Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (8): 2771-2781.doi: 10.13229/j.cnki.jdxbgxb.20231282

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Random noise suppression method for magnetic resonance full-wave signal with ensemble empirical mode decomposition

Ling WAN1,2(),Jia-lin ZHANG1,Shi-he LI1,Qing-yu PING1   

  1. 1.College of Instrumentation & Electrical Engineering，Jilin University，Changchun 130026，China
    2.Key Laboratory of Geo-Information Detection Instruments，Ministry of Education，Changchun 130026，China
  • Received:2023-11-20 Online:2025-08-01 Published:2025-11-14

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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 T2* 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

CLC Number: 

  • TH763

Fig.1

Hydrogen proton magnetic resonance response model"

Fig.2

Simulated MRS full-wave signal"

Fig.3

Simulation data upper and lower envelope lines"

Fig.4

Simulated data mean signal"

Fig.5

MRS full-wave signal and signal s（t）containing random noise"

Fig.6

Decomposition diagram of simulation signal s（t） EEMD"

Table 1

RP j of IMF components obtained from EEMD decomposition"

IMF分量RP jIMF分量RP j
IMF10.724IMF70.791
IMF20.838IMF80.809
IMF30.852IMF90.863
IMF41.176IMF100.899
IMF52.528IMF110.834
IMF60.807IMF120.743

Fig.7

EEMD reconstruction signal diagram"

Table 2

RP j of IMF components obtained from EMD decomposition"

IMF分量RP jIMF分量RP j
IMF11.509IMF60.296
IMF20.175IMF70.271
IMF30.137IMF80.200
IMF40.069IMF90.596
IMF50.034res0.480

Fig.8

EMD reconstruction signal diagram"

Fig.9

Simulated MRS full-wave signal processing results with SNR=10 dB andSNR=5 dB"

Fig.10

Time-frequency map of simulated MRS full-wave signal processing results with SNR=-5dB andSNR=-10 dB"

Table 3

Signal parameter extraction results and signal-to-noise ratio of simulated signals with different signal-to-noise ratios after EEMD denoising processing"

仿真信号E0/nVfL/HzT2*/msSNR/dB
理想信号200.002 330.00150.00
SNR=10 dB200.912 330.01149.0332.587 2
SNR=5 dB200.012 330.03148.2827.443 8
SNR=0 dB202.192 330.03146.9523.138 6
SNR=-5 dB202.422 330.09146.2217.432 6
SNR=-10 dB203.172 329.98145.5610.316 7

Fig.11

Results of the measured data after noise processing"

Fig.12

Processing results of measured data"

Fig.13

3D time-frequency map of measured data processing results"

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