Earthquake magnitude classification based on deep learning

In order to explore the magnitude information of the seismic acceleration time history recordings, we train a convolutional neural network to classify the seismic recordings based on the magnitude of the earthquakes. Nearly 120 000 earthquake recordings in K-NET and KiK-net are used as samples, and these acceleration time history recordings are used as inputs for model training after information screening and normalization. Taking the magnitude M5.5 as the classification standard, we train a deep learning model of convolutional neural network to classify large and small earthquakes. The results show that the model has an accuracy rate of 93.6% on the training set and 92.3% on the test set, which has a good classification effect. This suggests there are some fundamental differences between large earthquake recordings and small ones. Thus, earthquake magnitude information may be revealed from acceleration time history recordings of earthquakes.