Abstract
With increasing amount of strong motion data, Ground Motion Prediction Equation (GMPE) developers are able to quantify empirical site amplification functions (ΔS2Ss) from GMPE residuals, for use in site-specific Probabilistic Seismic Hazard Assessment. In this study, we first derive a GMPE for 5% damped Pseudo Spectral Acceleration (g) of Active Shallow Crustal earthquakes in Japan with 3.4≤Mw≤7.3 and 0≤RJB<600km. Using k-mean spectral clustering technique, we then classify our estimated ΔS2Ss (T=0.01–2 s) of 588 well-characterized sites, into 8 site clusters with distinct mean site amplification functions, and within-cluster site-to-site variability ~ 50% smaller than the overall dataset variability (ϕS2S). Following an evaluation of existing schemes, we propose a revised data-driven site classification characterized by kernel density distributions of Vs30, Vs10, H800, and predominant period (TG) of the site clusters
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