GNSS vertical time series denoising method for mining area subsidence monitoring

ZHENG Canguang; ZHENG Hui; XIE Shicheng; ZHU Mingfei; HAN Yuchen; YANG Xu

doi:10.12265/j.gnss.2024002

Volume 49 Issue 3

May 2024

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GNSS World of China > 2024 > 49(3): 28-37. > DOI: 10.12265/j.gnss.2024002

ZHENG Canguang, ZHENG Hui, XIE Shicheng, ZHU Mingfei, HAN Yuchen, YANG Xu. GNSS vertical time series denoising method for mining area subsidence monitoring[J]. GNSS World of China, 2024, 49(3): 28-37. DOI: 10.12265/j.gnss.2024002

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GNSS vertical time series denoising method for mining area subsidence monitoring

ZHENG Canguang^1,,
ZHENG Hui^1,,
XIE Shicheng^{2, 3, 4, 5, 6, ,},
ZHU Mingfei^{2, 3, 4, 5, 6},
HAN Yuchen^{2, 3, 4, 5, 6},
YANG Xu^{2, 3, 4, 5}

1.
Yankuang Energy Group Co., Ltd., zoucheng 273500, China
2.
School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China
3.
Urban 3D Real Scene and Intelligent Security Monitoring Joint Laboratory of Anhui Province, Huainan 232001, China
4.
Coal Industry Engineering Research Center of Mining Area Environmental And Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan 232001, China
5.
Key Laboratory of Aviation-aerospace-ground Cooperative Monitoring and Early Warning of Coal Mining-induced Disasters of Anhui Higher Education Institutes, Anhui University of Science and Technology, KLAHEI (KLAHEI18015), Huainan 232001, China
6.
School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China

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Received Date: January 02, 2024
Available Online: May 13, 2024

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Abstract

Abstract

The GNSS technology, as an important tool for mining subsidence monitoring, is significantly affected by the noise present in its time series. This paper proposes a denoising method that combines an Improved hybrid grey wolf particle swarm optimization (IPSOGWO) and an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), coupled with wavelet thresholding (WT). The IPSOGWO optimizes the hyperparameters of the ICEEMDAN algorithm to decompose the GNSS time series and extract the intrinsic mode functions (IMF). The multi-scale permutation entropy is used to select the IMF components containing noise. These components are then secondarily processed using wavelet thresholding and reconstructed with the remaining IMF components to obtain the denoised results. Experiments with simulated signals and actual data from an automated monitoring station in a mining area demonstrate that the proposed method outperforms the wavelet threshold, complete ensemble empirical mode decomposition (CEEMD), and GWO-ICEEMDAN in terms of denoising performance, providing reliable data for subsequent analysis of working face subsidence.
- GNSS,
- ICEEMDAN,
- wavelet thresholding,
- mining area monitoring,
- time series denoising.

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References

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