干旱区生态环境知识资源中心(Arid): A SNR Enhancement Method for Desert Seismic Data: Simplified Low-Rank Selection in Time-Frequency Decomposition Domain

Arid

DOI	10.1007/s00024-021-02789-w
	A SNR Enhancement Method for Desert Seismic Data: Simplified Low-Rank Selection in Time-Frequency Decomposition Domain
	Wu, Ning; Li, Yue; Yan, Jie; Ma, Haitao
通讯作者	Wu, N (corresponding author), Jilin Univ, Coll Commun Engn, Changchun 130012, Peoples R China.
来源期刊	PURE AND APPLIED GEOPHYSICS
ISSN	0033-4553
EISSN	1420-9136
出版年	2021
卷号	178 期号:8 页码:2905-2916
英文摘要	In seismic data processing, low-frequency random noise with non-Gaussian and non-stationary characteristics heavily contaminates the reflected signals in Tarim area, which brings great difficulties in interpretation of seismic records in northwest China. To achieve more satisfied resolution, more greater fidelity, together with much higher increased signal-to-noise ratio (SNR), this paper proposes a SNR enhancement method based on the combination of variational mode decomposition (VMD) and Semi-soft Go Decomposition (Semi-Soft GoDec), named VMD-SSGoDec, which can realize the simplification of low-rank extraction in time-frequency representation (TFR) domain. Firstly, each trace of the rough seismic record is decomposed into several modes to reconstruct a component matrix by VMD. Due to the semi-low rank or approximate low-rank character of the desert low-frequency noise component matrix in TFR domain, secondly, we apply the Semi-soft GoDec, a low-rank matrix estimation to extract the low-frequency random noise components from the VMD results obtained in the first step. Repeating the above single-trace procedure to each trace rather than decomposing the entire record but use low-rank estimation once can lead to a more reduced dimension of the component matrix, and thus simplify the low-rank selection in Semi-soft GoDec. Finally, with the extracted random noise results in the second step, we can obtain the denoised record by making a difference with the original input. The proposed algorithm is tested by both synthetic record and field desert seismic data. Experimental results show outstanding advantages in low-frequency noise attenuation comparing with those of f-x deconvolution and SSWT-OptShrink. Both low-frequency random noise and surface waves are almost thoroughly attenuated by the proposed method, while the reflected signals are left nearly intact, revealing a significant enhancement in SNR.
英文关键词	Variational mode decomposition (VMD) Semi-soft GoDec SNR enhancement Low-rank matrix approximation Time-frequency representation
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000663274000002
WOS关键词	RANDOM NOISE
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/2XILL650/352439
作者单位	[Wu, Ning; Li, Yue; Yan, Jie; Ma, Haitao] Jilin Univ, Coll Commun Engn, Changchun 130012, Peoples R China
推荐引用方式 GB/T 7714	Wu, Ning,Li, Yue,Yan, Jie,et al. A SNR Enhancement Method for Desert Seismic Data: Simplified Low-Rank Selection in Time-Frequency Decomposition Domain[J],2021,178(8):2905-2916.
APA	Wu, Ning,Li, Yue,Yan, Jie,&Ma, Haitao.(2021).A SNR Enhancement Method for Desert Seismic Data: Simplified Low-Rank Selection in Time-Frequency Decomposition Domain.PURE AND APPLIED GEOPHYSICS,178(8),2905-2916.
MLA	Wu, Ning,et al."A SNR Enhancement Method for Desert Seismic Data: Simplified Low-Rank Selection in Time-Frequency Decomposition Domain".PURE AND APPLIED GEOPHYSICS 178.8(2021):2905-2916.