基于相位编组与多级阈值的太阳射电Ⅲ型暴自动检测算法
doi: 10.11728/cjss2026.03.2025-0134 cstr: 32142.14.cjss.2025-0134
-
张洪瑞 男, 2000年7月出生于云南省普洱市, 现为云南大学信息学院硕士研究生, 主要研究方向为太阳射电暴频谱的自动识别、无线电环境测试等. E-mail: hongruizhang085@163.com -
董亮 男, 1982年9月出生于四川省自贡市, 现为中国科学院云南天文台高级工程师, 硕士生导师, 主要研究方向为射电天文技术及其转化应用、空间天气研究(太阳射电爆发干扰导航通信预警、预报技术研究, 空间天气中生物效应的研究)、无线电环境测试技术. E-mail: dongliang@ynao.ac.cn
作者简介:
通讯作者:
An Automated Detection Method for Solar Radio Type III Bursts Using Phase Clustering and Hierarchical Thresholding
-
摘要: 太阳射电III型暴是太阳活动中最为常见的射电爆发类型之一, 其具有快速频率漂移等特征, 是耀斑爆发和日冕物质抛射等太阳活动事件的示踪器. 现有的识别方法面临连续爆发检测能力弱、小样本学习不足等问题, 导致误检率高和召回率低. 针对这些缺陷, 提出一种基于相位编组法的自适应轮廓检测算法, 该方法融合灰度梯度相位信息与多级阈值过滤, 通过形态学操作拟合太阳射电Ⅲ型暴频谱轮廓, 实现对太阳射电Ⅲ型暴频谱的自动识别, 通过计算对应的爆发起止时间、爆发起止频率、频率漂移率等有效信息, 对海量频谱观测数据进行快速批量识别与分析. 基于云南天文台澄江米波太阳射电望远镜的观测数据集开展应用, 经检验, 该方法在识别太阳射电Ⅲ型暴上实现了93.5%的召回率.Abstract: Solar radio type III bursts are among the most common types of radio bursts in solar activities. They are characterized by features such as rapid frequency drift and serve as a precursor tracer for solar activity events like solar flares and coronal mass ejections. Existing identification methods face problems such as weak capability in detecting continuous bursts and insufficient learning with small samples, leading to high false detection rates and low recall rates. To address these shortcomings, this paper proposes an adaptive contour detection algorithm based on the phase grouping method. This method integrates gray gradient phase information with multi-level threshold filtering, fits the spectral contour of solar radio type III bursts through morphological operations, and realizes the automatic identification of the spectrum of solar radio type III bursts. Applied to the observation dataset from the Chengjiang Meter-wave Solar Radio Telescope of Yunnan Observatory, it has been verified that this method achieves a 93.5% recall rate in identifying solar radio type III bursts.
-
图 8 模拟形态学闭运算连接轮廓过程(红色表示结构元素中心, 绿色为相位编组法计算后有边缘特征的各段零散轮廓)
Figure 8. Simulated morphological closed operation connection contour process (Red represents the center of structural elements, and green represents the scattered segmented contours with edge features obtained via the phase grouping method )
图 14 多种形态的太阳射电Ⅲ型暴识别结果
Figure 14. Recognition results of Solar Radio Type III Bursts with various morphologies
表 1 核尺寸连接性能对比表
Table 1. Comparison table of nuclear size connection performance
核尺寸 特征边缘连接正确率/(%) 噪声连接错误率/(%) 3×2 37.5 0.9 5×2 73.5 4.6 7×2 92.6 10.3 9×2 94.8% 39.8 
下载: 导出CSV
-
[1] LIU Jing, WANG Wenbin, QIAN Liying, et al. Solar flare effects in the Earth’s magnetosphere[J]. Nature Physics, 2021, 17(7): 807-812 doi: 10.1038/s41567-021-01203-5 [2] SCHRIJVER C J, KAURISTIE K, AYLWARD A D, et al. Understanding space weather to shield society: A global road map for 2015–2025 commissioned by COSPAR and ILWS. Advances in Space Research, 2015, 55(12): 2745-2807 [3] WILD J P. The radio-frequency line spectrum of solar noise and its interpretation in terms of fundamental physical theory. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 1950, 202(1068): 113-125 [4] 谭宝林, 谭程明, 黄静, 等. 空间甚低频太阳射电Ⅲ型爆研究进展[J]. 深空探测学报(中英文), 2021, 8(01): 92-99TAN Baolin, TAN Chengming, HUANG Jing, et al. Research progress on space very low frequency solar radio Type Ⅲ bursts[J]. Journal of Deep Space Exploration (Chinese & English), 2021, 8(01): 92-99 [5] Kontar E P, Jeffrey N L S, Emslie A G, et al. High-time-resolution observations of Type III radio bursts and their connection to solar flare particle acceleration. Solar Physics, 2018, 293(10): 132 [6] Wang Mingming, Yuan Guowu, He Hailan, et al. Multi-category solar radio burst detection based on task-aligned one-stage object detection model. Astrophysics Space Science, 2025, 370: 23 [7] Lobzin V V, Cairns I H, Robinson P A, et al. Automatic recognition of type III solar radio bursts: automated radio burst identification system method and first observations. Space Weather, 2009, 7(4), S04002 [8] 高昌林. 太阳射电动态频谱图降噪与爆发检测方法研究[D]. 山东大学, 2021GAO Changlin. Research on Denoising and Burst Detection Methods for Solar Radio Dynamic Spectrograms[D]. Shandong University, 2021 [9] 沈发新, 高冠男, 汪敏. 基于概率霍夫变换的太阳射电Ⅲ和Ⅱ型暴自动识别及参数提取[J]. 天文研究与技术, 2022, 19(06): 559-567SHEN Faxin, GAO Guannan, WANG Min. Automatic identification and parameter extraction of solar radio type Ⅲ and type Ⅱ bursts based on probabilistic Hough transform[J]. Astronomical Research & Technology, 2022, 19(06): 559-567 [10] BUSSONS G J, FERNANDEZ R M, PRIETO M M, et al. Automatic burst detection in solar radio spectrograms using deep learning: deARCE method. Solar Physics, 2023, 298: 82 [11] Deng J , Yuan G , Zhou H , et al. Real-time automated detection of multi-category solar radio bursts[J]. Astrophysics and Space Science, 2024, 369(10): 99 [12] 张娟, 沙爱民, 孙朝云, 等. 基于相位编组法的路面裂缝自动识别[J]. 中国公路学报, 2008(02): 39-42 doi: 10.3321/j.issn:1001-7372.2008.02.008Zhang Juan, Sha Aimin, Sun Chaoyun, et al. Automatic identification of pavement cracks based on phase grouping method[J]. China Journal of Highway and Transport, 2008(02): 39-42 doi: 10.3321/j.issn:1001-7372.2008.02.008 [13] KASS M, WITKIN A. Analyzing oriented patterns[J]. Computer Vision, Graphics, and Image Processing, 1987, 37(3): 362-385 doi: 10.1016/0734-189X(87)90043-0 [14] 宋锐, 杨腾云浩, 李依倚, 等. 一种融合形态学闭运算的双SVM卫生陶瓷缺陷检测方法[J]. 自动化应用, 2021(06): 14-17,22 doi: 10.19769/j.zdhy.2021.06.005Song Rui, Yang Tengyunhao, Li Yiyi, et al. A method for detecting defects in sanitary ceramics using dual SVM combined with morphological closing operation[J]. Automation Application, 2021(06): 14-17,22 doi: 10.19769/j.zdhy.2021.06.005 [15] SERRA J. Image Analysis and Mathematical Morphology[M]. London: Academic Press, 1982 -
-
下载:
图(14) / 表(1)
计量
- 文章访问数: 499
- HTML全文浏览量: 70
- PDF下载量: 83
-
被引次数:
0(来源:Crossref)
0(来源:其他)
