A neural network for ground motion quality classification from New Zealand earthquakes of variable magnitudes and tectonic types (2020)
CitationDupuis M, Schill C, Lee R, Bradley B (2020). A neural network for ground motion quality classification from New Zealand earthquakes of variable magnitudes and tectonic types. Nelson, New Zealand: QuakeCoRE Annual Meeting. 07/12/2020.
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ANZSRC Fields of Research37 - Earth sciences::3706 - Geophysics::370609 - Seismology and seismic exploration
RightsAll rights reserved unless otherwise stated
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A neutal network for ground motion quality classification from New Zealand earthquakes of variable magnitudes and tectonic types Dupuis, Michael; Schill, Claudio; Lee, Robin; Bradley, Brendon (2020)High-quality ground motion records are required for engineering applications including response history analysis, seismic hazard development, and validation of physics-based ground motion simulations. However, the determination ...
Schill C; Thomson E; Lee R; Bradley, Brendon (2020)Probabilistic seismic hazard analysis (PSHA) based on physics-based simulations offers many advantages compared to more traditional empirical Ground Motion Model (GMM) based PHSA. However when a large number of seismic ...
Source modelling insights from ground motion simulation validation of moderate magnitude active shallow crustal earthquakes in New Zealand Lee R; Patterson J; Motha J; Graves R; Bradley, Brendon (2021)Hybrid broadband ground motion simulation validation has been an ongoing effort in NZ with recent studies focussed on active shallow crustal earthquakes of large magnitude (Mw>7.0) and small magnitude (3.5<Mw≤5.0). Lessons ...