Estimating Earthquake Magnitude using Simple Deep Learning Methods

Rapid and precise magnitude estimation is critical for effective earthquake early warning systems and other seismic applications. This study introduces a novel deep learning-based approach for magnitude estimation that leverages extracted features from earthquake signals. The proposed model offers several advantages, including computational efficiency, adaptability to various feature sets, and the ability to operate on resource-constrained devices. Trained and evaluated on a substantial dataset, the model demonstrates robust performance across a wide range of earthquake magnitudes, making it a promising candidate for integration into seismic applications.