干旱区生态环境知识资源中心(Arid): DESERT LOW FREQUENCY NOISE SUPPRESSION BASED ON MULTI-LEVEL WAVELET CONVOLUTION NEURAL NETWORK

Arid

	DESERT LOW FREQUENCY NOISE SUPPRESSION BASED ON MULTI-LEVEL WAVELET CONVOLUTION NEURAL NETWORK
	Ju, Hanqing; Li, Yue; Wang, Hongzhou; Yang, Baojun
通讯作者	Li, Y (corresponding author), Jilin Univ, Coll Commun Engn, Signal & Informat Proc, Changchun 130012, Peoples R China.
来源期刊	JOURNAL OF SEISMIC EXPLORATION
ISSN	0963-0651
出版年	2020
卷号	29 期号:6 页码:575-586
英文摘要	Due to the effect of various environment factors, the random noise in desert seismic exploration has complex characteristics, including low frequency, non-Gaussian and frequency band aliasing of signal and noise. Therefore, it is difficult for the denoising processing. Aiming at this problem, a Multi-level Wavelet Convolution Neural Network (MWCNN) is proposed to suppress the desert noise. MWCNN is a combination of two-dimensional discrete wavelet transformation and convolution neural network. Specifically, Discrete Wavelet Transformation (DWT) and inverse wavelet transformation (IWT) are used to replace the pooling layer and up-convolution of U-net respectively. So that the trade-off between receptive field and computational efficiency can be achieved. Consequently, the expansion of the receptive field can obtain more overall information of the events. In this paper, by adjusting the training set and structure of MWCNN. it is applied to suppress the random noise in desert seismic exploration. Furthermore. compared with other neural networks. MWCNN achieves better better denoising effect and better events' continuity by enlarging the receptive field in desert seismic records. And experiments on simulated synthetic records and actual seismic records respectively show our trained MWCNN model achieve a satisfactory denoising performance for the random noise in desert seismic exploration.
英文关键词	random noise denoising convolutional neural network
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000600433200004
WOS类目	Geochemistry & Geophysics
WOS研究方向	Geochemistry & Geophysics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/349047
作者单位	[Ju, Hanqing; Li, Yue; Wang, Hongzhou] Jilin Univ, Coll Commun Engn, Signal & Informat Proc, Changchun 130012, Peoples R China; [Yang, Baojun] Jilin Univ, Coll Geoexplorat Sci & Technol, Geodetect & Informat Technol, Changchun 130012, Peoples R China
推荐引用方式 GB/T 7714	Ju, Hanqing,Li, Yue,Wang, Hongzhou,et al. DESERT LOW FREQUENCY NOISE SUPPRESSION BASED ON MULTI-LEVEL WAVELET CONVOLUTION NEURAL NETWORK[J],2020,29(6):575-586.
APA	Ju, Hanqing,Li, Yue,Wang, Hongzhou,&Yang, Baojun.(2020).DESERT LOW FREQUENCY NOISE SUPPRESSION BASED ON MULTI-LEVEL WAVELET CONVOLUTION NEURAL NETWORK.JOURNAL OF SEISMIC EXPLORATION,29(6),575-586.
MLA	Ju, Hanqing,et al."DESERT LOW FREQUENCY NOISE SUPPRESSION BASED ON MULTI-LEVEL WAVELET CONVOLUTION NEURAL NETWORK".JOURNAL OF SEISMIC EXPLORATION 29.6(2020):575-586.