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  2. 地质与地球物理研究所
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  4. 中国科学院矿产资源研究重点实验室
Deep-learning for ionogram automatic scaling

文献类型：期刊论文

作者Xiao, Zhuowei2,3; Wang, Jian2,5; Li, Juan3,4,5; Zhao, Biqiang1,3,4,5; Hu, Lianhuan1,4,5; Liu, Libo1,3,4,5
刊名ADVANCES IN SPACE RESEARCH
出版日期2020-08-15
卷号66期号:4页码:942-950
关键词Ionogram scaling Deep-learning Ionosonde Ionosphere
ISSN号0273-1177
DOI10.1016/j.asr.2020.05.009
英文摘要Scientists can study the global ionospheric weather by manually or automatically scaling ionograms recorded by global ionosondes to obtain characteristic values of D, E, F regions in the ionosphere. Therefore, fast and accurate ionogram scaling is crucial to real-time space weather monitoring, which is closely related to the performance of space-borne and ground-based technological systems as well as life on earth. The significant increase in data collections during recent years makes an impossible task for human experts to manually scale large amounts of ionograms in time. While the scaling accuracy of traditional automatic methods is less than that by human experts, making them insufficient for scientific tasks. Deep-learning is currently attracting immense research interest in many scaling tasks due to its powerful ability to deal with huge data collections. In this study, we present a deep-learning method for ionogram automatic scaling (DIAS) that can rapidly scale ionograms precisely from the ionosonde data. We trained and tested on data recorded by Wuhan ionosonde located at 114.4 degrees E and 30.5 degrees N. Our results show that the proposed deep-learning method improved the precision and recall rate by 8%, 17%, respectively, compared to using Automatic Real-Time Ionogram Scaling with True-height (ARTIST), which is the most-widely-used automatically scaling routine, in scaling E, F1 and F2 layers. The scaling accuracy of the ionograms provided by our deep-learning model is close to that by human experts, which suggests that the ionograms provided by our deep-learning method can be applied directly to global ionospheric weather nowcasting. Therefore, this study may contribute greatly to improve our knowledge of the ionospheric space. (C) 2020 COSPAR. Published by Elsevier Ltd. All rights reserved.
WOS关键词VERTICAL INCIDENCE IONOGRAMS ; CRITICAL FREQUENCY ; AUTOSCALA ; LAYER ; PARAMETERS
资助项目National Natural Science Foundation of China[41674148] ; Strategic Priority Program of Chinese Academy of Sciences[XDB41000000] ; Youth Innovation Promotion Association of CAS[2014058]
WOS研究方向Engineering ; Astronomy & Astrophysics ; Geology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000546696600005
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Strategic Priority Program of Chinese Academy of Sciences ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS ; Youth Innovation Promotion Association of CAS
源URL[http://ir.iggcas.ac.cn/handle/132A11/97137]  
专题地质与地球物理研究所_中国科学院矿产资源研究重点实验室
地质与地球物理研究所_中国科学院地球与行星物理重点实验室
通讯作者Wang, Jian
作者单位1.Chinese Acad Sci, Inst Geol & Geophys, Beijing Natl Observ Space Environm, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Mineral Resources, Beijing 100029, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing 100029, Peoples R China
5.Chinese Acad Sci, Innovat Acad Earth Sci, Beijing 100029, Peoples R China
推荐引用方式
GB/T 7714		 Xiao, Zhuowei,Wang, Jian,Li, Juan,et al. Deep-learning for ionogram automatic scaling[J]. ADVANCES IN SPACE RESEARCH,2020,66(4):942-950.
APA Xiao, Zhuowei,Wang, Jian,Li, Juan,Zhao, Biqiang,Hu, Lianhuan,&Liu, Libo.(2020).Deep-learning for ionogram automatic scaling.ADVANCES IN SPACE RESEARCH,66(4),942-950.
MLA Xiao, Zhuowei,et al."Deep-learning for ionogram automatic scaling".ADVANCES IN SPACE RESEARCH 66.4(2020):942-950.

入库方式： OAI收割

来源：地质与地球物理研究所

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