干旱区生态环境知识资源中心(Arid): Desert Seismic Signal Denoising Based on Unsupervised Feature Learning and Time-Frequency Transform Technique

Arid

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
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).