CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas
The vertical time series of deformation monitoring data are often interfered with by multiple noises, which makes it difficult to extract useful information. In order to effectively filter out the noise components in the deformation monitoring data, this paper proposes a time series noise reduction...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11104493/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849394072557453312 |
|---|---|
| author | Xu Yang Xinxin Yao Xinjian Fang Xuexiang Yu Yi Wu Shicheng Xie |
| author_facet | Xu Yang Xinxin Yao Xinjian Fang Xuexiang Yu Yi Wu Shicheng Xie |
| author_sort | Xu Yang |
| collection | DOAJ |
| description | The vertical time series of deformation monitoring data are often interfered with by multiple noises, which makes it difficult to extract useful information. In order to effectively filter out the noise components in the deformation monitoring data, this paper proposes a time series noise reduction method (CPO-VMD-MPE) that integrates the crested porcupine optimizer (CPO), the variational modal decomposition (VMD), and the multiscale permutation entropy (MPE). The method uses the CPO algorithm to optimize the key parameters of the VMD, determines the high-frequency components with MPE values higher than a set threshold as noise components and removes them, and then reconstructs the remaining components in order to obtain the noise-reduced time series. The results of simulation and example analysis show that compared with wavelet denoising (WD), empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), this method shows significant advantages in the evaluation indexes of noise reduction effect—Pearson’s correlation coefficient (R), the signal-to-noise ratio (SNR), and the root-mean-square error (RMSE)—and all the indexes are better than the comparative methods. Taken together, the CPO-VMD-MPE method proposed in this paper significantly reduces the noise in the time series and provides a better theoretical and methodological reference for deformation analysis and prediction. |
| format | Article |
| id | doaj-art-a1d28b5575d047f9bd2fc8f46c57380a |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a1d28b5575d047f9bd2fc8f46c57380a2025-08-20T03:40:11ZengIEEEIEEE Access2169-35362025-01-011313605013606110.1109/ACCESS.2025.359401111104493CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining AreasXu Yang0https://orcid.org/0000-0001-9117-6156Xinxin Yao1Xinjian Fang2Xuexiang Yu3Yi Wu4Shicheng Xie5Engineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan, ChinaEngineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan, ChinaEngineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan, ChinaEngineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan, ChinaEngineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan, ChinaEngineering Research Center of Mining Area Environmental and Disaster Cooperative Monitoring, Anhui University of Science and Technology, Huainan, ChinaThe vertical time series of deformation monitoring data are often interfered with by multiple noises, which makes it difficult to extract useful information. In order to effectively filter out the noise components in the deformation monitoring data, this paper proposes a time series noise reduction method (CPO-VMD-MPE) that integrates the crested porcupine optimizer (CPO), the variational modal decomposition (VMD), and the multiscale permutation entropy (MPE). The method uses the CPO algorithm to optimize the key parameters of the VMD, determines the high-frequency components with MPE values higher than a set threshold as noise components and removes them, and then reconstructs the remaining components in order to obtain the noise-reduced time series. The results of simulation and example analysis show that compared with wavelet denoising (WD), empirical modal decomposition (EMD), ensemble empirical mode decomposition (EEMD), and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), this method shows significant advantages in the evaluation indexes of noise reduction effect—Pearson’s correlation coefficient (R), the signal-to-noise ratio (SNR), and the root-mean-square error (RMSE)—and all the indexes are better than the comparative methods. Taken together, the CPO-VMD-MPE method proposed in this paper significantly reduces the noise in the time series and provides a better theoretical and methodological reference for deformation analysis and prediction.https://ieeexplore.ieee.org/document/11104493/GNSStime series analysiscrested porcupine optimizerVMDMPEnoise reduction methods |
| spellingShingle | Xu Yang Xinxin Yao Xinjian Fang Xuexiang Yu Yi Wu Shicheng Xie CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas IEEE Access GNSS time series analysis crested porcupine optimizer VMD MPE noise reduction methods |
| title | CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas |
| title_full | CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas |
| title_fullStr | CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas |
| title_full_unstemmed | CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas |
| title_short | CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas |
| title_sort | cpo vmd combined with multiscale permutation entropy for noise reduction in gnss vertical time series in mining areas |
| topic | GNSS time series analysis crested porcupine optimizer VMD MPE noise reduction methods |
| url | https://ieeexplore.ieee.org/document/11104493/ |
| work_keys_str_mv | AT xuyang cpovmdcombinedwithmultiscalepermutationentropyfornoisereductioningnssverticaltimeseriesinminingareas AT xinxinyao cpovmdcombinedwithmultiscalepermutationentropyfornoisereductioningnssverticaltimeseriesinminingareas AT xinjianfang cpovmdcombinedwithmultiscalepermutationentropyfornoisereductioningnssverticaltimeseriesinminingareas AT xuexiangyu cpovmdcombinedwithmultiscalepermutationentropyfornoisereductioningnssverticaltimeseriesinminingareas AT yiwu cpovmdcombinedwithmultiscalepermutationentropyfornoisereductioningnssverticaltimeseriesinminingareas AT shichengxie cpovmdcombinedwithmultiscalepermutationentropyfornoisereductioningnssverticaltimeseriesinminingareas |