Denoising Method of Deformation Monitoring Data Based on Variational Mode Decomposition

LUO Yiyong; HUANG Cheng; ZHANG Jingying

doi:10.13203/j.whugis20180437

Volume 45 Issue 5

May 2020

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Geomatics and Information Science of Wuhan University > 2020 > 45(5): 784-790. > DOI: 10.13203/j.whugis20180437

LUO Yiyong, HUANG Cheng, ZHANG Jingying. Denoising Method of Deformation Monitoring Data Based on Variational Mode Decomposition[J]. Geomatics and Information Science of Wuhan University, 2020, 45(5): 784-790. DOI: 10.13203/j.whugis20180437

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Denoising Method of Deformation Monitoring Data Based on Variational Mode Decomposition

1.
School of Geodesy and Geomatics Engineering, East China University of Technology, Nanchang 330013, China
2.
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

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The National Natural Science Foundation of China 41861058

The National Natural Science Foundation of China 41664001

the Open Research Fund Program of Key Laboratory for Digital Land and Resources of Jiangxi Province DLLJ201612

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Author Bio:
LUO Yiyong, PhD, associate professor, specializes in deformation data processing method. E‐mail: luoyiyong@whu.edu.cn
Received Date: May 12, 2019
Published Date: May 04, 2020

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Abstract

Abstract

In order to improve the denoising accuracy and reliability of deformation monitoring data, a new denoising algorithm for deformed data is constructed based on variational mode decomposition (VMD). Firstly, the criterion for judging the high frequency noise component of VMD is established, and T index is introduced to determine the optimal K value of VMD denoising. Then, VMD component after eliminating high frequency noise is reconstructed, and the denoising method of VMD deformation data is established. Finally, the denoising methods of VMD, wavelet and empirical mode decomposition (EMD) are compared and analyzed through the examples of simulation signal, bridge deformation data and dam deformation data. The experimental results show that the correlation coefficient, root mean square error and signal-to-noise ratio of VMD are better than those of wavelet and EMD. Therefore, the validity and reliability of VMD denoising method are proved theoretically. When denoising bridge deformation data and dam deformation data, VMD denoising results have better denoising accuracy and smoothness than wavelet and EMD, while retaining the local deformation feature information.
- variational mode decomposition,
- deformation monitoring data,
- denoising,
- precision analysis

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