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Free keywords:
Ground-motion model · Spectral accelerations · Magnitude scaling · Nearsource
saturation · Within-model uncertainty · Heteroscedastic variability
Abstract:
Ground-motion models (GMMs) are often used to predict the random distribution of Spectral
accelerations ( 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 SAs with low within-model uncertainty
( σμ ) 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 SAs with increasing M
W ≥ Mh = 6.2 ,
and with large σμ at near-source distances ≤ 30km . In this study, we updated the parametrisation
of the GMM based on analyses of ESM and the Near source strong motion (NESS)
datasets. With Mh = 5.7 , we could rectify the M
W scaling issue, while also reducing σμ at
M
W ≥ Mh . We then evaluated the GMM against NESS data, and found that the 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,
σμ , and heteroscedastic variance models; while also cautioning against its application
for M
W ≤ 4 in low-moderate seismicity regions without evaluating the homogeneity of
M
W estimates between pan-European ESM and regional datasets.
