干旱区生态环境知识资源中心(Arid): A Branch Construction-Based CNN Denoiser for Desert Seismic Data

Arid

DOI	10.1109/LGRS.2020.2981965
	A Branch Construction-Based CNN Denoiser for Desert Seismic Data
	Lin, Hongbo; Wang, Shifu; Li, Yue
通讯作者	Lin, HB (corresponding author), Jilin Univ, Dept Informat, Coll Commun Engn, Changchun 130012, Peoples R China.
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
EISSN	1558-0571
出版年	2021
卷号	18 期号:4 页码:736-740
英文摘要	Seismic random noise reduction is an indispensable step in seismic data processing. Due to complex geological condition and acquisition environment, random noise in the desert seismic data has spatiotemporally variant noise levels and weak similarity to the signals, which severely obscures the seismic signals and increases the difficulty to extract the reflected seismic signals. This letter focuses on suppressing the desert random noise based on a convolutional neural network (CNN) and proposes a branch construction-based denoising network (BCDNet). The BCDNet contains a denoising main network and a branched network added to the downsampled layer of the main network. With the branched network, the global context feature of the seismic data is obtained early in the network to guide the denoising task of the subsequent main network, which allows a flexible denoising for the desert random noise. Moreover, the downsampled layer is able to enlarge the receptive field of the network without increasing the network depth, thus leading to the better retention of the structural features in the seismic records. The extensive experiments and the field desert data application confirm that our BCDNet not only has a significant denoising capacity to desert seismic data but also is competitive in training time and memory cost.
英文关键词	Noise reduction Training Convolution Radio frequency Data models Noise measurement Training data Convolutional neural network (CNN) deep learning denoise desert seismic random noise seismic exploration
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000633394400035
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/2XILL650/350528
作者单位	[Lin, Hongbo; Wang, Shifu; Li, Yue] Jilin Univ, Dept Informat, Coll Commun Engn, Changchun 130012, Peoples R China
推荐引用方式 GB/T 7714	Lin, Hongbo,Wang, Shifu,Li, Yue. A Branch Construction-Based CNN Denoiser for Desert Seismic Data[J],2021,18(4):736-740.
APA	Lin, Hongbo,Wang, Shifu,&Li, Yue.(2021).A Branch Construction-Based CNN Denoiser for Desert Seismic Data.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,18(4),736-740.
MLA	Lin, Hongbo,et al."A Branch Construction-Based CNN Denoiser for Desert Seismic Data".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 18.4(2021):736-740.