The research of earthquake magnitude determination based on Convolutional Neural Networks

The magnitude determination of earthquake is one of the most important and challenging parts in Early Earthquake Warning (EEW) system. In this paper, the method of determination of earthquake magnitude based on convolutional neural network (CNN) is proposed. This method transforms magnitude determination problem into a classification problem, by dividing earthquake magnitudes into 20 different categories which are greater than 2. 0 (ML>2. 0). In this paper, a total of 1928 earthquakes in Fujian, Taiwan Strait and Taiwan area recorded by Fujian Seismic Network from 2012 to 2019 were collected as research data; 14644 three component seismic records were obtained by station record interception, data tagging and quality screening along with other pre-processing procedures. A convolutional neural network (CNN) model for magnitude prediction was constructed by inserting three-second data records. The model was trained with the earthquake events from 2012 2018 and tested with the earthquake events in 2019. The results showed that 85. 6% of the magnitude deviation of a single station can be controlled within 0.3, and 91. 8% of the average magnitude deviation of the first three stations can be controlled within 0. 3. Those cases with relatively large deviation are mainly due to the lack of historical samples. Compared with the traditional methods, the magnitude determined by the CNN model is more stable and reliable, which can provide a new technical method for solving the challenging problem of EEW magnitude determination.