A Bayesian Lasso Logistic Regression Model for Predicting the Probability of Regional Seismic Phase Observation UsingSnin the Middle East and East Asia as Examples

Abstract

ABSTRACTHigh-frequency seismic wave blockage is often the result of strong attenuation, and the regional phase Sn is particularly prone to blockage in comparison with any of the other regional phases including Lg. As widespread blockage can lead to difficulty in the estimation of source parameters or path attenuation, accurate characterization of efficient regional wave propagation is necessary. We have applied two approaches to map Sn phase blockage: (1) the relatively standardized efficiency tomography and (2) a newly developed Bayesian logistic regression model that is able to predict the likelihood (probability) of phase blockage. As a byproduct of our Bayesian approach, we obtain measures of uncertainty for the probability of blockage. We applied both our methods on simulated efficiency data as well as real efficiency data obtained from earthquakes and stations from the middle east and eastern Asia. Our models successfully predict the probability of blockage zones with relatively high accuracy (>75%). In addition, we observe both low probability of Sn blockage and efficient Sn propagation in tectonically stable continental lithosphere, such as the Arabian plate, the Mediterranean Sea, northeastern Iran, the Ordos plateau, and the Sichuan basin. Regions with a high probability of Sn blockage or inefficient Sn propagation zones are in the tectonically active areas, such as the Tibetan and Iranian plateaus. Our probability of blockage model can also be used to image the regions where SnQ models are likely to be biased due to blocked data.