Hai-Nan Lightning Detection Network (HNLDN) is composed of seven sites that accept very low frequency/low frequency (VLF/LF) signals. A total of seven sites are evenly distributed on Hai-Nan Island. The baseline distance is between 50 and 150 km, which is about five times longer than in typical short-baseline VLF/LF imaging networks. Each detection station uses zero-phase digital filtering to avoid the phase deviation caused by filtering. HNLDN uses an improved peak search method to increase the number of detected of lightning waveforms, applies a One-dimensional convolutional neural network (1D-CNN) to identify and classify the waveforms, and to the increase the detection efficiency of intracloud lightning. This article analyzes three typical thunderstorm processes. From inside to outside the network, the detection efficiency of HNLDN relative to the advanced direction-time lightning detection system (ADTD) is 239%, 181%, and 19%, respectively. It can be seen that the detection efficiency is negatively correlated with the distance from the thunderstorm to HNLDN. The average location error of HNLDN inside the network is 190 m.

中文翻译：

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海南闪电探测网络（HNLDN）初步评估

海南雷电探测网（HNLDN）由七个接受极低频/低频（VLF/LF）信号的站点组成。共有七个站点均匀分布在海南岛。基线距离在 50 到 150 公里之间，比典型的短基线 VLF/LF 成像网络长约 5 倍。各检测站均采用零相位数字滤波，避免滤波引起的相位偏差。HNLDN采用改进的峰值搜索方法增加闪电波形的检测次数，应用一维卷积神经网络（1D-CNN）对波形进行识别和分类，提高云内闪电的检测效率。本文分析了三种典型的雷暴过程。从内网到外网，HNLDN相对于先进的定向时间闪电探测系统（ADTD）的探测效率分别为239%、181%和19%。可以看出，检测效率与雷暴到HNLDN的距离呈负相关。HNLDN在网络内部的平均定位误差为190 m。