干旱区生态环境知识资源中心(Arid): Background noise suppression using trainable nonlinear reaction diffusion assisted by robust principle component analysis

Arid

DOI	10.1080/08123985.2020.1738212
	Background noise suppression using trainable nonlinear reaction diffusion assisted by robust principle component analysis
	Jia, Nan; Ma, Haitao; Dong, Xintong; Li, Yue
通讯作者	Dong, Xintong
来源期刊	EXPLORATION GEOPHYSICS
ISSN	0812-3985
EISSN	1834-7533
出版年	2020
卷号	51 期号:6 页码:642-651
英文摘要	Due to the severe interference of background noise, the signal-to-noise ratio of desert seismic data is extremely low. In addition, due to low-frequency characteristics of sand in the Tarim desert region, the background noise in desert seismic data is mainly distributed in low-frequency band, so that the frequency spectrum aliasing of effective signals and background noise is more serious than the general land seismic data. Thus, conventional filtering methods cannot effectively suppress background noise in desert seismic data and recover effective signals. In order to overcome the problem that low-frequency background noise in desert seismic data is hard to suppress, a new method called R-TNRD based on robust principle component analysis (RPCA) algorithm and trainable nonlinear reaction diffusion (TNRD) network is proposed in this paper. By using the good sparsity of RPCA, the input noisy desert seismic data are decomposed into a low-rank matrix and a sparse matrix, and these two matrices contain background noise and effective signals. Due to the serious spectrum aliasing of desert seismic data, conventional thresholds have been unable to extract effective signals from the two matrices obtained by RPCA effectively. Therefore, we introduce TNRD network into desert seismic data denoising. By network training with a low-frequency noise set, the optimisation of TNRD network can be achieved, so as to accurately extract the effective signals from the low-rank matrix and the sparse matrix. In the experimental part, we test the performance of R-TNRD on both synthetic and real seismic data. The results demonstrate that the proposed method can suppress background noise more effectively than conventional methods.
英文关键词	Noise attenuation solid earth geophysics seismic exploration
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000525228600001
WOS类目	Geochemistry & Geophysics
WOS研究方向	Geochemistry & Geophysics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314501
作者单位	Jilin Univ, Dept Informat Engn, Changchun 130026, Peoples R China
推荐引用方式 GB/T 7714	Jia, Nan,Ma, Haitao,Dong, Xintong,et al. Background noise suppression using trainable nonlinear reaction diffusion assisted by robust principle component analysis[J],2020,51(6):642-651.
APA	Jia, Nan,Ma, Haitao,Dong, Xintong,&Li, Yue.(2020).Background noise suppression using trainable nonlinear reaction diffusion assisted by robust principle component analysis.EXPLORATION GEOPHYSICS,51(6),642-651.
MLA	Jia, Nan,et al."Background noise suppression using trainable nonlinear reaction diffusion assisted by robust principle component analysis".EXPLORATION GEOPHYSICS 51.6(2020):642-651.