干旱区生态环境知识资源中心(Arid): Desert seismic noise suppression based on multimodal residual convolutional neural network

Arid

DOI	10.1007/s11600-020-00405-4
	Desert seismic noise suppression based on multimodal residual convolutional neural network
	Wang, Shengnan; Li, Yue; Zhao, Yuxing
通讯作者	Li, Yue
来源期刊	ACTA GEOPHYSICA
ISSN	1895-6572
EISSN	1895-7455
出版年	2020
卷号	68 期号:2 页码:389-401
英文摘要	Seismic exploration is an important means of oil and gas detection, but affected by complex surface and near-surface conditions, and the seismic records are polluted by noise seriously. Particularly in the desert areas, due to the influence of wind and human activities, the complex desert noise with low-frequency, nonstationary and non-Gaussian characteristics is produced. It is difficult to extract effective signals from strong noise using existing denoising methods. To address this issue, the paper proposes a new denoising method, called multimodal residual convolutional neural network (MRCNN). MRCNN combines convolutional neural network (CNN) with variational modal decomposition (VMD) and adopts residual learning method to suppress desert noise. Since CNN-based denoisers can extract data features based on massive training set, the impact of noise types and intensity on the denoised results can be ignored. In addition, VMD algorithm can sparsely decompose signal, which will facilitate the feature extraction of CNN. Therefore, using VMD algorithm to optimize the input data will conducive to the performance of the network denoising. Moreover, MRCNN adopts reversible downsampling operator to improve running speed, achieving a good trade-off between denoising results and efficiency. Extensive experiments on synthetic and real noisy records are conducted to evaluate MRCNN in comparison with existing denoisers. The extensive experiments demonstrate that the MRCNN can exhibit good effectiveness in seismic denoising tasks.
英文关键词	Residual learning Desert seismic record Noise suppression Variational mode decomposition
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000515890100002
WOS关键词	EXPLORATION
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/2XILL650/313882
作者单位	Jilin Univ, Coll Commun Engn, Dept Informat, Changchun 130012, Peoples R China
推荐引用方式 GB/T 7714	Wang, Shengnan,Li, Yue,Zhao, Yuxing. Desert seismic noise suppression based on multimodal residual convolutional neural network[J],2020,68(2):389-401.
APA	Wang, Shengnan,Li, Yue,&Zhao, Yuxing.(2020).Desert seismic noise suppression based on multimodal residual convolutional neural network.ACTA GEOPHYSICA,68(2),389-401.
MLA	Wang, Shengnan,et al."Desert seismic noise suppression based on multimodal residual convolutional neural network".ACTA GEOPHYSICA 68.2(2020):389-401.