An Improved Wavelet Soft-Threshold Function Integrated with SVMD Dual-Parameter Joint Denoising for Ancient Building Deformation Monitoring
In deformation monitoring, complex environments, such as seismic excitation, often lead to noise during signal acquisition and transmission processing. This study integrates sequential variational mode decomposition (SVMD), a dual-parameter (DP) model, and an improved wavelet threshold function (IWT...
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MDPI AG
2025-06-01
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| author | Jiaxing Zhao Houzeng Han Yang Deng Youqiang Dong Jian Wang Wenjin Chen |
| author_facet | Jiaxing Zhao Houzeng Han Yang Deng Youqiang Dong Jian Wang Wenjin Chen |
| author_sort | Jiaxing Zhao |
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| description | In deformation monitoring, complex environments, such as seismic excitation, often lead to noise during signal acquisition and transmission processing. This study integrates sequential variational mode decomposition (SVMD), a dual-parameter (DP) model, and an improved wavelet threshold function (IWT), presenting a denoising method termed SVMD-DP-IWT. Initially, SVMD decomposes the signal to obtain intrinsic mode functions (IMFs). Subsequently, the DP parameters are determined using fuzzy entropy. Finally, the noisy IMFs denoised by IWT and the signal IMFs are used for signal reconstruction. Both simulated and engineering measurements validate the performance of the proposed method in mitigating noise. In simulation experiments, compared to wavelet soft-threshold function (WST) with the sqtwolog threshold, the root-mean-square error (RMSE) of SVMD-Dual-CC-WST (sqtwolog threshold), SVMD-DP-IWT (sqtwolog threshold), and SVMD-DP-IWT (minimaxi threshold) improved by 51.44%, 52.13%, and 52.49%, respectively. Global navigation satellite system (GNSS) vibration monitoring was conducted outdoors, and the accelerometer vibration monitoring experiment was performed on a pseudo-classical building in a multi-functional shaking table laboratory. GNSS displacement data and acceleration data were collected, and analyses of the acceleration signal characteristics were performed. SVMD-DP-IWT (sqtwolog) and SVMD-DP-IWT (minimaxi) effectively retain key vibration signal features during the denoising process. The proposed method significantly preserves vibration features during noise reduction of an ancient building in deformation monitoring, which is crucial for damage assessment. |
| format | Article |
| id | doaj-art-e91b993a9b1d4499b62bac7d039cdb97 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-e91b993a9b1d4499b62bac7d039cdb972025-08-20T03:27:36ZengMDPI AGRemote Sensing2072-42922025-06-011712205710.3390/rs17122057An Improved Wavelet Soft-Threshold Function Integrated with SVMD Dual-Parameter Joint Denoising for Ancient Building Deformation MonitoringJiaxing Zhao0Houzeng Han1Yang Deng2Youqiang Dong3Jian Wang4Wenjin Chen5School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Civil and Transportation Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Civil and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, ChinaIn deformation monitoring, complex environments, such as seismic excitation, often lead to noise during signal acquisition and transmission processing. This study integrates sequential variational mode decomposition (SVMD), a dual-parameter (DP) model, and an improved wavelet threshold function (IWT), presenting a denoising method termed SVMD-DP-IWT. Initially, SVMD decomposes the signal to obtain intrinsic mode functions (IMFs). Subsequently, the DP parameters are determined using fuzzy entropy. Finally, the noisy IMFs denoised by IWT and the signal IMFs are used for signal reconstruction. Both simulated and engineering measurements validate the performance of the proposed method in mitigating noise. In simulation experiments, compared to wavelet soft-threshold function (WST) with the sqtwolog threshold, the root-mean-square error (RMSE) of SVMD-Dual-CC-WST (sqtwolog threshold), SVMD-DP-IWT (sqtwolog threshold), and SVMD-DP-IWT (minimaxi threshold) improved by 51.44%, 52.13%, and 52.49%, respectively. Global navigation satellite system (GNSS) vibration monitoring was conducted outdoors, and the accelerometer vibration monitoring experiment was performed on a pseudo-classical building in a multi-functional shaking table laboratory. GNSS displacement data and acceleration data were collected, and analyses of the acceleration signal characteristics were performed. SVMD-DP-IWT (sqtwolog) and SVMD-DP-IWT (minimaxi) effectively retain key vibration signal features during the denoising process. The proposed method significantly preserves vibration features during noise reduction of an ancient building in deformation monitoring, which is crucial for damage assessment.https://www.mdpi.com/2072-4292/17/12/2057deformation monitoringfuzzy entropysequential variational mode decompositiondual-parametersignal denoising |
| spellingShingle | Jiaxing Zhao Houzeng Han Yang Deng Youqiang Dong Jian Wang Wenjin Chen An Improved Wavelet Soft-Threshold Function Integrated with SVMD Dual-Parameter Joint Denoising for Ancient Building Deformation Monitoring Remote Sensing deformation monitoring fuzzy entropy sequential variational mode decomposition dual-parameter signal denoising |
| title | An Improved Wavelet Soft-Threshold Function Integrated with SVMD Dual-Parameter Joint Denoising for Ancient Building Deformation Monitoring |
| title_full | An Improved Wavelet Soft-Threshold Function Integrated with SVMD Dual-Parameter Joint Denoising for Ancient Building Deformation Monitoring |
| title_fullStr | An Improved Wavelet Soft-Threshold Function Integrated with SVMD Dual-Parameter Joint Denoising for Ancient Building Deformation Monitoring |
| title_full_unstemmed | An Improved Wavelet Soft-Threshold Function Integrated with SVMD Dual-Parameter Joint Denoising for Ancient Building Deformation Monitoring |
| title_short | An Improved Wavelet Soft-Threshold Function Integrated with SVMD Dual-Parameter Joint Denoising for Ancient Building Deformation Monitoring |
| title_sort | improved wavelet soft threshold function integrated with svmd dual parameter joint denoising for ancient building deformation monitoring |
| topic | deformation monitoring fuzzy entropy sequential variational mode decomposition dual-parameter signal denoising |
| url | https://www.mdpi.com/2072-4292/17/12/2057 |
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