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

作　　者：邢乾龙 刘庆杰[1] XING Qianlong;LIU Qingjie(Institute of Disaster Prevention,Langfang 065201,China)

机构地区：[1]防灾科技学院,河北廊坊065201

出　　处：《现代信息科技》2025年第8期41-45,共5页Modern Information Technology

摘　　要：研究提出了一种基于CNN-LSTM-CBAM模型的地震前兆重力数据异常检测方法。地震前兆重力数据的异常检测对于提高地震预测的及时性至关重要。使用CNN提取重力数据的空间特征,利用LSTM捕捉时间序列中的长期依赖关系,引入CBAM注意力机制,增强模型对重要特征的关注能力,进而提升异常检测的性能。与AutoEncoder、CNN、LSTM、CNN-LSTM异常检测方法进行实验对比,文章所提模型在MAE、MSE、RMSE和R~2等指标上显著优于其他模型。该模型有效识别潜在的异常数据,为地震风险管理与预警提供了可靠基础,研究为地震前兆数据分析提供了新思路。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 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP39[自动化与计算机技术—控制科学与工程] P315.726[天文地球—地震学]