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Chinese Academy of Sciences Institutional Repositories Grid
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  2. 地质与地球物理研究所
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  4. 中国科学院油气资源研究重点实验室
Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification

文献类型：期刊论文

作者Geng, Zhi2,3; Wang, Yanfei1,2,3
刊名NATURE COMMUNICATIONS
出版日期2020-07-03
卷号11期号:1页码:11
ISSN号2041-1723
DOI10.1038/s41467-020-17123-6
英文摘要Geoscientists mainly identify subsurface geologic features using exploration-derived seismic data. Classification or segmentation of 2D/3D seismic images commonly relies on conventional deep learning methods for image recognition. However, complex reflections of seismic waves tend to form high-dimensional and multi-scale signals, making traditional convolutional neural networks (CNNs) computationally costly. Here we propose a highly efficient and resource-saving CNN architecture (SeismicPatchNet) with topological modules and multiscale-feature fusion units for classifying seismic data, which was discovered by an automated data-driven search strategy. The storage volume of the architecture parameters (0.73 M) is only similar to 2.7 MB, similar to 0.5% of the well-known VGG-16 architecture. SeismicPatchNet predicts nearly 18 times faster than ResNet-50 and shows an overwhelming advantage in identifying Bottom Simulating Reflection (BSR), an indicator of marine gas-hydrate resources. Saliency mapping demonstrated that our architecture captured key features well. These results suggest the prospect of end-to-end interpretation of multiple seismic datasets at extremely low computational cost.
WOS关键词GAS HYDRATE
资助项目Key Research Program of the Institute of Geology & Geophysics, CAS[IGGCAS-201903] ; Original Innovation Program of CAS[ZDBS-LY-DQC003] ; National Key R & D Program of the Ministry of Science and Technology of China[2018YFC0603500] ; National Key R & D Program of the Ministry of Science and Technology of China[2018YFC1504203]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000546623500011
出版者NATURE PUBLISHING GROUP
资助机构Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Key Research Program of the Institute of Geology & Geophysics, CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; Original Innovation Program of CAS ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China ; National Key R & D Program of the Ministry of Science and Technology of China
源URL[http://ir.iggcas.ac.cn/handle/132A11/97131]  
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Geng, Zhi; Wang, Yanfei
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing 100029, Peoples R China
3.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Petr Resources Res, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Geng, Zhi,Wang, Yanfei. Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification[J]. NATURE COMMUNICATIONS,2020,11(1):11.
APA Geng, Zhi,&Wang, Yanfei.(2020).Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification.NATURE COMMUNICATIONS,11(1),11.
MLA Geng, Zhi,et al."Automated design of a convolutional neural network with multi-scale filters for cost-efficient seismic data classification".NATURE COMMUNICATIONS 11.1(2020):11.

入库方式： OAI收割

来源：地质与地球物理研究所

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