Rapid Earthquake Magnitude Classification Using Single Station Data Based on the Machine Learning

Magnitude is one of the fundamental parameters of earthquake and also one of the essential information for earthquake early warning (EEW). Rapidly and accurately determining the high-or low-magnitude event is important for mitigating the damage of earthquake hazard. To address the problem of predicting whether an earthquake event is high magnitude (
$M \ge5.5$
) or low magnitude (
$M < 5.5$
), this letter proposes a machine learning magnitude classification framework (MCFrame) using single station data, which consists of feature extraction module and magnitude classifier, and we train the feature extraction module of MCFrame to learn the characteristics of high-magnitude (
$M \ge5.5$
) and low-magnitude (
$M < 5.5$
) earthquake events, using strong-motion records collected from the Japanese Kyoshin network (K-NET) seismic network. Then, the extracted features are used as an input of magnitude classifier to classify earthquake magnitudes. Meanwhile, we analyze the impact of three different magnitude classifiers on the performance of MCFrame, which include the deep neural network (DNN), support vector machine (SVM), and random forest (RF). We show that within 5 s after the P-wave arrival, for these three different classifiers, the magnitude classification accuracy of MCFrame is close, and the MCFrame proposed in this work has a better performance than baseline models for magnitude classification. Additionally, for the MCFrame, the accuracy of high-magnitude events (
$M \ge5.5$
) is more than 90%, and the accuracy of low-magnitude events (
$M < 5.5$
) is more than 99%. These results indicate that the MCFrame proposed in this work is significant for EEW.