Abstract Commercial lightning detection networks offer detailed lightning stroke data upon subscription. Alternatively, the lightning monitoring system can be deployed at lower cost using Very Low Frequency (VLF) signal reception. With that, a dual-class high-intensity lightning recognition algorithm was developed using VLF signal features to identify the severity of lightning occurrence in the vicinity of 500 km from a single VLF signal path. The algorithm combined feature extraction, lightning detection, and lightning severity classification modules to identify two lightning severity classes based on the detected lightning peak current (by Lightning Detection System Network). For each lightning stroke sample, 20 VLF signal features (in terms of wavelet coefficient) in five signal parameters of four different levels of frequency bands were extracted as pattern representation prior to the classification. A combination of bagged and boosted trees ensemble classifiers achieved a lightning detection rate of 64.0% and correct lightning severity classification of 60.3%. These outstanding results exceeded lightning detection rate of between 20% and 40% in related past studies that applied the early VLF events technique. Good design practices such as cross-validation, feature selection, and ensemble classifiers contribute good generalisation and unbiased classification for small dataset.

中文翻译：

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使用极低频信号特征的高强度闪电识别系统

摘要 商业闪电探测网络在订阅后提供详细的雷击数据。或者，可以使用甚低频 (VLF) 信号接收以更低的成本部署闪电监测系统。据此，利用甚低频信号特征开发了一种双级高强度闪电识别算法，用于识别单个甚低频信号路径500公里附近闪电发生的严重程度。该算法结合特征提取、雷电检测和雷电严重程度分类模块，根据检测到的雷电峰值电流（通过闪电检测系统网络）识别两个雷电严重程度等级。对于每个雷击样本，在分类之前，提取了四个不同级别频带的五个信号参数中的20个VLF信号特征（就小波系数而言）作为模式表示。袋装和提升树集成分类器的组合实现了 64.0% 的闪电检测率和 60.3% 的正确闪电严重性分类。这些出色的结果超过了过去应用早期 VLF 事件技术的相关研究中 20% 至 40% 的闪电检测率。交叉验证、特征选择和集成分类器等良好的设计实践为小数据集提供了良好的泛化和无偏分类。0% 和正确的闪电严重性分类 60.3%。这些出色的结果超过了过去应用早期 VLF 事件技术的相关研究中 20% 至 40% 的闪电检测率。交叉验证、特征选择和集成分类器等良好的设计实践为小数据集提供了良好的泛化和无偏分类。0% 和正确的闪电严重性分类 60.3%。这些出色的结果超过了过去应用早期 VLF 事件技术的相关研究中 20% 至 40% 的闪电检测率。交叉验证、特征选择和集成分类器等良好的设计实践为小数据集提供了良好的泛化和无偏分类。