Despite the potentially dramatic effect of the stress drop on groundmotion intensity, currently available earthquake early warning systems that deliver peak ground-motion predictions do not account for the effect of this parameter. To address this issue, a new evolutionary algorithm for determining stress drop and moment magnitude in real time is described. It consists of two distinct modules: one processes data recorded by individual stations and another computes event-average stress drops and moment magnitudes. To speed up the analysis, the real-time algorithm deviates from standard procedures of stress-drop determination in several ways. Because these time-saving measures come at the price of accuracy, a quality-control parameter is introduced, which quantifies the discrepancy between the observed and modeled ground motion. The results of implementing the algorithm offline using KiK-borehole data from Japan are presented. It is shown that it is possible to recover the moment magnitudes and the stress drops in real time. Two example timelines of seismic moment and stress drop are presented. These show that the source parameters of small-to-moderate earthquakes may be estimated quite accurately within 5 to 10 s since the first trigger, whereas those of larger magnitudes (i.e., Mw >6) take 20–30 s. Finally, ground-motion prediction equations for the velocity’s root mean square and peak ground velocity are presented. Once the epicenter, seismic moment, and stress drop are determined using a few stations nearest to the epicenter, their values can be input into those equations to get the ground-motion intensity at sites further away from it.
Bibliographical noteFunding Information:
We thank Associate Editor Ivan Wong for his assistance. We thank Adrien Oth and an anonymous reviewer for their insightful remarks. This research was supported by Grant Number 1081/14 from the Israel Science Foundation.
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