Seismic severity estimation using convolutional neural network for earthquake early warning

In this study, magnitude estimation in earthquake early warning (EEW) systems is seen as a classification problem: the single-channel waveform, starting from the P-wave onset and lasting 4 s, is given in the input, and earthquake severity (medium and large earthquakes: local magnitude (M-L) = 5; small earthquakes: M-L < 5) is the classification result. The convolutional neural network (CNN) is proposed to estimate the severity of the earthquake, which is com-posed of several blocks that can extract the latent representation of the input from different receptive fields automatically. We train and test the proposed CNN model using two data sets. One is recorded by the China Earthquake Networks Center (CENC), and the other is the Stanford Earthquake Dataset (STEAD). Accordingly, the proposed CNN model achieves a test accuracy of 97.90 per cent. The proposed CNN model is applied to estimate two real-world earthquake swarms in China (the Changning earthquake and the Tangshan earthquake swarms) and the INSTANCE data set, and demonstrated the promising performance of generalization. In addition, the proposed CNN model has been connected to the CENC for further testing using real-world real-time seismic data.