干旱区生态环境知识资源中心(Arid): New Suppression Technology for Low-Frequency Noise in Desert Region: The Improved Robust Principal Component Analysis Based on Prediction of Neural Network

Arid

DOI	10.1109/TGRS.2020.2966054
	New Suppression Technology for Low-Frequency Noise in Desert Region: The Improved Robust Principal Component Analysis Based on Prediction of Neural Network
	Dong, Xintong; Zhong, Tie; Li, Yue
通讯作者	Li, Y
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
EISSN	1558-0644
出版年	2020
卷号	58 期号:7 页码:4680-4690
英文摘要	Lots of low-frequency noise including random noise and surface waves seriously reduces the quality of desert seismic data. However, the suppression for desert low-frequency noise faces three main problems: nonstationary and non-Gaussian of random noise; strong energy of low-frequency noise; a more serious frequency-band overlap between effective signals and low-frequency noise. Robust principal component analysis (RPCA) is a classical low-rank matrix (LM) recovery method which is very suitable for processing nonlinear noise. It can decompose noisy data to the optimal LM and sparse matrix (SM), which include most effective signals and noise, respectively. Therefore, the RPCA is introduced to suppress desert low-frequency noise. However, due to the low signal-to-noise ratio (SNR) and serious frequency-band overlap, much low-frequency noise still remains in the LM of desert seismic data after the decomposition of RPCA. Meanwhile, some nonnegligible effective signals are decomposed into the SM of desert seismic data. To solve this problem, the convolutional neural network (CNN) is introduced to extract effective signals from SM and LM. By constructing suitable training sets to guide the CNN's training, the CNN denoising models after training are used to predict the effective signals from these two matrices, respectively. In this article, to approach real desert seismic data, we use a variety of seismic wavelets to simulate different types of seismic events, and then use these synthetic seismic events and real desert low-frequency noise to construct training set. In experiments, our method can raise the SNR of synthetic noisy data from -8.69 to 9.63 dB.
英文关键词	Low-frequency noise Noise reduction Training Noise measurement Sparse matrices Signal to noise ratio Predictive models Convolutional neural network (CNN) desert low-frequency noise low-rank matrix (LM) robust principal component analysis (RPCA) sparse matrix (SM)
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000543775800016
WOS关键词	RANK MATRIX RECOVERY ; SHEARLET TRANSFORM ; MICROSEISMIC DATA ; SEISMIC DATA ; REDUCTION
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/2XILL650/324853
作者单位	[Dong, Xintong; Li, Yue] Jilin Univ, Dept Informat, Coll Commun Engn, Changchun 130012, Peoples R China; [Zhong, Tie] Northeast Elect Power Univ, Dept Commun Engn, Coll Elect Engn, Jilin 132012, Jilin, Peoples R China
推荐引用方式 GB/T 7714	Dong, Xintong,Zhong, Tie,Li, Yue. New Suppression Technology for Low-Frequency Noise in Desert Region: The Improved Robust Principal Component Analysis Based on Prediction of Neural Network[J],2020,58(7):4680-4690.
APA	Dong, Xintong,Zhong, Tie,&Li, Yue.(2020).New Suppression Technology for Low-Frequency Noise in Desert Region: The Improved Robust Principal Component Analysis Based on Prediction of Neural Network.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,58(7),4680-4690.
MLA	Dong, Xintong,et al."New Suppression Technology for Low-Frequency Noise in Desert Region: The Improved Robust Principal Component Analysis Based on Prediction of Neural Network".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58.7(2020):4680-4690.