly detection method in earthquake precursor gravity data based on the CNN-LSTM-CBAM model.The anomaly detection in earthquake precursor gravity data is crucial for improving the timeliness of earthquake predictions.It extracts spatial features of the gravity data using CNN,and uses the LSTM to capture long-term dependency relationships in the time series.The CBAM is introduced to enhance the model's ability to focus on important features,thereby improving anomaly detection performance.Experimental comparisons with the anomaly detection methods such as AutoEncoder,CNN,LSTM,and CNN-LSTM methods show that the proposed model in this paper outperforms others in metrics such as MAE,MSE,RMSE,and R2.This model effectively identifies potential and abnormal data and provides a reliable foundation for earthquake risk management and early warning.This research offers new insights into the analysis of earthquake precursor data.关键词
地震前兆异常/重力数据/时间序列/LSTM/注意力机制Key words
earthquake precursor anomaly/gravity data/time series/LSTM/Attention Mechanism分类
计算机与自动化引用本文复制引用
邢乾龙,刘庆杰..基于CNN-LSTM-CBAM模型的地震前兆重力异常检测研究[J].现代信息科技,2025,9(8):41-45,5.
