WPD-Based Noise Reduction for Microseismic Data Through Adaptive Coefficient Shrinkage and Multi-Basis Fusion
In microseismic monitoring, sensors distributed at different spatial positions face varying noise conditions, leading to data quality deterioration. Due to the precise time-frequency analysis capabilities, flexibility, and low computational complexity, Wavelet Packet Decomposition (WPD) has become o...
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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10795862/ |
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| author | Yaqi Zhang Zhiqiang Lan Zheng Shi Yuhang Xue Jie Wang Kun Zhu Jian He Xiujian Chou |
| author_facet | Yaqi Zhang Zhiqiang Lan Zheng Shi Yuhang Xue Jie Wang Kun Zhu Jian He Xiujian Chou |
| author_sort | Yaqi Zhang |
| collection | DOAJ |
| description | In microseismic monitoring, sensors distributed at different spatial positions face varying noise conditions, leading to data quality deterioration. Due to the precise time-frequency analysis capabilities, flexibility, and low computational complexity, Wavelet Packet Decomposition (WPD) has become one of the most widely used noise reduction approaches in microseismic data enhancement. However, there are still notable limitations: 1) Uniform thresholding, which uses a constant threshold across the whole time-frequency node coefficients, confuses microseismic waveform edges and noise, resulting in information loss and noise retention 2) Sensors encounter complex and varied noise characteristics from different environments, making a single wavelet basis inadequate for representing this diversity, which ultimately limits denoising performance. To overcome the above barriers, we present a denoising method based on adaptive coefficient shrinkage and dynamic weighted fusion in this work. Firstly, this method utilizes energy filtering to eliminate noise that is not temporally correlated with microseismic waveforms, then employs time-varying thresholds to refine the edges of these waveforms, effectively leveraging time and amplitude information to preserve the details of waveform edge. Secondly, a short-time feature analysis is conducted based on denoising results from various wavelet bases, implementing time-varying weights, and enhancing noise suppression while retaining waveform information. The experimental results demonstrate that the proposed method significantly improves the clarity of microseismic waveforms, offering a more effective solution for precise analysis and processing than traditional methods. |
| format | Article |
| id | doaj-art-36d216c823aa40b5ae78a96bdde87ddc |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-36d216c823aa40b5ae78a96bdde87ddc2025-01-16T00:01:28ZengIEEEIEEE Access2169-35362024-01-011218921918923210.1109/ACCESS.2024.351714910795862WPD-Based Noise Reduction for Microseismic Data Through Adaptive Coefficient Shrinkage and Multi-Basis FusionYaqi Zhang0Zhiqiang Lan1https://orcid.org/0000-0003-3288-8879Zheng Shi2Yuhang Xue3Jie Wang4Kun Zhu5https://orcid.org/0000-0001-6820-8981Jian He6https://orcid.org/0000-0002-6353-4697Xiujian Chou7https://orcid.org/0000-0001-5677-0934State Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, Shanxi, ChinaSchool of Future Science and Engineering, Soochow University, Suzhou, ChinaSchool of Future Science and Engineering, Soochow University, Suzhou, ChinaState Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, Shanxi, ChinaSchool of Future Science and Engineering, Soochow University, Suzhou, ChinaSchool of Future Science and Engineering, Soochow University, Suzhou, ChinaState Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, Shanxi, ChinaState Science and Technology on Electronic Test and Measurement Laboratory, North University of China, Taiyuan, Shanxi, ChinaIn microseismic monitoring, sensors distributed at different spatial positions face varying noise conditions, leading to data quality deterioration. Due to the precise time-frequency analysis capabilities, flexibility, and low computational complexity, Wavelet Packet Decomposition (WPD) has become one of the most widely used noise reduction approaches in microseismic data enhancement. However, there are still notable limitations: 1) Uniform thresholding, which uses a constant threshold across the whole time-frequency node coefficients, confuses microseismic waveform edges and noise, resulting in information loss and noise retention 2) Sensors encounter complex and varied noise characteristics from different environments, making a single wavelet basis inadequate for representing this diversity, which ultimately limits denoising performance. To overcome the above barriers, we present a denoising method based on adaptive coefficient shrinkage and dynamic weighted fusion in this work. Firstly, this method utilizes energy filtering to eliminate noise that is not temporally correlated with microseismic waveforms, then employs time-varying thresholds to refine the edges of these waveforms, effectively leveraging time and amplitude information to preserve the details of waveform edge. Secondly, a short-time feature analysis is conducted based on denoising results from various wavelet bases, implementing time-varying weights, and enhancing noise suppression while retaining waveform information. The experimental results demonstrate that the proposed method significantly improves the clarity of microseismic waveforms, offering a more effective solution for precise analysis and processing than traditional methods.https://ieeexplore.ieee.org/document/10795862/Adaptive coefficient shrinkageclusteringdynamic weighted fusionmicroseismicwavelet packet decomposition |
| spellingShingle | Yaqi Zhang Zhiqiang Lan Zheng Shi Yuhang Xue Jie Wang Kun Zhu Jian He Xiujian Chou WPD-Based Noise Reduction for Microseismic Data Through Adaptive Coefficient Shrinkage and Multi-Basis Fusion IEEE Access Adaptive coefficient shrinkage clustering dynamic weighted fusion microseismic wavelet packet decomposition |
| title | WPD-Based Noise Reduction for Microseismic Data Through Adaptive Coefficient Shrinkage and Multi-Basis Fusion |
| title_full | WPD-Based Noise Reduction for Microseismic Data Through Adaptive Coefficient Shrinkage and Multi-Basis Fusion |
| title_fullStr | WPD-Based Noise Reduction for Microseismic Data Through Adaptive Coefficient Shrinkage and Multi-Basis Fusion |
| title_full_unstemmed | WPD-Based Noise Reduction for Microseismic Data Through Adaptive Coefficient Shrinkage and Multi-Basis Fusion |
| title_short | WPD-Based Noise Reduction for Microseismic Data Through Adaptive Coefficient Shrinkage and Multi-Basis Fusion |
| title_sort | wpd based noise reduction for microseismic data through adaptive coefficient shrinkage and multi basis fusion |
| topic | Adaptive coefficient shrinkage clustering dynamic weighted fusion microseismic wavelet packet decomposition |
| url | https://ieeexplore.ieee.org/document/10795862/ |
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