基于集成经验模态分解的磁共振全波信号随机噪声抑制方法

doi:10.13229/j.cnki.jdxbgxb.20231282

摘要/Abstract

摘要：

本文采用集成经验模态分解（EEMD）方法对磁共振全波信号进行分解降噪，获取了一系列本征模函数（IMF）分量后，根据自适应降噪原理计算每个IMF分量的能量密度和平均周期，去除噪声主导的IMF分量，将筛选所得的IMF分量进行重构，解决了磁共振全波信号受环境噪声干扰严重的问题。仿真实验数据结果表明，当磁共振信号的信噪比低至-10 dB时，经过EEMD处理后依然能够有效提取磁共振参数，初始振幅 $E 0$ 提取相对误差为1.57%，弛豫时间 $T 2 *$ 提取相对误差为2.96%，信噪比提升至10.31 dB。实测数据的噪声抑制结果进一步验证了本文研究算法的有效性和实用性，为磁共振地下水探测技术在复杂噪声环境下应用提供技术支撑。

关键词: 地面磁共振技术, 磁共振全波信号, 集成经验模态分解, 随机噪声, 数据处理

Abstract:

This article uses the ensemble empirical mode decomposition （EEMD） method to decompose and denoise the magnetic resonance full-wave signal， after obtaining a series of intrinsic mode function （IMF） components， the energy density and average period of each IMF component are calculated based on the principle of adaptive denoising，the IMF components dominated by noise are removed， and the selected IMF components are reconstructed，solved the problem of severe environmental noise interference in magnetic resonance full-wave signals. The simulation experimental data results show that when the signal-to-noise ratio of the magnetic resonance signal is as low as -10 dB， after EEMD processing， the magnetic resonance parameters can still be effectively extracted. The relative error of initial amplitude E₀ extraction is 1.57%， the relative error of relaxation time $T 2 *$ extraction is 2.96%， and the signal-to-noise ratio is improved to 10.31 dB. The noise suppression results of the measured data further validate the effectiveness and practicality of the algorithm studied in this paper， provides technical support for the application of magnetic resonance groundwater detection technology in complex roise environments.

Key words: ground magnetic resonance technology, magnetic resonance full-wave signal, ensemble empirical mode decomposition, random noise, data processing

中图分类号:

TH763

万玲,张嘉麟,李时赫,平清钰. 基于集成经验模态分解的磁共振全波信号随机噪声抑制方法[J]. 吉林大学学报(工学版), 2025, 55(8): 2771-2781.

Ling WAN,Jia-lin ZHANG,Shi-he LI,Qing-yu PING. Random noise suppression method for magnetic resonance full-wave signal with ensemble empirical mode decomposition[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2771-2781.

图/表 16

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表1

图 7

表2

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图 9

图 10

表3

不同信噪比仿真信号经EEMD消噪处理后信号参数提取结果及信噪比"

仿真信号	$E 0 / n V$	$f L / H z$	$T 2 * / m s$	$S N R / d B$
理想信号	200.00	2 330.00	150.00
SNR=10 dB	200.91	2 330.01	149.03	32.587 2
SNR=5 dB	200.01	2 330.03	148.28	27.443 8
SNR=0 dB	202.19	2 330.03	146.95	23.138 6
SNR=-5 dB	202.42	2 330.09	146.22	17.432 6
SNR=-10 dB	203.17	2 329.98	145.56	10.316 7

表3

图 11

图 12

图 13

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IMF分量	RP _j	IMF分量	RP _j
IMF1	0.724	IMF7	0.791
IMF2	0.838	IMF8	0.809
IMF3	0.852	IMF9	0.863
IMF4	1.176	IMF10	0.899
IMF5	2.528	IMF11	0.834
IMF6	0.807	IMF12	0.743

IMF分量	RP _j	IMF分量	RP _j
IMF1	1.509	IMF6	0.296
IMF2	0.175	IMF7	0.271
IMF3	0.137	IMF8	0.200
IMF4	0.069	IMF9	0.596
IMF5	0.034	res	0.480