吉林大学学报(理学版) ›› 2025, Vol. 63 ›› Issue (2): 559-0566.

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基于变分模态分解的地面磁共振谐波消噪方法

王琦1, 刘钊闻2, 杜海龙1, 玄玉波1, 刁庶3   

  1. 1. 吉林大学 通信工程学院, 长春 130012; 2. 吉林大学 仪器科学与电气工程学院, 长春 130061;3. 无锡职业技术学院 控制工程学院, 江苏 无锡 214121
  • 收稿日期:2024-03-22 出版日期:2025-03-26 发布日期:2025-03-26
  • 通讯作者: 刁庶 E-mail:diaoshu@jlu.edu.cn

Harmonic Noise Reduction Method for Surface Magnetic Resonance Based on Variational Mode Decomposition

WANG Qi1, LIU Zhaowen2, DU Hailong1, XUAN Yubo1, DIAO Shu3   

  1. 1. College of Communication Engineering, Jilin University, Changchun 130012, China; 2. College of Instrumentation and Electrical Engineering, Jilin University, Changchun 130061, China; 3. School of Control Engineering, Wuxi Institute of Technology, Wuxi 214121, Jiangsu Province, China
  • Received:2024-03-22 Online:2025-03-26 Published:2025-03-26

摘要: 针对地面磁共振信号非常弱, 极易受电磁噪声干扰的问题, 提出一种基于变分模态分解的地面磁共振谐波消噪方法. 该方法采用基于改进变分模态分解的工频谐波消除方式, 并根据频谱分析设定模态分量数与初始中心频率, 解决了常规谐波建模消噪方法仅能处理单次采集数据而导致的运算效率慢等问题. 实验结果表明, 该方法在多基频或基频随时间变化等复杂噪声场景中, 得到了良好的谐波分量估计效果, 并可快速、 有效地消除工频谐波干扰, 大幅度提升了地面磁共振探测数据信噪比.

关键词: 变分模态分解, 地面磁共振, 谐波干扰, 基频变化

Abstract: Aiming at the problem of very weak surface magnetic resonance  signals  and  susceptibility to electromagnetic noise interference, 
we proposed a harmonic noise reduction method for surface magnetic resonance based on variational mode decomposition.  This method adopted an improved variational mode decomposition-based method for power frequency harmonic elimination, and set the mode number and initial center frequency according to spectral analysis, solving the problem of slow computational efficiency caused by  conventional harmonic modeling denoising methods, which could only handle single-acquisition data. The experimental results show that the method achieves good effect of  harmonic component estimation in complex noise scenarios such as  multiple fundamental  frequencies or fundamental frequency variations over time,  and can  quickly and effectively eliminate power frequency  harmonic interference, significantly improving the signal-to-noise ratio of surface  magnetic resonance detection data.

Key words: variational mode decomposition, surface magnetic resonance, harmonic interference, fundamental frequency variation

中图分类号: 

  • TP631.2