基于气辉观测的中国南方电离层赤道等离子体泡标准数据集

钟佳; 邹自明; 徐寄遥; 吴坤; 孙龙昌; 袁韦; 胡晓彦

doi:10.11728/cjss2026.01.2025-0097

基于气辉观测的中国南方电离层赤道等离子体泡标准数据集

doi: 10.11728/cjss2026.01.2025-0097 cstr: 32142.14.cjss.2025-0097

钟佳^{1, 2, 第,},
邹自明^{1, 2,},
徐寄遥^1,,
吴坤³,
孙龙昌¹,
袁韦¹,
胡晓彦^{1, 2}

1.
中国科学院国家空间科学中心　北京　100190
2.
中国科学院国家空间科学数据中心　北京　101407
３.
长沙理工大学物理与电子科学学院　长沙　410114

详细信息

作者简介:

钟佳　男, 1987年出生, 博士, 现为中国科学院国家空间科学中心项目高级工程师, 主要研究方向为空间天气数据挖掘与机器学习建模、科学可视化技术. E-mail: zhongjia@nssc.ac.cn

邹自明　男, 1971年出生, 博士, 现为中国科学院国家空间科学中心研究员、国家空间科学数据中心主任、中国科学院大学博士生导师, 长期从事空间科学与数据科学交叉领域研究. E-mail: mzou@nssc.ac.cn

徐寄遥　男, 1959年出生, 博士, 现为中国科学院国家空间科学中心研究员、博士生导师, 国家杰出青年基金获得者, 现任国家重大科技基础设施“空间环境地基综合监测网——子午工程II期”总工程师 . 长期从事中高层大气物理学研究. E-mail: jyxu@spaceweather.ac.cn

中图分类号: P352
计量
- 文章访问数: 599
- HTML全文浏览量: 182
- PDF下载量: 15
- 被引次数:
  0(来源:Crossref)
  
  0(来源:其他)
出版历程
- 收稿日期: 2025-06-21
- 修回日期: 2025-10-22
- 网络出版日期: 2025-12-16

Standard Dataset of Ionospheric Equatorial Plasma Bubbles over Southern China Based on Airglow Observations

ZHONG Jia^{1, 2, 第
,},
ZOU Ziming^{1, 2
,},
XU Jiyao^1
,,
WU Kun³,
SUN Longchang¹,
YUAN Wei¹,
HU Xiaoyan^{1, 2}

1.
National Space Science Center, Chinese Academy of Sciences, Beijing 100190
2.
National Space Science Data Center, Chinese Academy of Sciences, Beijing 101407
３.
School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114

摘要

摘要: 气辉成像观测凭借其高时空分辨率及大范围连续监测能力, 为研究电离层赤道等离子体泡的水平精细结构及其演化特征提供了重要手段. 然而, 当前缺乏高质量、专业标注的等离子体泡数据集, 严重制约了监督式人工智能算法在该领域的应用研究. 为此, 研究构建了首个基于气辉观测的电离层等离子体泡标准数据集, 包含等离子体泡事件数据与精确轮廓标注数据. 该数据集源自云南曲靖站630 nm波段气辉成像仪历时一个完整太阳活动周(2012－2022年)的连续观测, 所有原始数据均经过图像增强、方位校正、几何畸变校正及地理坐标投影等标准化预处理, 并由专家团队完成等离子体泡事件识别及轮廓标注. 该数据集的时间分辨率高达3 min, 系统收录了不同太阳活动强度下的等离子体泡事件样本, 涵盖I型与Y型等多种典型形态. 本数据集为开发高精度监督式人工智能算法提供了高质量基准数据, 有助于促进基于气辉成像的电离层等离子体泡自动化检测与形态演化研究.
- 电离层赤道等离子体泡 /
- 气辉观测 /
- 标准数据集 /
- 轮廓标注 /
- 中国区域 /
- 太阳活动周 /
- 人工智能
Abstract: Airglow imaging observations, with their high spatiotemporal resolution and large-scale continuous monitoring capability, provide a crucial approach to studying the fine horizontal structures and evolutionary characteristics of ionospheric equatorial plasma bubbles. However, the current lack of high-quality, professionally annotated plasma bubble datasets severely restricts the application of supervised Artificial Intelligence (AI) algorithms in this field. To address this issue, this study constructs the first standardized dataset of ionospheric plasma bubbles based on airglow observations, including plasma bubble event data products and precise contour annotation data products. The dataset is derived from continuous observations over a full solar activity cycle (2012–2022) by a 630 nm band airglow imager at the Qujing Station in Yunnan, China. All raw data underwent standardized preprocessing, including image enhancement, azimuth correction, geometric distortion correction, and geographic coordinate projection. Expert teams then performed plasma bubble event identification and contour annotation. With a high temporal resolution of 3 minutes, the dataset systematically documents plasma bubble events under varying solar activity intensities, covering multiple typical morphologies such as shaped I- and Y-shaped structures. This dataset provides high-quality benchmark data for developing high-precision supervised AI algorithms, facilitating automated detection and morphological evolution research of ionospheric plasma bubbles based on airglow imaging.
- Ionospheric equatorial plasma bubbles /
- Airglow observation /
- Standardized dataset /
- Contour annotation /
- China region /
- solar activity cycle /
- Artificial Intelligence (AI)
注释:

1) 第一作者、通信作者

HTML全文

图 1 曲靖站 (红色点) 全天空气辉成像仪$ {\mathbf{160}}^{{^{\circ}}} $有效观测视场角覆盖空间范围

Figure 1. Spatial coverage of the 160°effective Field-of-View (FOV) for the all-sky airglow imager at Qujing Station (Red dot)

下载: 全尺寸图片幻灯片

图 2 全天空气辉成像仪实物^[21]

Figure 2. Picture of the all-sky airglow imager

下载: 全尺寸图片幻灯片

图 3 全天空气辉成像仪内部结构^[21]

Figure 3. All-sky airglow imager internal structure

下载: 全尺寸图片幻灯片

图 4 全天空气辉成像仪原始观测数据

Figure 4. Raw observational data from the all-sky airglow imager

下载: 全尺寸图片幻灯片

图 5 数据集加工流程

Figure 5. Dataset processing pipeline

下载: 全尺寸图片幻灯片

图 6 曲靖站2014年3月1日23:42:01 (UTC+8) 的等离子体泡事件图像

Figure 6. Plasma bubble event image observed at Qujing Station on 1 March 2014 at 23:42:01 (UTC+8)

下载: 全尺寸图片幻灯片

图 7 简单I型等离子体泡事件的轮廓标注. (a) 待标注的等离子体泡观测图像, (b)等离子体泡掩膜图像

Figure 7. Contour annotation of a simple I-shaped plasma bubble event. (a) Observation image of plasma bubble to be annotated, (b) mask image of plasma bubble

下载: 全尺寸图片幻灯片

图 8 具有复杂分叉结构的Y型等离子体泡事件轮廓标注. (a) 待标注的等离子体泡观测图像, (b)等离子体泡掩膜图像

Figure 8. Contour annotation of a Y-shaped plasma bubble event with complex bifurcation structure. (a) Observation image of plasma bubble to be annotated, (b) mask image of plasma bubble

下载: 全尺寸图片幻灯片

图 9 多个I型与Y型离子体泡事件轮廓标注. (a) 待标注的等离子体泡观测图像, (b)等离子体泡掩膜图像

Figure 9. Contour annotation of multiple I-shaped and Y-shaped plasma bubble events . (a) Observation image of plasma bubble to be annotated, (b) mask image of plasma bubble

下载: 全尺寸图片幻灯片

图 10 等离子体泡事件轮廓标注文件

Figure 10. Contour annotation file of plasma bubble events

下载: 全尺寸图片幻灯片

图 11 等离子体泡标准数据集文件组织形式

Figure 11. File organization format of plasma bubble standard dataset

下载: 全尺寸图片幻灯片

图 12 等离子体泡样本逐年逐月分布与太阳活动水平的关系^[25].

Figure 12. Relationship between interannual and monthly distributions of plasma bubble samples and solar activity levels

下载: 全尺寸图片幻灯片

图 13 等离子体泡图像数量年均值与太阳射电流量年均值的关系

Figure 13. Relationship between the annual mean number of EPB images and the annual mean solar flux

下载: 全尺寸图片幻灯片

表 1 曲靖台站不同年份观测夜晚与气辉观测图像及等离子体泡样本分布

Table 1. Distribution of observation nights, airglow observation images and plasma bubble samples at Qujing Station in different years

Year	Observation nights	Clear nights	No. of images	No. of good quality images	No. of EPB images
2012	291	108	43389	13639	474
2013	347	132	58441	16012	799
2014	357	145	68662	18066	1893
2015	360	121	75368	19112	889
2016	323	112	65849	18012	99
2017	341	110	71820	17739	0
2018	313	129	64227	19510	21
2019	294	133	60604	18701	0
2020	313	134	62052	17150	25
2021	363	135	77322	20012	20
2022	355	133	75641	20124	444
Total	3657	1392	723375	198077	4664

下载: 导出CSV

参考文献(26)

[1]	OTT E. Theory of Rayleigh-Taylor bubbles in the equatorial ionosphere[J]. Journal of Geophysical Research: Space Physics, 1978, 83(A5): 2066-2070 doi: 10.1029/JA083iA05p02066
[2]	WEBER E J, BUCHAU J, EATHER R H, et al. North-south aligned equatorial airglow depletions[J]. Journal of Geophysical Research: Space Physics, 1978, 83(A2): 712-716 doi: 10.1029/JA083iA02p00712
[3]	KELLEY M C. The Earth’s ionosphere: Plasma physics and electrodynamics, Second Edition[M]. Boston: Academic Press, c2009. Hardback ISBN: 9780120884254
[4]	AGGSON T L, LAAKSO H, MAYNARD N C, et al. In situ observations of bifurcation of equatorial ionospheric plasma depletions[J]. Journal of Geophysical Research: Space Physics, 1996, 101(A3): 5125-5132 doi: 10.1029/95ja03837
[5]	IMOÇIN E, INYURT S, TEMUÇIN H, et al. Investigation of equatorial plasma bubble irregularities under different geomagnetic conditions during the equinoxes and the occurrence of plasma bubble suppression[J]. Acta Astronautica, 2020, 177: 341-350 doi: 10.1016/j.actaastro.2020.08.007
[6]	WOODMAN R F, LA HOZ C. Radar observations of F region equatorial irregularities[J]. Journal of Geophysical Research, 1976, 81(31): 5447-5466 doi: 10.1029/JA081i031p05447
[7]	WOODMAN R F. Spread F-an old equatorial aeronomy problem finally resolved[J]. Annales Geophysicae, 2009, 27(5): 1915-1934 doi: 10.5194/angeo-27-1915-2009
[8]	MENDILLO M, BAUMGARDNER J. Airglow characteristics of equatorial plasma depletions[J]. Journal of Geophysical Research: Space Physics, 1982, 87(A9): 7641-7652 doi: 10.1029/JA087iA09p07641
[9]	SAHAI Y, FAGUNDES P R, BITTENCOURT J A. Transequatorial F-region ionospheric plasma bubbles: solar cycle effects[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2000, 62(15): 1377-1383 doi: 10.1016/S1364-6826(00)00179-6
[10]	KELLEY M C, MAKELA J J, LEDVINA B M, et al. Observations of equatorial spread-F from Haleakala, Hawaii[J]. Geophysical Research Letters, 2002, 29(20): 2003 doi: 10.1029/2002GL015509
[11]	MAKELA J J, LEDVINA B M, KELLEY M C, et al. Analysis of the seasonal variations of equatorial plasma bubble occurrence observed from Haleakala, Hawaii[J]. Annales Geophysicae, 2004, 22(9): 3109-3121 doi: 10.5194/angeo-22-3109-2004
[12]	TAORI A, SINDHYA A. Measurements of equatorial plasma depletion velocity using 630 nm airglow imaging over a low-latitude Indian station[J]. Journal of Geophysical Research: Space Physics, 2014, 119(1): 396-401 doi: 10.1002/2013JA019465
[13]	NARAYANAN V L, GURUBARAN S, SHINY M B B, et al. Some new insights of the characteristics of equatorial plasma bubbles obtained from Indian region[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2017, 156: 80-86 doi: 10.1016/j.jastp.2017.03.006
[14]	WANG C. New chains of space weather monitoring stations in China[J]. Space Weather, 2010, 8(8): S08001 doi: 10.1029/2010SW000603
[15]	LI Q Z, XU J Y, GUSMAN A R, et al. Upper-atmosphere responses to the 2022 Hunga Tonga-Hunga Ha′apai volcanic eruption via acoustic gravity waves and air-sea interaction[J]. Atmospheric Chemistry and Physics, 2024, 24(14): 8343-8361 doi: 10.5194/acp-24-8343-2024
[16]	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90 doi: 10.1145/3065386
[17]	SMIRNOV E A, TIMOSHENKO D M, ANDRIANOV S N. Comparison of regularization methods for ImageNet classification with deep convolutional neural networks[J]. AASRI Procedia, 2014, 6: 89-94 doi: 10.1016/j.aasri.2014.05.013
[18]	LIU L, OUYANG W L, WANG X G, et al. Deep learning for generic object detection: a survey[J]. International Journal of Computer Vision, 2020, 128(2): 261-318 doi: 10.1007/s11263-019-01247-4
[19]	LAI C, XU J Y, YUE J, et al. Automatic extraction of gravity waves from all-sky airglow image based on machine learning[J]. Remote Sensing, 2019, 11(13): 1516 doi: 10.3390/rs11131516
[20]	LAI C, XU J Y, LIN Z S, et al. Statistical characteristics of nighttime medium-scale traveling ionospheric disturbances from 10-years of airglow observation by the machine learning method[J]. Space Weather, 2023, 21(5): e2023SW003430 doi: 10.1029/2023SW003430
[21]	SUN Longchang. Study on Low- and Mid-Latitudinal Ionospheric Irregularity Based on Ground-Based and Satellite Observations[D]. Beijing: University of Chinese Academy of Sciences (National Space Science Center, Chinese Academy of Sciences), 2017: 47-48
[22]	GARCIA F J, TAYLOR M J, KELLEY M C. Two-dimensional spectral analysis of mesospheric airglow image data[J]. Applied Optics, 1997, 36(29): 7374-7385 doi: 10.1364/AO.36.007374
[23]	HUANG C Y, BURKE W J, MACHUZAK J S, et al. Equatorial plasma bubbles observed by DMSP satellites during a full solar cycle: toward a global climatology[J]. Journal of Geophysical Research: Space Physics, 2002, 107(A12): 1434 doi: 10.1029/2002JA009452
[24]	WU K, XU J Y, ZHU Y J, et al. Occurrence characteristics of branching structures in equatorial plasma bubbles: a statistical study based on all-sky imagers in China[J]. Earth and Planetary Physics, 2021, 5(5): 407-415 doi: 10.26464/epp2021044
[25]	ZHONG J, ZOU Z M, WU K, et al. Automatic detection and feature extraction of equatorial plasma bubbles from all-sky airglow image based on machine learning[J]. Space Weather, 2025, 23(5): e2025SW004336 doi: 10.1029/2025SW004336
[26]	HAASE J S, DAUTERMANN T, TAYLOR M J, et al. Propagation of plasma bubbles observed in Brazil from GPS and airglow data[J]. Advances in Space Research, 2011, 47(10): 1758-1776 doi: 10.1016/j.asr.2010.09.025