A Bayesian Method for Real-Time Earthquake Location Using Multiparameter Data

A primary task of a network-based, earthquake early warning system is the prompt event detection and location, needed to assess the magnitude of the event and its potential damage through the predicted peak ground shaking amplitude using empirical attenuation relationships. Most of real-time, automatic earthquake location methods ground on the progressive measurement of the first P-wave arrival time at stations located at increasing distances from the source but recent approaches showed the feasibility to improve the accuracy and rapidity of the earthquake location by using the additional information carried by the P-wave polarization or amplitude, especially with unfavorable seismic network lay-outs. Here, we propose an evolutionary, Bayesian method for the real-time earthquake location which combines the information derived from the differential P-wave arrival times, amplitude ratios and back-azimuths measured at a minimum of two stations. As more distant stations record the P-wave, the posterior pdf is updated and new earthquake location parameters are determined along with their uncertainty. To validate the location method, we performed a retrospective analysis of mainshocks (M > 4.5) occurred during the 2016–2017 Central Italy earthquake sequence by simulating the typical acquisition layouts of in-land, coastal and linear array of stations. Results show that with the combined use of the three parameters, 2–4 s after the first P-wave detection, the method converges to stable and accurate determinations of epicentral coordinates and depth even with a nonoptimal coverage of stations. The proposed methodology can be generalized and adapted to the off-line analysis of seismic records collected by standard local networks