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Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods.

Jiaquan Wang^1,2, Qijun Huang¹, Qiming Ma²

¹School of Physics and Technology, Wuhan University, Wuhan 430072, China.

Sensors (Basel, Switzerland)

|February 21, 2020

Summary

This summary is machine-generated.

This study introduces an accurate deep learning method for classifying lightning waveforms using a one-dimensional convolutional neural network (1D-CNN). The model achieved high accuracy, aiding in lightning observation and disaster investigation.

Keywords:

VLF/LF lightning waveform automatic classification convolutional neural network (CNN)deep learning

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Area of Science:

Atmospheric Physics
Electrical Engineering
Computer Science

Background:

Lightning waveform analysis is crucial for observation, location, and disaster investigation.
Existing methods require accurate and automatic lightning waveform classification.
Deep learning offers potential for advanced waveform analysis.

Purpose of the Study:

To develop an automatic and accurate lightning waveform classification method.
To utilize deep learning for distinguishing various lightning waveform types.
To enhance lightning monitoring and positioning capabilities.

Main Methods:

Collected and categorized 50,000 lightning waveform samples.
Designed a multi-layer one-dimensional convolutional neural network (1D-CNN).
Employed the 1D-CNN to automatically extract features and classify waveforms.

Main Results:

Achieved 99.11% overall accuracy on the lightning dataset.
Attained 97.55% overall accuracy during a thunderstorm process.
Demonstrated the model's effectiveness in distinguishing diverse lightning types.

Conclusions:

The developed 1D-CNN model accurately classifies lightning waveforms.
This method significantly improves lightning observation and disaster investigation.
The model is suitable for integration into lightning sensors for real-time monitoring.