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Using Deep Convolutional Neural Networks for Earthquake and Explosion Classification
Accurate discrimination between natural earthquakes and artificial explosions is critical for seismic monitoring, disaster management, and national security. Traditional seismic analysis techniques rely heavily on handcrafted features and expert interpretation, which can be time-consuming and error-prone. With the advancement of deep learning, automated seismic signal classification has gained significant attention. This project proposes a Deep Convolutional Neural Network (DCNN)-based approach to classify seismic events as earthquakes or explosions. Seismic waveform data are transformed into time-frequency representations such as spectrograms for effective feature learning. The CNN model automatically extracts discriminative spatial and temporal features from seismic signals. Data preprocessing enhances signal quality and reduces noise interference. The trained model achieves high classification accuracy without manual feature engineering. Performance is evaluated using accuracy, precision, recall, and F1-score metrics. The system supports rapid and reliable seismic event classification. Explainability tools provide insight into model decisions. The proposed framework improves detection reliability in real-time seismic monitoring systems. This approach enhances both scientific research and security surveillance. Overall, the system offers a scalable and intelligent solution for seismic event classification.
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