干旱区生态环境知识资源中心(Arid): Low-Frequency Noise Suppression Method Based on Improved DnCNN in Desert Seismic Data

Arid

DOI	10.1109/LGRS.2018.2882058
	Low-Frequency Noise Suppression Method Based on Improved DnCNN in Desert Seismic Data
	Zhao, Yuxing; Li, Yue; Dong, Xintong; Yang, Baojun
通讯作者	Li, Yue
来源期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
EISSN	1558-0571
出版年	2019
卷号	16 期号:5 页码:811-815
英文摘要	High-quality seismic data are the basis for stratigraphic imaging and interpretation, but the existence of random noise can greatly affect the quality of seismic data. At present, most understanding and processing of random noise still stay at the level of Gaussian white noise. With the reduction of resource, the acquired seismic data have lower signal-to-noise ratio and more complex noise natures. In particular, the random noise in the desert area has the characteristics of low frequency, non-Gaussian, nonstationary, high energy, and serious aliasing between effective signal and random noise in the frequency domain, which has brought great difficulties to the recovery of seismic events by conventional denoising methods. To solve this problem, an improved feed-forward denoising convolution neural network (DnCNN) is proposed to suppress random noise in desert seismic data. DoCNN has the characteristics of automatic feature extraction and blind denoising. According to the characteristics of desert noise, we modify the original DnCNN from the aspects of patch size, convolution kernel size, network depth, and training set to make it suitable for low-frequency and non-Gaussian desert noise suppression. Both simulation and practical experiments prove that the improved DnCNN has obvious advantages in terms of desert noise and surface wave suppression as well as effective signal amplitude preservation. In addition, the improved DnCNN, in contrast to existing methods, has considerable potential to benefit from large data sets. Therefore, we believe that it can open a new direction in the area of seismic data processing.
英文关键词	Convolutional neural network (CNN) low-frequency noise noise suppression seismic exploration training set
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000466228400030
WOS关键词	DEEP ; CLASSIFICATION ; CNN
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/2XILL650/216238
作者单位	Jilin Univ, Coll Commun Engn, Dept Informat, Changchun 130012, Jilin, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Yuxing,Li, Yue,Dong, Xintong,et al. Low-Frequency Noise Suppression Method Based on Improved DnCNN in Desert Seismic Data[J],2019,16(5):811-815.
APA	Zhao, Yuxing,Li, Yue,Dong, Xintong,&Yang, Baojun.(2019).Low-Frequency Noise Suppression Method Based on Improved DnCNN in Desert Seismic Data.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,16(5),811-815.
MLA	Zhao, Yuxing,et al."Low-Frequency Noise Suppression Method Based on Improved DnCNN in Desert Seismic Data".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 16.5(2019):811-815.