基于深度学习的地震数据处理及异常检测研究  

Research on Earthquake Data Processing and Abnormality Detection Based on Deep Learning

作　　者：甄齐 文金龙 ZHEN Qi;WEN Jinlong(Xilinhot Earthquake Monitoring Center Station,Xilinhot Inner Mongolia 026000)

机构地区：[1]锡林浩特地震监测中心站,内蒙古锡林浩特026000

出　　处：《软件》2024年第8期60-62,共3页Software

基　　金：内蒙古自治区地震局局长基金课题资助项目(2023QN22)。

摘　　要：地震数据处理与异常检测是地震预测不可或缺的关键步骤,其准确率直接关系到预测结果的可靠性。本研究利用深度学习技术,对地震数据处理与异常检测进行了深入探索。通过分析地震数据的特性,提出了一种基于深度学习的预处理方法,显著提升了数据质量和处理效率。进一步构建了基于深度学习的异常检测模型,该模型能自动学习数据特性,精确识别地震数据中的异常信息,提高了异常检测的精准度。对比实验显示,本研究提出的方法相较于传统方法具有显著优势,能有效提高地震预测的准确率。本研究成果对于提升我国地震预警能力、降低地震灾害损失具有重要的参考价值。Earthquake data processing and abnormality detection are crucial steps in earthquake prediction,and their accuracy directly affects the reliability of prediction results.This study utilizes deep learning technology to conduct in-depth exploration of earthquake data processing and abnormality detection.By analyzing the characteristics of earthquake data,a preprocessing method based on deep learning is proposed,which significantly improves data quality and processing efficiency.Furthermore,an abnormality detection model based on deep learning is constructed,which can automatically learn data characteristics and accurately identify abnormal information in earthquake data,thus improving the accuracy of abnormality detection.Comparative experiments show that the method proposed in this study has significant advantages compared with traditional methods,effectively improving the accuracy of earthquake prediction.The research results have important reference value for enhancing China's earthquake warning capability and reducing earthquake disaster losses.

关 键 词：深度学习 地震数据处理 异常检测 地震预测 地震预警能力 

分 类 号：P315[天文地球—地震学]