EMC: Earthquake Magnitudes Classification on Seismic Signals via Convolutional Recurrent Networks

Abstract

We propose a novel framework for reliable automatic classification of earthquake magnitudes. Using deep learning methods, we aim to classify the earthquake magnitudes into different categories. The method is based on a convolutional recurrent neural network in which a new approach for feature extraction using Log-Mel spectrogram representations is applied for seismic signals. The neural network is able to classify earthquake magnitudes from minor to major. Stanford Earthquake Dataset (STEAD) is used to train and validate the proposed method. The evaluation results demonstrate the efficacy of the proposed method in a rigorous event independent scenario, which can reach a F-score of 67% depending upon the earthquake magnitude.