Analysis of Single-Station Standard Deviation Using the KiK-net Data

Estimates of single-station standard deviation can be used as a lower bound to probabilistic seismic hazard analyses that remove the ergodic assumption on site response. This paper presents estimates of single-station standard deviation using data from the KiK-net network. The KiK-net network has a dense array of stations that recorded a large number of earthquakes over the period of study, both at the surface and at colocated borehole instruments. The large number of records implies that there are a large number of stations with recordings from multiple events; hence, site terms and single-station standard deviations can be properly estimated. Borehole instruments permit a breakdown of residuals, considering the effect of amplification in the shallow surface layers. Random-effects regression was first used to develop a ground-motion prediction equation (GMPE) using both the surface and borehole data. The GMPE was constrained such that event terms were the same at the surface and borehole. Residuals were then computed and the within-event (intraevent) residuals were separated into a repeatable site-term and a remaining residual, for both the ground motion itself and for the empirical amplification factor between surface and borehole. Results show that single-station standard deviations are considerably lower than standard deviations using the ergodic assumption, and these standard deviations are further reduced if only a small bracket of station-to-event azimuths is considered for each station such that path variability is minimized. Moreover, analyses of residuals indicate that most of the differences between ergodic standard deviations of surface and borehole data are the results of a poor parametrization of shallow site effects. However, the contribution of site-to-site variability in the empirical amplification factor is only limited. Finally, a comparison with results from other studies at different tectonic regions indicates that the values of single-station standard deviations are strikingly similar for all studies.