Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition
Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/5/806 |
| _version_ | 1797263965424713728 |
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| author | Kang Xing Shiyan Li Zhijie Qu Xiaojuan Zhang |
| author_facet | Kang Xing Shiyan Li Zhijie Qu Xiaojuan Zhang |
| author_sort | Kang Xing |
| collection | DOAJ |
| description | Noise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques. |
| first_indexed | 2024-04-25T00:21:23Z |
| format | Article |
| id | doaj.art-ad9bc6605e9e4aae9cafba9e6f2ce29d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-25T00:21:23Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ad9bc6605e9e4aae9cafba9e6f2ce29d2024-03-12T16:54:04ZengMDPI AGRemote Sensing2072-42922024-02-0116580610.3390/rs16050806Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode DecompositionKang Xing0Shiyan Li1Zhijie Qu2Xiaojuan Zhang3Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaTianjin Navigation Instruments Research Institute, Tianjin 300131, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaNoise suppression is essential in time-domain electromagnetic (TDEM) data processing and interpretation. TDEM data are typically in broadband signal, which makes it difficult to separate the signal in the whole frequency band. The conventional methods tend to process data trace by trace, ignoring the lateral continuity between channels. This paper proposes a workflow based on multivariate variational mode decomposition (MVMD) and multivariate detrended fluctuation analysis (MDFA) to deal with the noise in 2-D TDEM data. The proposed method initially employs MVMD to decompose TDEM signals into a series of intrinsic mode functions (IMFs). Subsequently, MDFA is used to calculate the scaling exponent of each IMF, facilitating the selection of signal-dominant IMFs. Finally, the signal IMFs are summed up to reconstruct the TDEM signal. Both simulation and field results demonstrate that, by considering the lateral continuity of data across channels, the proposed method is more effective at noise removal than other single-channel data processing techniques.https://www.mdpi.com/2072-4292/16/5/806time-domain electromagneticsubsurface target detectionmultivariate variational mode decomposition (MVMD)denoising |
| spellingShingle | Kang Xing Shiyan Li Zhijie Qu Xiaojuan Zhang Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition Remote Sensing time-domain electromagnetic subsurface target detection multivariate variational mode decomposition (MVMD) denoising |
| title | Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition |
| title_full | Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition |
| title_fullStr | Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition |
| title_full_unstemmed | Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition |
| title_short | Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition |
| title_sort | time domain electromagnetic noise suppression using multivariate variational mode decomposition |
| topic | time-domain electromagnetic subsurface target detection multivariate variational mode decomposition (MVMD) denoising |
| url | https://www.mdpi.com/2072-4292/16/5/806 |
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