MESCNN: Magnitude estimation system based on convolutional neural networks

Magnitude is a critical parameter in earthquake early warning systems, directly influencing alert issuance and warning levels. In this study, we introduce the Magnitude Estimation System based on Convolutional Neural Networks (MESCNN), a novel approach built upon the Python programming language and the TensorFlow deep learning framework. MESCNN automates the calculation of earthquake magnitudes using real-time seismic data, leveraging the capabilities of convolutional neural networks (CNN) to analyze seismic waveforms. The system is designed to enhance the accuracy and efficiency of magnitude estimation, thereby enabling more timely and reliable earthquake warnings to reduce the impact of seismic events.