Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method

作     者：Mao Zhang GaoPeng Lu HaiLiang Huang ZhengWei Cheng YaZhou Chen Steven A.Cummer JiaYi Zheng JiuHou Lei Mao Zhang;GaoPeng Lu;HaiLiang Huang;ZhengWei Cheng;YaZhou Chen;Steven A.Cummer;JiaYi Zheng;JiuHou Lei

作者机构：School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefei 230026China Key Laboratory of Atmospheric OpticsAnhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Space WeatherNational Space Science CenterChinese Academy of SciencesBeijing 100190China National Key Laboratory on Electromagnetic Environment EffectsArmy Engineering UniversityShijiazhuang CampusShijiazhuang 050003China Electrical and Computer Engineering DepartmentDuke UniversityDurhamNC 27708USA 

出 版 物：《Earth and Planetary Physics》 (地球与行星物理（英文版）)

年 卷 期：2023年第7卷第3期

页      面：407-413页

核心收录：

学科分类：0709[理学-地质学] 070802[理学-空间物理学] 07[理学] 0708[理学-地球物理学] 0706[理学-大气科学] 0816[工学-测绘科学与技术] 0704[理学-天文学] 0825[工学-航空宇航科学与技术] 

基　　金：supported by the Chinese Academy of Sciences(CAS)Project of Stable Support for Youth Team in Basic Research Field(Grant No.YSRR-018) the National Key R&D Program of China(Grant No.2019YFC1510103) the National Natural Science Foundation of China(Grant Nos.41875006 and U1938115) the Chinese Meridian Project,and the International Partnership Program of CAS(Grant No.183311KYSB20200003) 

主　　题：machine learning method tweek atmospherics reflection height D-region ionosphere 

摘      要：Tweek atmospherics are extremely low frequency and very low frequency pulse signals with frequency dispersion characteristics that originate from lightning discharges and that propagate in the Earth–ionosphere waveguide over long *** this study,we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning *** differences(automatic−manual)in each ionosphere parameter between the automatic method and the manual method were−0.07±2.73 km,0.03±0.92 cm^(−3),and 91±1,068 km for the ionospheric reflection height(h),equivalent electron densities at reflection heights(Ne),and propagation distance(d),***,the automatic method is capable of recognizing higher harmonic tweek *** evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.