Wang Caixia, Zhang Xiaoyi, Yang Hui, Guo Jinyuan, Xu Jia, Sun Zhuling
School of Applied Science, Beijing Information Science and Technology University, Beijing, China.
Guangdong Provincial Key Laboratory of Geophysical High-resolution Imaging Technology, Southern University of Science and Technology, Shenzhen, 518055, China.
Sci Rep. 2025 Jan 13;15(1):1883. doi: 10.1038/s41598-025-85473-6.
Quickly identifying and classifying lightning waveforms is the foundation of lightning forecasting and early warning. In this paper, based on the electric field observation of the Beijing lightning location website of the Institute of Atmospheric Physics, Chinese Academy of Sciences, a recognition and classification method of pulse signal waveform based on Convolutional Neural Network(CNN) algorithm is designed and implemented. The CNN network model and its parameters were optimized from three aspects: dataset, model parameters, and network structure, achieving a recognition rate of over 90%. The effects of various optimization terms and their different optimization orders on the training time of the model were studied. The results indicate that the CNN algorithm is suitable for the classification and recognition of lightning electric field (LEF) waveforms. Optimization can significantly improve recognition rate. The optimization method of fitting idealized waveforms can reduce noise in the dataset and significantly improve recognition rate, indicating that noise has a significant impact on waveform recognition. Therefore, it is necessary to perform noise preprocessing before recognition. The optimization has a huge impact on training efficiency, increasing training time by about 51% after optimization, but the influence of optimization order on it can be ignored.
快速识别和分类闪电波形是雷电预报和预警的基础。本文基于中国科学院大气物理研究所北京闪电定位站的电场观测数据，设计并实现了一种基于卷积神经网络（CNN）算法的脉冲信号波形识别与分类方法。从数据集、模型参数和网络结构三个方面对CNN网络模型及其参数进行了优化，识别率达到了90%以上。研究了各种优化项及其不同优化顺序对模型训练时间的影响。结果表明，CNN算法适用于闪电电场（LEF）波形的分类与识别。优化可以显著提高识别率。拟合理想化波形的优化方法可以降低数据集中的噪声，并显著提高识别率，表明噪声对波形识别有显著影响。因此，在识别前进行噪声预处理是必要的。优化对训练效率有巨大影响，优化后训练时间增加了约51%，但优化顺序对其影响可忽略不计。
