Estimation of dusk time F-region electron density vertical profiles using LSTM neural networks: A preliminary investigation

Lucas Alves Salles , Paulo Renato Pereira Silva , Guilherme Schwinn Fagundes , Jonas Sousasantos , Alison Moraes
{"title":"Estimation of dusk time F-region electron density vertical profiles using LSTM neural networks: A preliminary investigation","authors":"Lucas Alves Salles ,&nbsp;Paulo Renato Pereira Silva ,&nbsp;Guilherme Schwinn Fagundes ,&nbsp;Jonas Sousasantos ,&nbsp;Alison Moraes","doi":"10.1016/j.aiig.2023.12.001","DOIUrl":null,"url":null,"abstract":"<div><p>The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles (EPBs), that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System (GNSS). Accurate estimation of ionospheric delays through vertical electron density profiles is vital for mitigating GNSS errors and enhancing location-based services. The objective of this study is to propose a neural network, trained with radio occultation data from the COSMIC-1 mission, that generates average ionospheric electron density profiles during dusk, focusing on the pre-reversal enhancement of the zonal electric field. Results show that the estimated profiles exhibit a clear seasonal pattern, and reproduce adequately the climatological behavior of the ionosphere, thus presenting strong appeal on ionospheric error attenuation.</p></div>","PeriodicalId":100124,"journal":{"name":"Artificial Intelligence in Geosciences","volume":"4 ","pages":"Pages 209-219"},"PeriodicalIF":0.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666544123000333/pdfft?md5=2ca98126aaa23ba289e29231c504922b&pid=1-s2.0-S2666544123000333-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial Intelligence in Geosciences","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666544123000333","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles (EPBs), that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System (GNSS). Accurate estimation of ionospheric delays through vertical electron density profiles is vital for mitigating GNSS errors and enhancing location-based services. The objective of this study is to propose a neural network, trained with radio occultation data from the COSMIC-1 mission, that generates average ionospheric electron density profiles during dusk, focusing on the pre-reversal enhancement of the zonal electric field. Results show that the estimated profiles exhibit a clear seasonal pattern, and reproduce adequately the climatological behavior of the ionosphere, thus presenting strong appeal on ionospheric error attenuation.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用 LSTM 神经网络估算黄昏时间 F 区域电子密度垂直剖面:初步研究
电离层密度的垂直剖面在低纬度赤道等离子体气泡(EPB)的发展中起着重要作用,反过来又会导致电离层闪烁,严重降低全球导航卫星系统(GNSS)关键用户的精度和可用性。通过垂直电子密度剖面准确估算电离层延迟对减少全球导航卫星系统误差和增强定位服务至关重要。本研究的目的是提出一种神经网络,利用 COSMIC-1 飞行任务的无线电掩星数据进行训练,生成黄昏期间电离层平均电子密度剖面图,重点是逆转前增强的地带电场。结果表明,估计的剖面图呈现出明显的季节性模式,并充分再现了电离层的气候学行为,因此对电离层误差衰减具有很强的吸引力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
4.20
自引率
0.00%
发文量
0
期刊最新文献
Convolutional sparse coding network for sparse seismic time-frequency representation Research on the prediction method for fluvial-phase sandbody connectivity based on big data analysis--a case study of Bohai a oilfield Pore size classification and prediction based on distribution of reservoir fluid volumes utilizing well logs and deep learning algorithm in a complex lithology Benchmarking data handling strategies for landslide susceptibility modeling using random forest workflows A 3D convolutional neural network model with multiple outputs for simultaneously estimating the reactive transport parameters of sandstone from its CT images
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1