摘要
闪电哨声波作为探索空间物理环境的重要媒介,淹没在海量的电磁卫星数据中.近年来随着计算机视觉和深度学习等人工智能技术的发展,从电磁卫星的存档数据中自动检测闪电哨声波的算法取得了一定的效果.本文对近年来闪电哨声波智能检测算法的文献进行了整理和总结.首先,阐述闪电哨声波在电磁卫星数据中呈现的时频特征和类型;然后,介绍了闪电哨声波智能检测算法的流程并从闪电哨声波的特征提取、分类和定位三个方面对主要的智能检测算法进行归纳、总结和评述;其次,简述了闪电哨声波智能检测模型的评价指标;接着,在张衡一号(ZH-1)卫星的磁场数据上对三种典型的闪电哨声波智能检测算法进行复现,并对三种算法的优缺点进行了较深入的分析;最后,对基于电磁卫星的闪电哨声波智能检测的研究领域进行总结和展望.
Lightning whistlers,found frequently in electromagnetic satellite observation,are the important media to study the plasmasphere of the earth space.With the increasing number of data observed from electromagnetic satellites,a considerable amount of time and human efforts are needed to detect lightning whistlers from these tremendous data.However,in recent years,with the development of artificial intelligence(AI)technologies such as computer vision and deep learning,algorithms for lightning whistlers automatic detection in the time-frequency profile of the electromagnetic satellites data have been conducted.This study analyzes and summarizes the existing automatic detection algorithms.Firstly,we describe the time-frequency characteristics and types of lightning whistlers recorded by electromagnetic satellites.Secondly,we introduce the automatic detection technique,which is composed of three steps involving extracting the feature of the object(the lightning whistler or noise),choosing the correct class label for the feature,and providing the boundaries of each object.Then,we analyze and summarize the existing research methods from three aspects including lightning whistler feature extraction,classification and positioning.Thirdly,we briefly describe the metrics to evaluate the mathematical model for automatic detection of lightning whistler.Fourthly,we apply three typical algorithms on the search coil magnetometer data of ZH-1 satellite to detect the lightning whistler automatically and conduct a deep analysis on the results.Lastly,we present existing problems and future possible research directions.
作者
袁静
王桥
张学民
杨德贺
王志国
张乐
申旭辉
泽仁志玛
YUAN Jing;WANG Qiao;ZHANG XueMin;YANG DeHe;WANG ZhiGuo;ZHANG Le;SHEN XuHui;Zeren Zima(Institute of Disaster Prevention,Langfang Hebei 062541,China;National Institute of Natural Hazards,Ministry of Emergency Management of China,Beijing 100085,China;Institute of Earthquake Forecasting,China Earthquake Administration,Beijing 100036,China;Tsinghua University,Beijing 100084,China;China Telecom Research Institute,Beijing 102209,China)
出处
《地球物理学报》
SCIE
EI
CAS
CSCD
北大核心
2021年第5期1471-1495,共25页
Chinese Journal of Geophysics
基金
中央直属高校基本科研业务经费(ZY20180122)
中国科技部国家重点研发计划(2018YFC1503502和2018YFC1503806和2018YFC1503501)
廊坊科技局科学研究与发展计划自筹经费项目(2020011025)
中央直属高校基本科研业务经费(2020011025)
(亚太地震二期项目:地震前兆特征的星地一体化观测研究)项目资助
ISSI-BJ(2019IT-33)项目资助
关键词
电磁卫星
闪电哨声波
智能检测算法
张衡一号卫星
Electromagnetic satellites
Lightning whistler
Automatic detection
ZH-1 satellite
