ArticleSensors (Basel, Switzerland)2020
Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Study on Multi-Station Identification Technology of Lightning Electromagnetic Pulses (LEMPs) Based on Deep Learning.Sensors (Basel, Switzerland) · 2025Article
- High-Power-Density Miniaturized VLF Antenna with Nanocrystalline Core for Enhanced Field Strength.Nanomaterials (Basel, Switzerland) · 2025Article
- Application research of convolutional neural network and its optimization in lightning electric field waveform recognition.Scientific reports · 2025Article
- An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and Autoencoder.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
9 authors.
Funding
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Abstract
Lightning waveform plays an important role in lightning observation, location, and lightning disaster investigation. Based on a large amount of lightning waveform data provided by existing real-time very low frequency/low frequency (VLF/LF) lightning waveform acquisition equipment, an automatic and accurate lightning waveform classification method becomes extremely important. With the widespread application of deep learning in image and speech recognition, it becomes possible to use deep learning to classify lightning waveforms. In this study, 50,000 lightning waveform samples were collected. The data was divided into the following categories: positive cloud ground flash, negative cloud ground flash, cloud ground flash with ionosphere reflection signal, positive narrow bipolar event, negative narrow bipolar event, positive pre-breakdown process, negative pre-breakdown process, continuous multi-pulse cloud flash, bipolar pulse, skywave. A multi-layer one-dimensional convolutional neural network (1D-CNN) was designed to automatically extract VLF/LF lightning waveform features and distinguish lightning waveforms. The model achieved an overall accuracy of 99.11% in the lightning dataset and overall accuracy of 97.55% in a thunderstorm process. Considering its excellent performance, this model could be used in lightning sensors to assist in lightning monitoring and positioning.
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