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A Deep Learning Method for Denoising Based on a Fast and Flexible Convolutional Neural Network

文献类型：期刊论文

作者Li, Wenda1; Liu, Hong1; Wang, Jian2
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2022
卷号60页码:13
ISSN号0196-2892
关键词Convolutional neural networks Noise reduction Neural networks Training Signal to noise ratio Convolution Image denoising Geophysical data geophysics computing
DOI10.1109/TGRS.2021.3073001
英文摘要Seismic data denoising has always been an indispensable step in the seismic exploration workflow. The quality of the results directly affects the results of subsequent inversion and migration imaging. In this article, we proposed a fast and flexible convolutional neural network (FFCNN) based on DnCNN. In contrast to the existing DnCNN and other artificial intelligence (AI)-based denoisers, FFCNN enjoys several desirable properties: 1) downsampling and upscaling operations, which can sensibly reduce runtimes and memory requirements while maintaining the denoising performance, and 2) we introduced noised level maps, which can make that a single convolutional neural network (CNN) model is expected to inherit the flexibility of handling noise models with different parameters, even spatially variant noises by noting M can be nonuniform. Another benefit of increasing the noise-level map is that we can preserve more useful seismic data information by controlling the tradeoff of noise removal effect and seismic data detail preservation. For real seismic data denoised work, the main work and advantages of this article are concentrated on the following two aspects: 1) we introduced a data augmentation strategy to overcome the lack of well-labeled samples and 2) transfer learning has been introduced to the training processing, which used the well-trained synthetic seismic data denoising network as a pretrained model. In this way, we can greatly accelerate and optimize the learning efficiency of the training network. Ultimately, we can greatly improve the computational efficiency and denoising performance based on this intelligent denoised network FFCNN. Finally, numerical experiments prove the effectiveness of our method in synthetic and real seismic data.
WOS关键词T-X ; INTERPOLATION ; CNN
资助项目National Key Scientific Instrument and Equipment Development Project[2018YFF01013503] ; National Natural Science Foundation of China[41630319]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000732785400001
资助机构National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Key Scientific Instrument and Equipment Development Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/103932]  
专题地质与地球物理研究所_中国科学院油气资源研究重点实验室
通讯作者Liu, Hong
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Acoust, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Li, Wenda,Liu, Hong,Wang, Jian. A Deep Learning Method for Denoising Based on a Fast and Flexible Convolutional Neural Network[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:13.
APA Li, Wenda,Liu, Hong,&Wang, Jian.(2022).A Deep Learning Method for Denoising Based on a Fast and Flexible Convolutional Neural Network.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,13.
MLA Li, Wenda,et al."A Deep Learning Method for Denoising Based on a Fast and Flexible Convolutional Neural Network".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):13.

入库方式： OAI收割

来源：地质与地球物理研究所

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