空间科学学报2025，Vol.45Issue(5)：1197-1210,14.DOI:10.11728/cjss2025.05.2024-0132
强鲁棒高速闪电哨声波自动检测模型
A Robust and High-speed Automated Detection Model for Lightning Whistler
摘要
Abstract
The Zhangheng Satellite has accumulated a vast amount of observational data over its six years in orbit.Detecting all Lightning Whistler(LW)events from this dataset is crucial for comprehen-sively analyzing the variation patterns of the space physical environment.However,using the current mainstream LW detection technology,which is based on time-frequency spectrograms,it would take ap-proximately 40 years to complete this task.To address the slow inference speed and meet practical engi-neering demands,this study proposes,for the first time,a high-speed detection model for lightning whistler waves from the perspective of audio event detection—WhisNet.This model reduces the time cost from 40 years to just 54 days.First,waveform data is segmented using a 4-second sliding window;then,Mel-spectrogram audio features are extracted.Next,a lightweight Convolutional Recurrent Neural Network(CRNN)is constructed to further extract the audio event features of LW.Finally,two fully connected networks are created at the output layer to predict the start time and duration of each LW event.To evaluate the model's performance and computational speed,experiments were conducted on data from the SCM(Search Coil Magnetometer)between April 1 and April 10,2020.The results show that the performance of the WhisNet model is comparable to that of time-frequency image-based meth-ods,but with a 99%reduction in computational and parameter costs and a 98%increase in computation-al speed.The model was further applied to SCM data from May 2020,and the detection results were sta-tistically analyzed and visually compared to the average lightning density trend from the WWLLN Glo-bal Lightning Climatology and time series(WGLC)for May 2020.The high consistency between the two further confirms the applicability and accuracy of the WhisNet model in processing large-scale satellite data.This method offers significant reference value for thoroughly exploring other large-scale geospace events.关键词
闪电哨声波/高速检测/张衡一号卫星/卷积循环神经网络Key words
Lightning Whistler(LW)/High-speed detection/Zhangheng-1 Satellite/Convolutional Recurrent Neural Network(CRNN)分类
地球科学引用本文复制引用
银韩柯,袁静,赵庶凡,申旭辉,靳晓媛,王桥,廖力,杨德贺..强鲁棒高速闪电哨声波自动检测模型[J].空间科学学报,2025,45(5):1197-1210,14.基金项目
河北省教育厅科学研究项目(ZC2024028)和民用航天技术预先研究项目(D040203)共同资助 （ZC2024028）
