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Identifying appropriate seismic events is the primary precondition for conducting meaningful analysis in seismological research. The successful creation of a method to automatically identify earthquakes from large amounts of data has become increasingly vital, especially with the construction of seismic stations, the collection of extensive seismic data, and the development of earthquake early warning (EEW) systems. To accurately identify seismic events, a combined model based on a generative adversarial network (GAN) and a support vector machine (SVM) is proposed to distinguish between earthquakes and microtremors. We first use 52,537 strong ground motion records from Japan to train a GAN and extract the characteristics of P waves and then use an SVM to discriminate seismic events in the testing set, thereby transforming the complex seismic event identification into a simpler binary classification of earthquakes and microtremors. The results illustrate that the combined model can achieve accuracies of 99.74% for P waves and 99.93% for microtremors, which represents an increase in accuracy of 14.13% compared with the traditional short-term averaging/long-term averaging (STA/LTA) method. Additionally, 98% of the local seismic events in the Great East Japan earthquake were identified. Therefore, the combined model has a wide range of applications in EEW and earthquake monitoring.

Seismic Event Identification Based on a Generative Adversarial Network and Support Vector Machine