Real-time Performance of the Virtual Seismologist Earthquake Early Warning Algorithm in Southern California

Abstract

The Virtual Seismologist (VS) method is a Bayesian approach to regional network-based earthquake early warning (EEW) that estimates earthquake magnitude, location, and the distribution of peak ground motion using observed ground motion amplitudes, predefined prior information, and appropriate attenuation relationships (Cua 2005; Cua and Heaton 2007). The application of Bayes's theorem in earthquake early warning (Cua 2005) states that the most probable source estimate at any given time is a combination of contributions from prior information (possibilities include network topology or station health status, regional hazard maps, earthquake forecasts, the Gutenberg-Richter magnitude-frequency relationship) and a likelihood function, which takes into account observations from the ongoing earthquake. Prior information can be considered relatively static over the timescale of a given earthquake rupture. The changes in the source estimates and predicted peak ground motion distribution, which are updated each second, are due to changes in the likelihood function as additional arrival and amplitude data become available. The potential use of prior information differentiates the VS approach from other regional, network-based EEW algorithms, such as ElarmS (Allen and Kanamori 2003).