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

Arid

DOI	10.1109/TGRS.2020.3030692
	Generative Adversarial Network for Desert Seismic Data Denoising
	Wang, Hongzhou; Li, Yue; Dong, Xintong
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 TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
EISSN	1558-0644
出版年	2021
卷号	59 期号:8 页码:7062-7075
英文摘要	Seismic exploration is a kind of exploration method for oil and gas resources. However, the disturbance of numerous random noise will decrease the quality and signal-to-noise ratio (SNR) of real seismic records, which brings difficulties to the following works of processing and interpretation. The seismic records of desert region pose a particular problem because of the strong energy noise and the spectrum overlapping between effective signals and random noise. Recent research works demonstrate that a convolutional neural network (CNN) can increase the SNR of seismic records. The optimization of denoising methods based on CNN is principally driven by the loss functions that largely focus on minimizing the mean-squared reconstruction error between denoising records and theoretical pure records. The denoising results estimated by the CNN model are often lacking the perfection of the signal structure. Therefore, when processing seismic records with low SNR, the denoising results often have a lack of effective signal in some traces, which leads to the poor continuity of events. In order to solve this problem, we adopt the strategy of generative adversarial network (GAN) to construct a GAN for denoising. It is divided into two parts: the generator (the denoising network based on CNN) is used to remove noise, while the discriminator is used to guide the generator to restore the structure information of effective signals. The generator and discriminator enhance the performance of each other through adversarial training, and the generator after adversarial training can greatly recover events and suppress random noise in synthetic and real desert seismic data.
英文关键词	Noise reduction Generators Generative adversarial networks Gallium nitride Convolution Signal to noise ratio Training Adversarial training convolutional neural network (CNN) desert seismic data generative adversarial network (GAN) low signal-to-noise ratios (SNRs)
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000675402300065
WOS关键词	SHEARLET TRANSFORM ; NOISE SUPPRESSION ; MICROSEISMIC DATA ; ATTENUATION ; REDUCTION
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/2XILL650/363567
作者单位	[Wang, Hongzhou; Li, Yue; Dong, Xintong] Jilin Univ, Coll Commun Engn, Dept Informat, Changchun 130012, Peoples R China
推荐引用方式 GB/T 7714	Wang, Hongzhou,Li, Yue,Dong, Xintong. Generative Adversarial Network for Desert Seismic Data Denoising[J],2021,59(8):7062-7075.
APA	Wang, Hongzhou,Li, Yue,&Dong, Xintong.(2021).Generative Adversarial Network for Desert Seismic Data Denoising.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(8),7062-7075.
MLA	Wang, Hongzhou,et al."Generative Adversarial Network for Desert Seismic Data Denoising".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.8(2021):7062-7075.