干旱区生态环境知识资源中心(Arid): Low-Frequency Desert Noise Intelligent Suppression in Seismic Data Based on Multiscale Geometric Analysis Convolutional Neural Network

Arid

DOI	10.1109/TGRS.2019.2938836
	Low-Frequency Desert Noise Intelligent Suppression in Seismic Data Based on Multiscale Geometric Analysis Convolutional Neural Network
	Zhao, Yuxing; Li, Yue; Yang, Baojun
通讯作者	Li, Yue
来源期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
EISSN	1558-0644
出版年	2020
卷号	58 期号:1 页码:650-665
英文摘要	Existing denoising algorithms often need to meet some premise assumptions and applicable conditions, such as the signal-to-noise ratio (SNR) cannot be too low, and the noise needs to obey a specific distribution (such as Gaussian distribution) and to satisfy some properties (such as stationarity). For the desert noise that shares the same frequency band with the effective signal and has complex characteristics (nonlinear, nonstationary, and non-Gaussian), it is difficult to find a universally applicable method. In response to this problem, a multiscale geometric analysis (MGA) convolutional neural network (CNN) is proposed in this article. One of the most important features of the CNN is that it can extract data-rich intrinsic information from the training set without relying on a priori assumption. By introducing the CNN into the MGA, a new kind of denoising method can be created, which can achieve good results even under a low SNR. This article takes the nonsubsampled contourlet transform as an example to create a denoising network named NC-CNN for high-efficiency and intelligent denoising of desert seismic data. The processing results of synthetic seismic records and field seismic records prove that NC-CNN can effectively suppress the low-frequency noise (random noise and surface wave), and the effective signal almost has no energy loss. In addition, the reconstruction ability of the missing signals is also an advantage of this method.
英文关键词	Noise reduction Feature extraction Signal to noise ratio Signal resolution Training Transforms Surface waves Convolutional neural network (CNN) low-frequency noise suppression multiscale geometric analysis (MGA) 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:000507307800048
WOS关键词	CONTOURLET TRANSFORM ; CNN ; CLASSIFICATION ; ATTENUATION ; PICKING ; KEY
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
EI主题词	2020-01-01
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/312063
作者单位	Jilin Univ, Dept Informat, Coll Commun Engn, Changchun 130012, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Yuxing,Li, Yue,Yang, Baojun. Low-Frequency Desert Noise Intelligent Suppression in Seismic Data Based on Multiscale Geometric Analysis Convolutional Neural Network[J],2020,58(1):650-665.
APA	Zhao, Yuxing,Li, Yue,&Yang, Baojun.(2020).Low-Frequency Desert Noise Intelligent Suppression in Seismic Data Based on Multiscale Geometric Analysis Convolutional Neural Network.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,58(1),650-665.
MLA	Zhao, Yuxing,et al."Low-Frequency Desert Noise Intelligent Suppression in Seismic Data Based on Multiscale Geometric Analysis Convolutional Neural Network".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58.1(2020):650-665.