Using Deep Learning for Rapid Earthquake Parameter Estimation in Single-Station Single-Component Earthquake Early Warning System

Earthquake early warning systems (EEWSs) often rely on fast determination of earthquake source parameters, namely, location, magnitude, and depth. In areas where the seismic network is coarse, the capability to determine source parameters based on data recorded by a single station is desirable. Moreover, being able to use a single component of the seismic data might increase the robustness of the system to sensor malfunction and might save on sensor cost and computation time. Here, we propose a hybrid deep learning (DL) model to estimate source parameters based on single-component data recorded by a single station at 3 s after the P-wave onset. The model, which we call EEWS-311, uses a convolutional neural network (CNN) and bidirectional long short-term memory. It is trained and tested on recordings of more than 14000 events by a single station of the Japanese Hi-net high-sensitivity short-period seismic network. Compared with source parameters obtained by conventional methods, our model achieves excellent performance (average errors in latitude, longitude, magnitude, and depth equal to 0.05 degrees, 0.1 degrees, 0.14 velocity magnitude (Mv), and 5.68 km, respectively). The results demonstrate the suitability of EEWS-311 for earthquake early warning in areas with sufficient training data.