Study on Multi-Station Identification Technology of Lightning Electromagnetic Pulses (LEMPs) Based on Deep Learning.

Area of Science:

• Atmospheric physics
• Electrical engineering
• Computer science

Background:

• Lightning electromagnetic pulses (LEMP) pose increasing threats to electronic systems and human safety.
• Accurate identification and classification of LEMP are crucial for effective mitigation strategies.

Purpose of the Study:

• To develop and validate a deep learning model for improved LEMP identification and classification.
• To enhance the accuracy and robustness of lightning monitoring systems.

Main Methods:

• Collected a dataset of LEMP signals from various lightning types and environmental conditions using a multi-station lightning location system.
• Developed a deep learning model integrating a convolutional neural network for feature extraction and pattern recognition.
• Utilized multi-station data for enhanced signal analysis.

Main Results:

• The proposed deep learning model achieved over 97% accuracy in LEMP identification, significantly outperforming single-station methods.
• The model effectively uncovered complex hidden features within the LEMP data.
• Demonstrated superior accuracy and robustness compared to conventional approaches.

Conclusions:

• The developed deep learning model provides a reliable technical foundation for advanced lightning monitoring and localization.
• This approach enhances the understanding and prediction of lightning-related electromagnetic phenomena.
• Offers improved protection for critical infrastructure and human activities against lightning hazards.