Peak ground accelerations from large (M≥7.2) shallow crustal earthquakes: a comparison with predictions from eight recent ground-motion models

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Strong-motion data from large (M≥7.2) shallow crustal earthquakes invariably make up a small proportion of the records used to develop empirical ground motion prediction equations (GMPEs). Consequently GMPEs are more poorly constrained for large earthquakes than for small events. In this article peak ground accelerations (PGAs) observed in 38 earthquakes worldwide with M≥7.2 are compared with those predicted by eight recent GMPEs. Well over half of the 38 earthquakes were not considered when deriving these GMPEs but the data were identified by a thorough literature review of strong-motion reports from the past sixty years. These data are provided in an electronic supplement for future investigations on ground motions from large earthquakes. The addition of these data provides better constraint of the between-event ground-motion variability in large earthquakes. It is found that the eight models generally provide good predictions for PGAs from these earthquakes, although there is evidence for slight under- or over-prediction of motions by some models (particularly for M>7.5). The between-event variabilities predicted by most models match the observed variability, if data from two events (2001 Bhuj and 2005 Crescent City) that are likely atypical of earthquakes in active regions are excluded. For some GMPEs there is evidence that they are over-predicting PGAs in the near-source region of large earthquakes as well as over-predicting motions on hard rock. Overall, however, all the considered models, despite having been derived using limited data, provide reliable predictions of PGAs in the largest crustal earthquakes.
Original languageEnglish
Number of pages21
JournalBulletin of Earthquake Engineering
Early online date26 Jul 2017
StateE-pub ahead of print - 26 Jul 2017

    Research areas

  • large earthquakes, ground-motion prediction, peak ground acceleration, strong-motion data, residual analysis, between-event variability

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