空间科学学报2025，Vol.45Issue(3)：662-676,15.DOI:10.11728/cjss2025.03.2024-0039
极光亚暴爆发时点机器识别方法
Machine Identification Method of Auroral Substorm Onset Time
摘要
Abstract
Auroral substorm is a geomagnetic disturbance resulting from the interaction between Earth's magnetic field and the solar wind.The accurate identification of the onset times is crucial for a deep understanding of the underlying physical mechanisms.The existing machine methods for auroral substorm identification differ from manual identification standards and typically require complex image preprocessing and parameter tuning by manual.To achieve a machine model consistent with manual identification standards,this paper designs two identification strategies aimed at addressing the issue of variable image sequence lengths encountered in replicating manual standards.Based on deep learning methods,this paper proposes an EfficientNet model featuring CBAM attention as a key component for model construction.The model is trained using ultraviolet auroral images from the Polar satellite be-tween 1996 and 1998 and tested on image data from 1999 to 2000.The model achieves an identification accuracy of up to 0.98 and an efficiency of 36.93 frames per second.This model not only eliminates the reliance on image preprocessing present in existing models but also adapts to real-world observations with unequal image sequence lengths and extreme imbalances in the number of samples between sub-storm and non-substorm sequences,demonstrating its high practicality.关键词
极光亚暴/爆发时点/机器识别/深度学习Key words
Auroral substorm/Onset time/Machine identification/Deep learning分类
天文与地球科学引用本文复制引用
蒋家楠,邹自明,陆阳..极光亚暴爆发时点机器识别方法[J].空间科学学报,2025,45(3):662-676,15.基金项目
国家重点研发计划专项项目(2022YFF0711400),中国科学院"十四五"网络安全和信息化专项项目(CAS-WX2022SDC-XK15)和中国科学院网信专项项目(CAS-WX2022SF-0103)共同资助 （2022YFF0711400）
