Abstract
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 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 sec after the first P-wave detection, the method
converges to stable and accurate determinations of epicentral
coordinates and depth even with a non-optimal coverage of stations. The
proposed methodology can be generalized and adapted to the off-line
analysis of seismic records collected by standard local networks.