背景(考古学)
危害
计算机科学
地震动
计算树逻辑
变量(数学)
过程(计算)
地震灾害
树(集合论)
运动(物理)
数据挖掘
算法
人工智能
地质学
数学
地震学
模型检查
古生物学
数学分析
化学
有机化学
操作系统
作者
Brendon Bradley,Sanjay Singh Bora,Robin Lee,Elena Florinela Manea,Matthew C. Gerstenberger,Peter J. Stafford,Gail M. Atkinson,Graeme Weatherill,Jesse Hutchinson,Christopher A de la Torre,Anne M. Hulsey,Anna Kaiser
摘要
ABSTRACT This article summarizes the ground-motion characterization (GMC) model component of the 2022 New Zealand National Seismic Hazard Model (2022 NZ NSHM). The model development process included establishing a NZ-specific context through the creation of a new ground-motion database, and consideration of alternative ground-motion models (GMMs) that have been historically used in NZ or have been recently developed for global application with or without NZ-specific regionalizations. Explicit attention was given to models employing state-of-the-art approaches in terms of their ability to provide robust predictions when extrapolated beyond the predictor variable scenarios that are well constrained by empirical data alone. We adopted a “hybrid” logic tree that combined both a “weights-on-models” approach along with backbone models (i.e., metamodels), the former being the conventional approach to GMC logic tree modeling for NSHM applications using published models, and the latter being increasingly used in research literature and site-specific studies. In this vein, two NZ-specific GMMs were developed employing the backbone model construct. All of the adopted subduction GMMs in the logic tree were further modified from their published versions to include the effects of increased attenuation in the back-arc region; and, all but one model was modified to account for the reduction in ground-motion standard deviations as a result of nonlinear surficial site response. As well as being based on theoretical arguments, these adjustments were implemented as a result of hazard sensitivity analyses using models without these effects, which we consider gave unrealistically high hazard estimates.
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