干旱区生态环境知识资源中心(Arid): Desert Noise Suppression for Seismic Data Based on Feature Enhancement Denoising Network

Arid

DOI	10.1134/S1069351321060136
	Desert Noise Suppression for Seismic Data Based on Feature Enhancement Denoising Network
	Li, Juan; An, Ran; Li, Yue; Zhao, Yuxing
通讯作者	Li, Y (corresponding author), Jilin Univ, Coll Commun Engn, Changchun, Peoples R China.
来源期刊	IZVESTIYA-PHYSICS OF THE SOLID EARTH
ISSN	1069-3513
EISSN	1555-6506
出版年	2021
卷号	57 期号:6 页码:935-949
英文摘要	Increasing the quality of seismic data is the basis for subsequent seismic data processing and interpretation. In the acquisition of seismic records, the introduction of noise is the main factor that affects the quality of seismic data. Especially in the desert areas, the complex desert noise has the characteristics of high energy, nonstationary, nonlinear, low frequency, non-Gaussian, which brings great difficulties to the conventional denoising methods. Due to the powerful learning ability, we have been devoted to use convolutional neural networks (CNN) to suppress the desert noise of seismic data. However, as the depth increases, network might be faced with the problem that the influence of shallow layers on deep layers is reduced, which is not conducive to the suppression of complex desert noise. This paper raises a new network named a feature enhancement denoising network (FEDnet). First, the proposed model increase the width of the network and fuse the feature information of several different layers to fully utilize the influence of the whole network. It is beneficial to capture more desert noise features concealed in the complicated background. Second, we integrate the hybrid dilated convolution design into our model to improve the receptive field, which plays an important role for getting more context information in the denoising task. Finally, we also adopt residual learning to facilitate the network training. The experimental results demonstrate that the proposed network can obtain superior performance for the denoising task of seismic data compared with existing denoisers.
英文关键词	feature enhancement seismic data desert noise suppression hybrid dilated convolution
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000727331800012
WOS关键词	CNN
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/2XILL650/373716
作者单位	[Li, Juan; An, Ran; Li, Yue; Zhao, Yuxing] Jilin Univ, Coll Commun Engn, Changchun, Peoples R China
推荐引用方式 GB/T 7714	Li, Juan,An, Ran,Li, Yue,et al. Desert Noise Suppression for Seismic Data Based on Feature Enhancement Denoising Network[J],2021,57(6):935-949.
APA	Li, Juan,An, Ran,Li, Yue,&Zhao, Yuxing.(2021).Desert Noise Suppression for Seismic Data Based on Feature Enhancement Denoising Network.IZVESTIYA-PHYSICS OF THE SOLID EARTH,57(6),935-949.
MLA	Li, Juan,et al."Desert Noise Suppression for Seismic Data Based on Feature Enhancement Denoising Network".IZVESTIYA-PHYSICS OF THE SOLID EARTH 57.6(2021):935-949.