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Peer-reviewedCC BY 4.0PLOS One

Research on magnetotelluric signal noise separation based on improved SVD

  • Xingtong Yue1
  • Chang Liu2
  • Aibing Jin3
  • Xiaobo Liu4
  • Liancheng Wang5
  • Huaiyuan Wang6
  • Fuming Qu
  • Rui Zhou
6 affiliations
  1. 1School of Resource and Safety Engineering, University of Science and Technology Beijing, Beijing, China
  2. 2Institute of Minerals Research, University of Science and Technology Beijing, Beijing, China
  3. 3Ansteel Beijing Research Institute Co., Ltd., Beijing, China
  4. 4Ansteel Group Mining Co., Ltd., Anshan, Liaoning, China
  5. 5Institute of Smart Mining, University of Science and Technology Liaoning, Anshan, China
  6. 6College of Geoexploration Science and Technology, Jilin University, Changchun, China
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Abstract

As the impact of cultural noise on magnetotelluric impedance estimation becomes increasingly severe, we propose a magnetotelluric signal-to-noise separation method based on improved Singular Value Decomposition (SVD). Compared to cultural noise, the amplitude of the useful electromagnetic signal is weaker. Based on the difference in amplitude between the noise and the useful signal, the SVD method can separate the noisy components from the original signal. However, traditional SVD struggles to completely extract and remove noise when separating different components, and residual noise often exists at the boundary of noise waveforms. Therefore, by combining the Complementary Ensemble Empirical Mode Decomposition (CEEMD) to improve the SVD method, the goal of effectively separating useful magnetotelluric signals and cultural noise is achieved. The proposed method is applied to synthetic cases and actual data processing. Compared with traditional SVD and CEEMD, the results showed that the proposed method improved the problem of incomplete noise suppression in the SVD method. It achieved good processing results for large-scale noise and impulse noise in low signal-to-noise ratio data, and can reduce the distortion of the apparent resistivity phase curve. Furthermore, compared with the processing results of traditional robust methods, the proposed method exhibits superior noise resistance and restores smoother response curves.

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Cite

BibTeXRIS
BibTeX
@article{yue2026research,
  title = {{Research on magnetotelluric signal noise separation based on improved SVD}},
  author = {Xingtong Yue and Chang Liu and Aibing Jin and Xiaobo Liu and Liancheng Wang and Huaiyuan Wang and Fuming Qu and Rui Zhou},
  journal = {PLOS One},
  year = {2026},
  month = nov,
  doi = {10.1371/journal.pone.0334468},
  url = {https://doi.org/10.1371/journal.pone.0334468},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Research on magnetotelluric signal noise separation based on improved SVD
AU  - Xingtong Yue
AU  - Chang Liu
AU  - Aibing Jin
AU  - Xiaobo Liu
AU  - Liancheng Wang
AU  - Huaiyuan Wang
AU  - Fuming Qu
AU  - Rui Zhou
PY  - 2026
DA  - 2026/11/05
JO  - PLOS One
DO  - 10.1371/journal.pone.0334468
UR  - https://doi.org/10.1371/journal.pone.0334468
AB  - As the impact of cultural noise on magnetotelluric impedance estimation becomes increasingly severe, we propose a magnetotelluric signal-to-noise separation method based on improved Singular Value Decomposition (SVD). Compared to cultural noise, the amplitude of the useful electromagnetic signal is weaker. Based on the difference in amplitude between the noise and the useful signal, the SVD method can separate the noisy components from the original signal. However, traditional SVD struggles to completely extract and remove noise when separating different components, and residual noise often exists at the boundary of noise waveforms. Therefore, by combining the Complementary Ensemble Empirical Mode Decomposition (CEEMD) to improve the SVD method, the goal of effectively separating useful magnetotelluric signals and cultural noise is achieved. The proposed method is applied to synthetic cases and actual data processing. Compared with traditional SVD and CEEMD, the results showed that the proposed method improved the problem of incomplete noise suppression in the SVD method. It achieved good processing results for large-scale noise and impulse noise in low signal-to-noise ratio data, and can reduce the distortion of the apparent resistivity phase curve. Furthermore, compared with the processing results of traditional robust methods, the proposed method exhibits superior noise resistance and restores smoother response curves.
SN  - 1932-6203
ER  -