The accurate and reliable discrimination of earthquakes from background noise is a primary task of earthquake early warning (EEW); however, ubiquitous and complex microtremor signals substantially complicate this task. To mitigate this problem, a generative adversarial network (GAN) is adopted to distinguish between earthquakes and microtremors in this study. We train a GAN based on 52,537 K-NET and KiK-net strong ground motion records from Japan, and use the well-trained discriminator to identify 5373 P waves and 5373 microtremors in the testing set. The results indicate that this algorithm can correctly identify 99.89% of P waves and 99.24% of microtremors with high confidence. In addition, a verification of the proposed algorithm on data from the Great East Japan earthquake confirms that this model can achieve robust results for local records of large events and ultimately discriminate earthquakes from microtremors. This algorithm is an exploratory test of a GAN for identifying earthquake P waves. Though the GAN uses only P waves for training (There are no microtremors in the input data.), it has extensive potential in seismological and EEW applications.
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.
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