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YIN Hanke, YUAN Jing, ZHAO Shufan, SHEN Xuhui, JIN Xiaoyuan, WANG Qiao, LIAO Li, YANG Dehe. A Robust and High-Speed Automated Detection Model for Lightning Whistler (in Chinese). Chinese Journal of Space Science, 2025, 45(5): 1-14 doi: 10.11728/cjss2025.05.2024-0132
Citation: YIN Hanke, YUAN Jing, ZHAO Shufan, SHEN Xuhui, JIN Xiaoyuan, WANG Qiao, LIAO Li, YANG Dehe. A Robust and High-Speed Automated Detection Model for Lightning Whistler (in Chinese). Chinese Journal of Space Science, 2025, 45(5): 1-14 doi: 10.11728/cjss2025.05.2024-0132
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A Robust and High-Speed Automated Detection Model for Lightning Whistler

doi: 10.11728/cjss2025.05.2024-0132 cstr: 32142.14.cjss.2024-0132
  • 1.

    Institute of Disaster Prevention, Langfang 065201

  • 2.

    National Space Science Center, Chinese Academy of Sciences, Beijing 100190

  • 3.

    Institute of Geophysics, China Earthquake Administration, Beijing 100081

  • 4.

    National Institute of Natural Hazards, Beijing 100085

  • Received Date: 2024-10-22
  • Rev Recd Date: 2025-03-31
    • Available Online: 2025-03-31
    Abstract
  • The Zhangheng Satellite has accumulated a vast amount of observational data over its six years in orbit. Detecting all Lightning Whistler Wave (LW) events from this dataset is crucial for comprehensively 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 approximately 40 years to complete this task. To address the slow inference speed and meet practical engineering 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 methods, but with a 99% reduction in computational and parameter costs and a 98% increase in computational speed. The model was further applied to SCM data from May 2020, and the detection results were statistically analyzed and visually compared to the average lightning density trend from the WWLLN Global Lightning Climatology and timeseries (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.

     

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