Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1109/LGRS.2021.3067645 |
Desert Seismic Signal Denoising Based on Unsupervised Feature Learning and Time-Frequency Transform Technique | |
Li, Mo; Li, Yue; Tian, Yanan; Wu, Ning | |
通讯作者 | Li, Y (corresponding author), Jilin Univ, Dept Informat, Coll Commun Engn, Changchun 130012, Peoples R China. |
来源期刊 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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ISSN | 1545-598X |
EISSN | 1558-0571 |
出版年 | 2022 |
英文摘要 | Noise reduction is an essential step in seismic exploration. Formally, the random broadband noise in the desert seismic is characterized as nonlinear, nonstationary, and non-Gaussian, and the energy is concentrated mainly in the low-frequency range. Moreover, the reflected signals generally cover the same spectral region as a strong random broadband noise. In this letter, a method combining unsupervised feature learning and the time-frequency transform (TFT) technique is proposed to reduce random broadband noise in desert seismic data. First, as a TFT technique, the variational mode decomposition (VMD) is carried out to decompose the multicomponent desert seismic signal into an ensemble of band-limited modes. Then, we apply an unsupervised feature learning method on each decomposed mode for detecting desert seismic events. Finally, the inverse VMD transform is conducted to obtain the final denoised result. This method is tested on both synthetic and field desert seismic data, demonstrating its preferable performance in reducing random broadband noise and preserving reflected signals. |
英文关键词 | Feature extraction Transforms Broadband communication Noise reduction Time-frequency analysis Thin film transistors Signal to noise ratio Denoising desert seismic random broadband noise feature learning sparse filtering variational mode decomposition (VMD) |
类型 | Article |
语种 | 英语 |
收录类别 | SCI-E |
WOS记录号 | WOS:000733549500001 |
WOS关键词 | BACKGROUND-NOISE ; DECOMPOSITION ; ENHANCEMENT |
WOS类目 | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/374575 |
作者单位 | [Li, Mo; Li, Yue; Tian, Yanan; Wu, Ning] Jilin Univ, Dept Informat, Coll Commun Engn, Changchun 130012, Peoples R China; [Li, Mo] Changchun Univ, Sch Elect & Informat Engn, Changchun 130022, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Mo,Li, Yue,Tian, Yanan,et al. Desert Seismic Signal Denoising Based on Unsupervised Feature Learning and Time-Frequency Transform Technique[J],2022. |
APA | Li, Mo,Li, Yue,Tian, Yanan,&Wu, Ning.(2022).Desert Seismic Signal Denoising Based on Unsupervised Feature Learning and Time-Frequency Transform Technique.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. |
MLA | Li, Mo,et al."Desert Seismic Signal Denoising Based on Unsupervised Feature Learning and Time-Frequency Transform Technique".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022). |
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