Data-driven adjustments for combined use of NGA-East hard-rock ground motion and site amplification models

Abstract

Model development in the Next Generation Attenuation-East (NGA-East) project included two components developed concurrently and independently: (1) earthquake ground-motion models (GMMs) that predict the median and aleatory variability of various intensity measures conditioned on magnitude and distance, derived for a reference hard-rock site condition with an average shear-wave velocity in the upper 30 m ( VS30) = 3000 m/s; and (2) a site amplification model that modifies intensity measures for softer site conditions. We investigate whether these models, when used in tandem, are compatible with ground-motion recordings in central and eastern North America (CENA) using an expanded version of the NGA-East database that includes new events from November 2011 (end date of NGA-East data curation) to April 2022. Following this expansion, the data set has 187 events, 2096 sites, and 16,272 three-component recordings, although the magnitude range remains limited (∼4 to 5.8). We compute residuals using 17 NGA-East GMMs and three data selection criteria that reflect within-CENA regional variations in ground-motion attributes. Mixed-effects regression of the residuals reveals a persistent pattern in which ground motions are overpredicted at short periods (0.01–0.6 s, including peak ground acceleration (PGA)) and underpredicted at longer periods. These misfits are regionally variable, with the Texas–Oklahoma–Kansas region having larger absolute misfits than other parts of CENA. Two factors potentially influencing these misfits are (1) differences in the site amplification models used to adjust the data to the reference condition during NGA-East GMM development relative to CENA amplification models applied since the 2018 National Seismic Hazard Model (NSHM), and (2) potential bias in simulation-based factors used to adjust ground motions from the hard-rock reference condition to a VS30 = 760 m/s condition. We provide adjustment factors and their epistemic uncertainties and discuss implications for applications.