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Research papers, University of Canterbury Library

This study investigates the uncertainty of simulated earthquake ground motions for smallmagnitude events (Mw 3.5 – 5) in Canterbury, New Zealand. 148 events were simulated with specified uncertainties in: event magnitude, hypocentre location, focal mechanism, high frequency rupture velocity, Brune stress parameter, the site 30-m time-averaged shear wave velocity (Vs30), anelastic attenuation (Q) and high frequency path duration. In order to capture these uncertainties, 25 realisations for each event were generated using the Graves and Pitarka (2015) hybrid broadband simulation approach. Monte-Carlo realisations were drawn from distributions for each uncertainty, to generate a suite of simulation realisations for each event and site. The fit of the multiple simulation realisations to observations were assessed using linear mixed effects regression to generate the systematic source, path and site effects components across all ground motion intensity measure residuals. Findings show that additional uncertainties are required in each of the three source, path, and site components, however the level of output uncertainty is promising considering the input uncertainties included.

Research papers, University of Canterbury Library

In the last two decades, New Zealand (NZ) has experienced significant earthquakes, including the 2010 M 7.2 Darfield, 2011 M 6.2 Christchurch, and 2016 M 7.8 Kaikōura events. Amongst these large events, tens of thousands of smaller earthquakes have occurred. While previous event and ground-motion databases have analyzed these events, many events below M 4 have gone undetected. The goal of this study is to expand on previous databases, particularly for small magnitude (M<4) and low-amplitude ground motions. This new database enables a greater understanding of regional variations within NZ and contributes to the validity of internationally developed ground-motion models. The database includes event locations and magnitude estimates with uncertainty considerations, and tectonic type assessed in a hierarchical manner. Ground motions are extracted from the GeoNet FDSN server and assessed for quality using a neural network classification approach. A deep neural network approach is also utilized for picking P and S phases for determination of event hypocentres. Relative hypocentres are further improved by double-difference relocation and will contribute toward developing shallow (< 50 km) seismic tomography models. Analysis of the resulting database is compared with previous studies for discussion of implications toward national hazard prediction models.