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Rapid Estimation of Earthquake Source and Ground-Motion Parameters for Earthquake Early Warning Using Data from a Single Three- Component Broadband or Strong-Motion Sensor

We propose a new algorithm to rapidly determine earthquake source and 
ground-motion parameters for earthquake early warning (EEW). This algorithm uses 
the acceleration, velocity, and displacement waveforms of a single three-component 
broadband (BB) or strong-motion (SM) sensor to perform real-time earthquake/noise 
discrimination and near/far source classification. When an earthquake is detected, the 
algorithm estimates the moment magnitude M, epicentral distance Δ, and peak 
ground velocity (PGV) at the site of observation. The algorithm was constructed 
by using an artificial neural network (ANN) approach. Our training and test datasets 
consist of 2431 three-component SM and BB records of 161 crustal earthquakes in 
California, Japan, and Taiwan with 3.1 ≤ M ≤ 7.6 at Δ ≤ 115 km. First estimates become 
available at t_0 = 0.25 s after the P pick and are regularly updated. We find that 
displacement and velocity waveforms are most relevant for the estimation of M and 
PGV, while acceleration is important for earthquake/noise discrimination. Including 
site corrections reduces the errors up to 10%. The estimates improve by an additional 
10% if we use both the vertical and horizontal components of recorded ground 
motions. The uncertainties of the predicted parameters decrease with increasing time 
window length t_0; larger magnitude events show a slower decay of these uncertainties 
than small earthquakes. We compare our approach with the τ_c algorithm and find that 
our prediction errors are around 60% smaller. However, in general there is a limitation 
to the prediction accuracy an EEW system can provide if based on single-sensor 
observations.