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The document discusses the development of an automated framework for accurately classifying earthquake precursor signals, focusing on distinguishing ramping patterns from non-ramping signals using a combination of traditional machine learning and deep learning techniques. A Convolutional Neural Network (CNN) with an attention mechanism achieved a classification accuracy of 99.81%, demonstrating improved performance in real-time P-wave detection. The proposed system is designed to handle large datasets effic

Author

bcelluloid

Language

EN