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 t0 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 t0; 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.