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 

机构地区：[1]School of Earth and Space Sciences,University of Science and Technology of China,Hefei 230026,China [2]Key Laboratory of Atmospheric Optics,Anhui Institute of Optics and Fine Mechanics,Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China [3]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science and Technology,Nanjing 210044,China [4]State Key Laboratory of Space Weather,National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China [5]National Key Laboratory on Electromagnetic Environment Effects,Army Engineering University,Shijiazhuang Campus,Shijiazhuang 050003,China [6]Electrical and Computer Engineering Department,Duke University,Durham,NC 27708,USA

出　　处：《Earth and Planetary Physics》2023年第3期407-413,共7页地球与行星物理（英文版）

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).

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 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.

关 键 词：machine learning method tweek atmospherics reflection height D-region ionosphere 

分 类 号：P352.7[天文地球—空间物理学]