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在真实地震数据上基于时间频率的地震和噪声信号分离:EMD,DWT和集成分类器方法

Yunus Emre Erdoğan¹, Ali Narin¹

¹Department of Electrical and Electronics Engineering, Faculty of Engineering, Zonguldak Bülent Ecevit University, Zonguldak 67100, Türkiye.

Sensors (Basel, Switzerland)

|November 13, 2025

概括

这项研究开发了一种自动化方法,使用地震数据的时间频率分析来检测地震. 最好的方法结合了实证模式分解和离散波形变换与随机森林分类,达到100%的准确性.

科学领域:

地质物理学地质物理学
地震学地震学
信号处理信号处理

背景情况:

地震是破坏性的自然事件,需要快速检测以尽量减少损害.
目前的地震监测依赖于准确的信号识别来区分地震和噪音.
自动检测系统对于实时地震预警和减缓至关重要.

研究的目的:

开发和评估一种用于区分地震信号与地震数据中的噪声的自动化方法.
分析各种时频特征提取技术的有效性.
为地震信号分析确定最佳特征选择和分类方法.

主要方法:

使用z-score正常化处理地震信号.
使用实证模式分解 (EMD),离散波纹转换 (DWT) 和组合EMD+DWT进行了特征提取.
使用拉索,ReliefF和学生t测试进行特征选择.
分类是通过集袋树,决策树,随机森林,k-NN和SVM实现的.

主要成果:

随机森林分类器与拉索选择的EMD+DWT功能实现了100%的准确性,特异性和灵敏性.
组合EMD+DWT和DWT功能单独超过了EMD.
与k-NN和SVM相比,基于树的分类器表现出更高的性能.
拉索和ReliefF特征选择方法比Student的t测试更有效.

结论:

时间频率特征对于准确的地震信号检测和噪声分离至关重要.
开发的自动化方法显示了对实时地震监测和预警系统的巨大潜力.
这项研究为改善地震数据分析和地震应对提供了一个强大的框架.

关键词:

这就是为什么DWT DWT DWT 电磁磁指令 (EMD) 是一个电子指令.地震检测检测系统地震检测系统功能选择功能选择机器学习是机器学习.时间频率特征是时间频率特征.

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