中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 国家天文台
  3. 中国科学院国家天文台
EUV Wave Detection and Characterization Using Deep Learning

文献类型：期刊论文

作者Xu, Long2; Liu, Sixuan2,3; Yan, Yihua2; Zhang, Weiqiang1
刊名SOLAR PHYSICS
出版日期2020-03-19
卷号295期号:3页码:14
关键词Coronal Mass Ejection (CME) Extreme Ultraviolet (EUV) waves Solar burst Deep neural network
ISSN号0038-0938
DOI10.1007/s11207-020-01612-4
英文摘要Coronal Mass Ejections (CMEs) are the most violent solar bursts. They cause severe disturbances in the solar-terrestrial space and affect human activities in many aspects, especially causing damage to high-tech infrastructure. It usually takes few hours for a CME to arrive at the Earth after eruption. Therefore, many efforts have been devoted to CME arrival time prediction, so that we have enough time to take action before a CME arrives at the Earth. For predicting CME arrival time, it is vital to detect the CME origin, arrival and departure speed in a coronagraph. It has been widely accepted that Extreme Ultraviolet (EUV) waves are associated with CMEs, so EUV waves are the signatures of CMEs as CMEs originate and traverse the solar disk, specifically for front-side CMEs. In this paper, two deep neural networks are developed to first detect EUV waves and then outline their wavefronts, giving early signatures of CMEs. Usually, CMEs are recorded by coronagraphs as they transit the corona, so our proposed method can obtain a certain time ahead compared with conventional CME forecasting. In addition, the parameters for describing EUV waves can be more easily deduced, benefiting the subsequent statistical analysis of CMEs. The experimental results demonstrate the effectiveness of the proposed model for detecting EUV waves and generating their outlines.
WOS关键词EIT WAVES ; CORONAL WAVE
资助项目National Natural Science Foundation of China (NSFC)[61572461] ; National Natural Science Foundation of China (NSFC)[61811530282] ; National Natural Science Foundation of China (NSFC)[61872429] ; National Natural Science Foundation of China (NSFC)[11790301] ; National Natural Science Foundation of China (NSFC)[11790305] ; Specialized Research Fund for State Key Laboratories[2018-026F-04]
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:000522137100002
出版者SPRINGER
资助机构National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Specialized Research Fund for State Key Laboratories ; Specialized Research Fund for State Key Laboratories ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Specialized Research Fund for State Key Laboratories ; Specialized Research Fund for State Key Laboratories ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Specialized Research Fund for State Key Laboratories ; Specialized Research Fund for State Key Laboratories ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Specialized Research Fund for State Key Laboratories ; Specialized Research Fund for State Key Laboratories
源URL[http://ir.bao.ac.cn/handle/114a11/55324]  
专题中国科学院国家天文台
通讯作者Xu, Long
作者单位1.Shenzhen Univ, Coll Math & Stat, Shenzhen 518060, Peoples R China
2.Chinese Acad Sci, Natl Astron Observ, Key Lab Solar Act, Beijing 100101, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Long,Liu, Sixuan,Yan, Yihua,et al. EUV Wave Detection and Characterization Using Deep Learning[J]. SOLAR PHYSICS,2020,295(3):14.
APA Xu, Long,Liu, Sixuan,Yan, Yihua,&Zhang, Weiqiang.(2020).EUV Wave Detection and Characterization Using Deep Learning.SOLAR PHYSICS,295(3),14.
MLA Xu, Long,et al."EUV Wave Detection and Characterization Using Deep Learning".SOLAR PHYSICS 295.3(2020):14.

入库方式： OAI收割

来源：国家天文台

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。