Detection of short-term peaks in VLF/LF signals around the time of gama-ray burst detections using deep learning
基于深度学习检测伽玛射线暴期间VLF/LF信号短时峰值
Gamma-ray bursts (GRBs) are recognized as the most energetic and luminous astrophysical phenomena whose high-energy photons can ionize atmospheric particles and thus affect electrical conductivity. Consequently, electromagnetic waves such as Very Low and Low Frequency (VLF/LF) radio signals used for the lower ionosphere monitoring can be used to detect disturbances, i.e. the impact of GRBs on the area in which these signals propagate. This paper addresses application of deep learning for automatic detection the short-term peaks in VLF/LF signals around the time of satellite registration of GRBs. We utilized a refined hybrid architecture combining Convolutional Neural Networks (CNN) for automatic feature extraction and Long Short-Term Memory network (LSTM) for modeling sequential dependencies in the multi-channel time-series data. We used derived, peak-based representations of the raw ionospheric waveforms and trained the proposed neural network architecture on such pre-processed data representations. The experiments with the sample of 54 short-lasting GRBs revealed limitations due to extreme class imbalance; however, a refined approach utilizing manual adjustment of positive-class weights and optimization of the classification threshold achieved the best detection performance. The final CNN-LSTM model demonstrated strong performance metrics, confirming the viability of CNN-LSTM approaches in this task.
展开 ▾创新地将CNN-LSTM混合架构应用于VLF信号中GRB电离层效应的自动识别，通过手动调整正类权重与分类阈值克服极端类别不平衡，取得了强检测性能，证实了深度学习在该任务中的可行性。
伽玛射线暴（GRB）的高能光子可通过电离大气影响VLF/LF信号传播，传统方法多基于统计或人工识别扰动。本文首次采用CNN-LSTM混合深度学习模型，从预处理的信号峰值表示中自动提取时空特征，实现了对短时GRB电离层响应的有效检测。未来可融合多卫星触发数据、引入注意力机制或迁移学习，以提升对微弱事件的灵敏度与泛化能力。随着空间天气监测与人工智能深度结合，此类方法有望发展为实时、自动化的GRB电离层效应监测工具。
刊出 2026 · 收录 2026-07-28