Yinzhe Zhang

Earthquake early warning (EEW) systems can give people warnings before damaging ground motions reach their site and can reduce earthquake-related losses. It has been shown that Bayesian reference can be used to estimate earthquake magnitude, location, and the distribution of peak ground motion using observed ground-motion amplitudes, predefined prior information, and appropriate attenuation relationships. In this article, we describe a Bayesian approach to earthquake early warning (EEW) systems for the estimation of magnitude (M) and peak ground-motion velocity (PGV) using the Gutenberg–Richter relation as a priori probability derived from statistical historical earthquake information from the Japanese KiK-net. Moreover, using the magnitude M_j = 4.5 and PGV = 0.4 cm/s as the warning threshold, an earthquake hazard discrimination model was established. Following this, the proposed approach was compared with the traditional fitting method to analyze the earthquake hazard discrimination using an M_j = 4.3 earthquake in the Mt. Fuji area and an M_j = 5.5 earthquake in the Ibaraki area of Japan as examples. The results are as follows: (1) The probabilistic algorithm founded on the predictive model of the magnitude from the average period (τ_c) and PGV from the displacement amplitude (P_d) from the initial 3 s of the P wave is able to provide a fast and accurate estimation of the final magnitude and location. (2) The Bayesian inference approach for the earthquake hazard discrimination model has a 2.94% miss rate and a 5.88% false alarm rate, which is lower than that of the traditional fitting method, thus increasing the accuracy and reliability of earthquake hazard estimation.