[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128451-en":3,"doc-seo-128451-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128451,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method","Tweek atmospherics are extremely low frequency and very low frequency pulse signals with dispersion characteristics originating from lightning discharges and propagating in the Earth–ionosphere waveguide over long distances. This study develops an automatic recognition and lower-ionosphere diagnostics method using machine learning. Differences between automatic and manual results for ionospheric reflection height (h), equivalent electron density at reflection heights (Ne), and propagation distance (d) are quantified, and the approach also identifies higher harmonic tweek sferics. Evaluation indicates strong capability for studying long-term variations in the lower ionosphere.","RESEARCH ARTICLE  \nSPACE PHYSICS: IONOSPHERIC PHYSICS  \nEarth and Planetary Physics  \n7: 407–413, 2023 doi: 10.26464/epp2023039  \nAutomatic recognition oftweek atmospherics and plasma diagnostics in the lower ionosphere with the  \nmachine learning method  \nMao Zhang1, GaoPeng Lu1,2,3*, HaiLiang Huang1, ZhengWei Cheng4, YaZhou Chen5, Steven A. Cummer6, JiaYi Zheng1, and JiuHou Lei1  \n1School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China;  \n2Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China;  \n3Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China;  \n4State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China;  \n5National Key Laboratory on Electromagnetic Environment Effects, Army Engineering University, Shijiazhuang Campus, Shijiazhuang 050003, China;  \n6Electrical and Computer Engineering Department, Duke University, Durham, NC 27708, USA  \nKey Points:  \n● An automatic method was developed to recognize tweek atmospherics and diagnose the lower ionosphere with the machine learning method.  \n● The differences in the ionospheric reflection height (h) and equivalent electron densities at reflection heights (Ne) between the automatic and manual methods were −0.07 ± 2.73 km and 0.03 ± 0.92 cm−3, respectively.  \n● The automatic method was capable of recognizing higher harmonic tweek sferics as well.  \nCitation: Zhang, M., Lu, G. P., Huang, H. L., Cheng, Z. W., Chen, Y. Z., Cummer, S. A., Zheng, J. Y., and Lei, J. H. (2023) . Automatic recognition oftweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method. Earth Planet. Phys., 7(3), 407–413. [http://doi.org/10.26464/epp2023039](http://doi.org/10.26464/epp2023039)  \nAbstract: 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 distances. In this study, we developed an automatic method to recognize tweek atmospherics and diagnose the lower ionosphere based on the machine learning method. The 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), respectively. Moreover, the automatic method is capable of recognizing higher harmonic tweek sferics. The evaluation results of the model suggest that the automatic method is a powerful tool for investigating the long-term variations in the lower ionosphere.  \nKeywords: machine learning method; tweek atmospherics; reflection height; D-region ionosphere  \n1. Introduction  \nLightning strokes spawned by thunderstorms radiate electromagnetic waves from a few hertz to tens of megahertz. The vast majority of this energy is in the extremely low frequency (ELF, 3− 3,000 Hz) and very low frequency (VLF, 3−30 kHz) bands. These radio emissions are reflected atthe bottom of the lower ionosphere and propagate in the Earth–ionosphere waveguide with a low  \nFirst author: M. Zhang, [zhangmao@mail.ustc.edu.cn](zhangmao@mail.ustc.edu.cn)[ ](zhangmao@mail.ustc.edu.cn)Correspondence to: G. P. Lu, [gaopenglu@gmail.com](gaopenglu@gmail.com)[ ](gaopenglu@gmail.com)[Received 19 DEC 2022](Received 19 DEC 2022); Accepted 07 APR 2023. Accepted article online 24 APR 2023.  \n©2023 by Earth and Planetary Physics.  \nattenuation rate (2−3 dB/1,000 km), which allows them to travel over considerable distances. This waveguide mode propagation causes an appreciable dispersion near t","cbCaiqU8CM9NS3cu","https://ap.wps.com/l/cbCaiqU8CM9NS3cu","pdf",428032,2,1,7,"English","en",105,"# Key Points\n# Introduction","[{\"question\":\"What are tweek atmospherics in this research?\",\"answer\":\"Tweek atmospherics are ELF/VLF pulse signals with distinctive frequency dispersion, originating from lightning discharges and propagating in the Earth–ionosphere waveguide over long distances.\"},{\"question\":\"How does the proposed method work for diagnosing the lower ionosphere?\",\"answer\":\"The study introduces an automatic recognition and diagnosis approach based on machine learning, trained on a dataset and then used to derive ionospheric parameters.\"},{\"question\":\"How do the automatic results compare with manual measurements?\",\"answer\":\"The differences between automatic and manual methods are reported for ionospheric reflection height (h), equivalent electron density (Ne), and propagation distance (d), showing small discrepancies with quantified uncertainties.\"}]","Automatic recognition of tweek atmospherics and plasma diagnostics in the lower ionosphere with the machine learning method | 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are tweek atmospherics in this research?","Question",{"text":76,"@type":77},"Tweek atmospherics are ELF/VLF pulse signals with distinctive frequency dispersion, originating from lightning discharges and propagating in the Earth–ionosphere waveguide over long distances.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method work for diagnosing the lower ionosphere?",{"text":81,"@type":77},"The study introduces an automatic recognition and diagnosis approach based on machine learning, trained on a dataset and then used to derive ionospheric parameters.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the automatic results compare with manual measurements?",{"text":85,"@type":77},"The differences between automatic and manual methods are reported for ionospheric reflection height (h), equivalent electron density (Ne), and propagation distance (d), showing small discrepancies with quantified 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