Bayesian analysis of ground motion models using chimney fragility curves: 2021, 5.9-Mw Woods Point intraplate earthquake, Victoria, Australia

James La Greca, Mark Quigley, Jaroslav Vaculik, Trevor Allen, Peter Rayner

The 22 September 2021 (AEST) Mw 5.9 Woods Point earthquake occurred in an intraplate setting (southeast Australia) approximately 130 km East Northeast of the central business district of Melbourne (pop. ∼5.15 million). A lack of seismic instrumentation and a low population density in the epicentral region resulted in a dearth of near-source instrumental and “felt” report intensity data, limiting evaluation of the near-source performance of ground motion models (GMMs). To address this challenge, we first surveyed unreinforced masonry chimneys following the earthquake to establish damage states and develop fragility curves. Using Bayesian inference, and including pre-earthquake GMM weightings as Bayesian priors, we evaluate the relative performance of GMMs in predicting chimney observations for different fragility functions and seismic velocity profiles. At the most likely VS30 (760 m/s), the best performing models are AB06, A12, and CY08SWISS. GMMs that were preferentially selected for utility in the Australian National Seismic Hazard Model (NSHA18) prior to the Woods Point earthquake outperform other GMMs. The recently developed NGA-East GMM performs relatively well in the more distal region (e.g. >50 km) but is among the poorest performing GMMs in the near-source region across the range of VS30. Our new method of combining analysis of engineered features (chimneys) with Bayesian inference to evaluate the near-source performance of GMMs may have applicability in diverse settings worldwide, particularly in areas of sparse seismic instrumentation.