Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion
Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/10/11/3790 |
| _version_ | 1797566679508582400 |
|---|---|
| author | Jinyong Zhang Linlu Dong Nuwen Xu |
| author_facet | Jinyong Zhang Linlu Dong Nuwen Xu |
| author_sort | Jinyong Zhang |
| collection | DOAJ |
| description | Microseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (<i>SNR</i>s) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the <i>SNR</i>s of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics. |
| first_indexed | 2024-03-10T19:29:55Z |
| format | Article |
| id | doaj.art-a6ae4a1f6b294aff8356e41ae628607d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T19:29:55Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a6ae4a1f6b294aff8356e41ae628607d2023-11-20T02:12:32ZengMDPI AGApplied Sciences2076-34172020-05-011011379010.3390/app10113790Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information CriterionJinyong Zhang0Linlu Dong1Nuwen Xu2State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaMicroseismic (MS) signals recorded by sensors are often mixed with various noise, which produce some interference to the further analysis of the collected data. One problem of many existing noise suppression methods is to deal with noisy signals in a unified strategy, which results in low-frequency noise in the non-microseismic section remaining. Based on this, we have developed a novel MS denoising method combining variational mode decomposition (VMD) and Akaike information criterion (AIC). The method first applied VMD to decompose a signal into several limited-bandwidth intrinsic mode functions and adaptively determined the effective components by the difference of correlation coefficient. After reconstructing, the improved AIC method was used to determine the location of the valuable waveform, and the residual fluctuations in other positions were further removed. A synthetic wavelet signal and some synthetic MS signals with different signal-to-noise ratios (<i>SNR</i>s) were used to test its denoising effect with ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), and the VMD method. The experimental results depicted that the <i>SNR</i>s of the proposed method were obviously larger than that of other methods, and the waveform and spectrum became cleaner based on VMD. The processing results of the MS signal of Shuangjiangkou Hydropower Station also illustrated its good denoising ability and robust performance to signals with different characteristics.https://www.mdpi.com/2076-3417/10/11/3790microseismic signalnoise suppressionvariational mode decompositionakaike information criterion |
| spellingShingle | Jinyong Zhang Linlu Dong Nuwen Xu Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion Applied Sciences microseismic signal noise suppression variational mode decomposition akaike information criterion |
| title | Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion |
| title_full | Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion |
| title_fullStr | Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion |
| title_full_unstemmed | Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion |
| title_short | Noise Suppression of Microseismic Signals via Adaptive Variational Mode Decomposition and Akaike Information Criterion |
| title_sort | noise suppression of microseismic signals via adaptive variational mode decomposition and akaike information criterion |
| topic | microseismic signal noise suppression variational mode decomposition akaike information criterion |
| url | https://www.mdpi.com/2076-3417/10/11/3790 |
| work_keys_str_mv | AT jinyongzhang noisesuppressionofmicroseismicsignalsviaadaptivevariationalmodedecompositionandakaikeinformationcriterion AT linludong noisesuppressionofmicroseismicsignalsviaadaptivevariationalmodedecompositionandakaikeinformationcriterion AT nuwenxu noisesuppressionofmicroseismicsignalsviaadaptivevariationalmodedecompositionandakaikeinformationcriterion |