Near-source magnitude scaling of spectral accelerations: analysis and update of Kotha et al. (2020) model

Abstract

Ground-motion models (GMMs) are often used to predict the random distribution of Spectral accelerations ($\mathrm{SAs}$) at a site due to a nearby earthquake. In probabilistic seismic hazard and risk assessment, large earthquakes occurring close to a site are considered as critical scenarios. GMMs are expected to predict realistic $\mathrm{SAs}$ with low within-model uncertainty (${\upsigma }_{\upmu }$) for such rare scenarios. However, the datasets used to regress GMMs are usually deficient of data from critical scenarios. The (Kotha et al., A Regionally Adaptable Ground-Motion Model for Shallow Crustal Earthquakes in Europe Bulletin of Earthquake Engineering 18:4091–4125, 2020) GMM developed from the Engineering strong motion (ESM) dataset was found to predict decreasing short-period $\mathrm{SAs}$ with increasing ${M}_{W}\ge {\mathrm{M}}_{\mathrm{h}}=6.2$, and with large ${\upsigma }_{\upmu }$ at near-source distances $\le 30\mathrm{km}$. In this study, we updated the parametrisation of the GMM based on analyses of ESM and the Near source strong motion (NESS) datasets. With ${\mathrm{M}}_{\mathrm{h}}=5.7$, we could rectify the ${M}_{W}$ scaling issue, while also reducing ${\upsigma }_{\upmu }$ at ${M}_{W}\ge {\mathrm{M}}_{\mathrm{h}}$. We then evaluated the GMM against NESS data, and found that the $\mathrm{SAs}$ from a few large, thrust-faulting events in California, New Zealand, Japan, and Mexico are significantly higher than GMM median predictions. However, recordings from these events were mostly made on soft-soil geology, and contain anisotropic pulse-like effects. A more thorough non-ergodic treatment of NESS was not possible because most sites sampled unique events in very diverse tectonic environments. We provide an updated set of GMM coefficients,${\upsigma }_{\upmu }$, and heteroscedastic variance models; while also cautioning against its application for ${M}_{W}\le 4$ in low-moderate seismicity regions without evaluating the homogeneity of ${M}_{W}$ estimates between pan-European ESM and regional datasets.