干旱区生态环境知识资源中心(Arid): Multi-scale DCFF network: a new desert low-frequency noise suppression method

Arid

DOI	10.1007/s40328-021-00339-3
	Multi-scale DCFF network: a new desert low-frequency noise suppression method
	Li, Yue; Zhao, Zhen; Tian, Yanan; Feng, Qiankun
通讯作者	Tian, YA (corresponding author), Jilin Univ, Coll Commun & Engn, Changchun 130012, Jilin, Peoples R China.
来源期刊	ACTA GEODAETICA ET GEOPHYSICA
ISSN	2213-5812
EISSN	2213-5820
出版年	2021
卷号	56 期号:2 页码:357-371
英文摘要	Seismic exploration is an essential way for stratigraphic information acquisition and resource exploitation. However, the unique near surface conditions of the desert region in northwest China pose a special problem. On the one hand, compared with Gaussian noise, desert noise concentrates in the low-frequency band, which is seriously overlapped with those of the effective signals; On the other hand, its nonstationary, nonlinear, and non-Gaussian characteristics seriously affect the accuracy of weak signal recovery and increase the difficulty of noise suppression. In the collected field data, effective signals are often submerged by the intense low-frequency noise. Considering that traditional denoising methods have some limitations on this kind of noise, a new multi-scale dense connection feature fusion (MS-DCFF) denoising convolution neural network is presented in this paper. This denoising network can adaptively learn the potential features of effective signals through multi-scale feature fusion techniques and increase the degree of information exchange by utilizing the dense connections between different blocks. Moreover, we construct relatively complete training dataset containing an effective signal dataset and a noise dataset for desert low-frequency noise suppression, thereby boosting the feasibility of the network for desert noise suppression. Both simulation and field data prove that the superior performance of the proposed MS-DCFF in the aspect of low-frequency noise (mainly includes random noise and surface waves) suppression and effective signal recovery, compared with band-pass filtering, f-x filtering, and DnCNNs.
英文关键词	Complicated low-frequency noise Desert seismic data Dense connection Multi-scale feature fusion
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000642036200001
WOS关键词	ATTENUATION ; SIGNAL
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/2XILL650/349253
作者单位	[Li, Yue; Zhao, Zhen; Tian, Yanan; Feng, Qiankun] Jilin Univ, Coll Commun & Engn, Changchun 130012, Jilin, Peoples R China
推荐引用方式 GB/T 7714	Li, Yue,Zhao, Zhen,Tian, Yanan,et al. Multi-scale DCFF network: a new desert low-frequency noise suppression method[J],2021,56(2):357-371.
APA	Li, Yue,Zhao, Zhen,Tian, Yanan,&Feng, Qiankun.(2021).Multi-scale DCFF network: a new desert low-frequency noise suppression method.ACTA GEODAETICA ET GEOPHYSICA,56(2),357-371.
MLA	Li, Yue,et al."Multi-scale DCFF network: a new desert low-frequency noise suppression method".ACTA GEODAETICA ET GEOPHYSICA 56.2(2021):357-371.