• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

2014-2019 年日本电离层总电子含量的时空变化研究及 2016 年熊本地震

Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014-2019 and the 2016 Kumamoto Earthquake.

机构信息

School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China.

Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China.

出版信息

Sensors (Basel). 2021 Mar 19;21(6):2156. doi: 10.3390/s21062156.

DOI:10.3390/s21062156
PMID:33808646
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8003414/
Abstract

There are a large number of excellent research cases in Global Navigation Satellite System (GNSS) positioning and disaster prediction in Japan region, where the simulation and prediction of total electron content (TEC) is a powerful research method. In this study, we used the data of the GNSS Earth Observation Network (GEONET) established by the Geographical Survey Institute of Japan (GSI) to compare the performance of two regional ionospheric models in Japan, in which the spherical cap harmonic (SCH) model has the best performance. In this paper, we investigated the spatial and temporal variations of ionospheric TEC in Japan and their relationship with latitude, longitude, seasons, and solar activity. The results show that the TEC in Japan increases as the latitude decreases, with the highest average TEC in spring and summer and the lowest in winter, and has a strong correlation with solar activity. In addition, the observation and analysis of ionospheric disturbances over Japan before the 2016 Kumamoto earthquake and geomagnetic storms showed that GNSS observing of ionospheric TEC seems to be very effective in forecasting natural disasters and monitoring space weather.

摘要

日本在全球导航卫星系统（GNSS）定位和灾害预测方面有大量优秀的研究案例，其中总电子含量（TEC）的模拟和预测是一种强大的研究方法。在这项研究中，我们使用了由日本地理调查研究所（GSI）建立的 GNSS 地球观测网络（GEONET）的数据，比较了日本两个区域电离层模型的性能，其中球形谐和（SCH）模型表现最佳。本文研究了日本电离层 TEC 的时空变化及其与纬度、经度、季节和太阳活动的关系。结果表明，日本的 TEC 随纬度降低而增加，春季和夏季的平均 TEC 最高，冬季最低，且与太阳活动有很强的相关性。此外，观测和分析 2016 年熊本地震和地磁暴前日本上空的电离层扰动表明，GNSS 观测电离层 TEC 似乎非常有效地预测自然灾害和监测空间天气。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/4dddeb23102d/sensors-21-02156-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/d707acdec22e/sensors-21-02156-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/8c48cf7ef919/sensors-21-02156-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/e013db4c746d/sensors-21-02156-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/780ecd110740/sensors-21-02156-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/3541a0f6f30f/sensors-21-02156-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/c875384652a5/sensors-21-02156-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/8bd62a894e9e/sensors-21-02156-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/6e02a1f18e06/sensors-21-02156-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/66aa08aed30c/sensors-21-02156-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/2929588a682c/sensors-21-02156-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/fdeb30155133/sensors-21-02156-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/a990baaea088/sensors-21-02156-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/1d27d09fedbd/sensors-21-02156-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/deac1f162535/sensors-21-02156-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/0a753d49fb97/sensors-21-02156-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/bcc5449c455b/sensors-21-02156-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/394318d5041a/sensors-21-02156-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/53cf3b34f0d6/sensors-21-02156-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/65670a5d87bd/sensors-21-02156-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/1598c609d693/sensors-21-02156-g020a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/77cac74eedcd/sensors-21-02156-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/4dddeb23102d/sensors-21-02156-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/d707acdec22e/sensors-21-02156-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/8c48cf7ef919/sensors-21-02156-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/e013db4c746d/sensors-21-02156-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/780ecd110740/sensors-21-02156-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/3541a0f6f30f/sensors-21-02156-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/c875384652a5/sensors-21-02156-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/8bd62a894e9e/sensors-21-02156-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/6e02a1f18e06/sensors-21-02156-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/66aa08aed30c/sensors-21-02156-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/2929588a682c/sensors-21-02156-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/fdeb30155133/sensors-21-02156-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/a990baaea088/sensors-21-02156-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/1d27d09fedbd/sensors-21-02156-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/deac1f162535/sensors-21-02156-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/0a753d49fb97/sensors-21-02156-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/bcc5449c455b/sensors-21-02156-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/394318d5041a/sensors-21-02156-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/53cf3b34f0d6/sensors-21-02156-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/65670a5d87bd/sensors-21-02156-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/1598c609d693/sensors-21-02156-g020a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/77cac74eedcd/sensors-21-02156-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2882/8003414/4dddeb23102d/sensors-21-02156-g022.jpg

相似文献

1
Study of Spatial and Temporal Variations of Ionospheric Total Electron Content in Japan, during 2014-2019 and the 2016 Kumamoto Earthquake.2014-2019 年日本电离层总电子含量的时空变化研究及 2016 年熊本地震
Sensors (Basel). 2021 Mar 19;21(6):2156. doi: 10.3390/s21062156.
2
Spatial and Temporal Variations of Polar Ionospheric Total Electron Content over Nearly Thirteen Years.近十三年来极区电离层总电子含量的时空变化。
Sensors (Basel). 2020 Jan 19;20(2):540. doi: 10.3390/s20020540.
3
Efficient Usage of Dense GNSS Networks in Central Europe for the Visualization and Investigation of Ionospheric TEC Variations.密集全球导航卫星系统网络在中欧用于电离层总电子含量变化可视化和研究的高效利用
Sensors (Basel). 2017 Oct 10;17(10):2298. doi: 10.3390/s17102298.
4
Detection of possible ionospheric precursor caused by Papua New Guinea earthquake (Mw 7.5).检测巴布亚新几内亚地震（Mw7.5）可能引发的电离层前兆。
Environ Monit Assess. 2020 Feb 20;192(3):190. doi: 10.1007/s10661-020-8146-0.
5
Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction.用于区域电离层重建的具有未知方差分量的克里金法。
Sensors (Basel). 2017 Feb 27;17(3):468. doi: 10.3390/s17030468.
6
Investigation of GIM-TEC disturbances before M ≥ 6.0 inland earthquakes during 2003-2017.2003-2017 年 M≥6.0 内陆地震前 GIM-TEC 干扰调查。
Sci Rep. 2020 Oct 22;10(1):18038. doi: 10.1038/s41598-020-74995-w.
7
Real-Time Detection of Tsunami Ionospheric Disturbances with a Stand-Alone GNSS Receiver: A Preliminary Feasibility Demonstration.利用独立全球导航卫星系统接收机实时探测海啸电离层扰动：初步可行性论证
Sci Rep. 2017 Apr 21;7:46607. doi: 10.1038/srep46607.
8
Lithosphere atmosphere ionosphere coupling associated with the 2019 M 7.1 California earthquake using GNSS and multiple satellites.利用 GNSS 和多颗卫星研究与 2019 年加利福尼亚 7.1 级地震相关的岩石圈-大气-电离层耦合
Environ Monit Assess. 2021 Jul 22;193(8):501. doi: 10.1007/s10661-021-09278-6.
9
Assessing Global Ionosphere TEC Maps with Satellite Altimetry and Ionospheric Radio Occultation Observations.利用卫星测高和电离层无线电掩星观测评估全球电离层 TEC 图。
Sensors (Basel). 2019 Dec 12;19(24):5489. doi: 10.3390/s19245489.
10
Machine learning based storm time modeling of ionospheric vertical total electron content over Ethiopia.基于机器学习的埃塞俄比亚电离层垂直总电子含量的风暴时间建模
Sci Rep. 2024 Aug 20;14(1):19293. doi: 10.1038/s41598-024-69738-0.

本文引用的文献

1
Criticality Analysis of the Lower Ionosphere Perturbations Prior to the 2016 Kumamoto (Japan) Earthquakes as Based on VLF Electromagnetic Wave Propagation Data Observed at Multiple Stations.基于多台站观测的甚低频电磁波传播数据对2016年日本熊本地震前低电离层扰动的临界分析
Entropy (Basel). 2018 Mar 16;20(3):199. doi: 10.3390/e20030199.
2
Real-Time Global Ionospheric Map and Its Application in Single-Frequency Positioning.实时全球电离层图及其在单频定位中的应用。
Sensors (Basel). 2019 Mar 6;19(5):1138. doi: 10.3390/s19051138.