测绘科学技术学报2025，Vol.41Issue(1)：27-36,10.DOI:10.3969/j.issn.1673-6338.2025.01.005
基于多通道CNN-GRU的低纬度区域电离层预测研究
Research on Low-latitude Small-area Ionospheric Prediction Based on Multi-channel CNN-GRU
摘要
Abstract
The northern crest of the equatorial ionization anomaly covers southern China,where the"fountain effect"creates highly complex ionospheric dynamics that significantly affect the accuracy of satellite navigation and positioning.In this paper,Yunnan and Sichuan are selected as study areas.Based on data from 48 GNSS observa-tion stations of China's Crustal Movement Observation Network,a convolutional neural network-gated recurrent unit(CNN-GRU)algorithm with multi-channel characteristics is applied to study ionospheric prediction in low-latitude regions.In terms of spatial features,the CNN-GRU can effectively predict ionospheric spatial structures at various scales,with a correlation coefficient better than 0.9.However,there is a noticeable prediction error near the south-ern boundary region.Temporally,the model's prediction error in the 0～12 h time scale is smaller than that in the 12～24 h time scale,with an overall error of less than 1.7 TECu.Moreover,the prediction accuracy during sol-stices is better than that during equinoxes.The validation results show that this TEC model can significantly im-prove the ionospheric prediction accuracy in low-latitude regions,providing effective support for high-precision navigation,positioning,and space environment monitoring.关键词
电离层模型/深度学习/总电子含量/川滇区域/多通道/低纬区域Key words
ionospheric model/deep learning/total electron content/Sichuan-Yunnan area/multi-channel/low-latitude regions分类
测绘与仪器引用本文复制引用
张仁中,杨嘉祎,李家乐,陈冠宇,刘佳悦,李豪瑞,申云萧,李旺..基于多通道CNN-GRU的低纬度区域电离层预测研究[J].测绘科学技术学报,2025,41(1):27-36,10.基金项目
国家自然科学基金项目(42204030) （42204030）
云南省"兴滇英才支持计划"项目 （）
云南省基础研究计划项目(202201BE070001-035 （）
202301AU070062) （）
昆明理工大学学生课外学术科技创新基金项目(2024ZK093). （2024ZK093）
