A Model for Single-Station Standard Deviation Using Data from Various Tectonic Regions

Abstract

Correctly accounting for the uncertainty in ground-motion prediction is a critical component of probabilistic seismic-hazard analysis (PSHA). This prediction is commonly achieved using empirical ground-motion prediction equations. The differences between the observed and predicted ground-motion parameters are generally assumed to follow a normal distribution with a mean of zero and a standard deviation sigma. Recent work has focused on the development of partially nonergodic PSHA, where the repeatable effects of site response on ground-motion parameters are removed from their total standard deviation. The resulting value is known as single-station standard deviation or single-station sigma. If event-to-event variability is also removed from the single-station standard deviation, the resulting value is referred to as the event-corrected single-station standard deviation (phi(ss)). In this work, a large database of ground motions from multiple regions is used to obtain global estimates of these parameters. Results show that the event-corrected single-station standard deviation is remarkably stable across tectonic regions. Various models for this parameter are proposed accounting for potential magnitude and distance dependencies. The article also discusses requirements for using single-station standard deviation in a PSHA. These include the need for an independent estimate of the site term (e. g., the repeatable component of the ground-motion residual at a given station) and properly accounting for the epistemic uncertainty in both the site term and the site-specific single-station standard deviation. Values for the epistemic uncertainty on phi(ss) are proposed based on the station-to-station variability of this parameter.