干旱区生态环境知识资源中心(Arid): DnResNeXt Network for Desert Seismic Data Denoising

Arid

DOI	10.1109/LGRS.2020.3044036
	DnResNeXt Network for Desert Seismic Data Denoising
	Yao, Haiyang; Ma, Haitao; Li, Yue; Feng, Qiankun
通讯作者	Li, Y (corresponding author)，Jilin Univ, Coll Commun Engn, Dept Informat, Changchun 130012, Peoples R China.
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
EISSN	1558-0571
出版年	2022
卷号	19
英文摘要	In recent years, the denoising of low-frequency desert noise has been the significant and difficult point in processing seismic data. Traditional random noise suppression methods could not get a good result in processing seismic data in desert areas. Moreover, convolutional neural network (CNN) has made notable achievements in many fields recently. In order to denoise seismic data in desert areas and improve the signal-to-noise ratio (SNR), CNN is introduced to process seismic data. According to the characteristics of desert seismic data, we designed a new network suitable for desert seismic data training and denoising, which is named DnResNeXt. Then, to form a mapping from the noisy data to the pure desert noise, we build a mass of training sets to train the denoising network. Thus, the network can predict the noise, then by subtracting the predicted noise from the noisy data, the denoised data are obtained. Consequently, compared with the traditional methods in suppressing random noise, DnResNeXt network has obvious advantages in both simulation and actual experiments.
英文关键词	Noise reduction Signal to noise ratio Noise measurement Training Convolutional neural networks Convolution Deep learning Convolutional neural network (CNN) deep learning denoising desert seismic noise residual learning seismic exploration
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000736769300051
WOS关键词	RANDOM NOISE ; CLASSIFICATION
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/2XILL650/376773
作者单位	[Yao, Haiyang; Ma, Haitao; Li, Yue; Feng, Qiankun] Jilin Univ, Coll Commun Engn, Dept Informat, Changchun 130012, Peoples R China
推荐引用方式 GB/T 7714	Yao, Haiyang,Ma, Haitao,Li, Yue,et al. DnResNeXt Network for Desert Seismic Data Denoising[J],2022,19.
APA	Yao, Haiyang,Ma, Haitao,Li, Yue,&Feng, Qiankun.(2022).DnResNeXt Network for Desert Seismic Data Denoising.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19.
MLA	Yao, Haiyang,et al."DnResNeXt Network for Desert Seismic Data Denoising".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022).