The backbone modeling in ground-motion characterization (GMC) is a useful methodology to describe the epistemic uncertainty in median ground-motion predictions. The approach uses a backbone ground-motion model (GMM) and populates the GMC logic tree with the scaled and/or adjusted versions of the backbone GMM to capture the epistemic uncertainty in median ground motions. The scaling and/or adjustment should represent the specific features and uncertainties involved in source, path, and site effects at the target site. The identification of the backbone model requires different considerations specific to the nature of the ground-motion hazard problem. In this article, we present a scaled backbone modeling approach that considers the magnitude- and distance-scaling predictors as well as their correlation to address the epistemic uncertainty in median ground-motion predictions. This approach results in a trivariate normal distribution to fully define a range of epistemic uncertainty in a model sample space. The simultaneous consideration of magnitude and distance scaling while defining the epistemic uncertainty and the methodology followed for the simplified representation of trivariate normal distribution in ground-motion logic tree are the two important features in our procedure. We first present the proposed approach that is followed by a case study for Central and Eastern North America (CENA) stable continental region. The case study discusses the underlying assumptions and limitations of the proposed approach.

地面运动表征 (GMC) 中的主干建模是描述中值地面运动预测中的认知不确定性的有用方法。该方法使用主干地面运动模型 (GMM) 并使用主干 GMM 的缩放和/或调整版本填充 GMC 逻辑树，以捕获中值地面运动的认知不确定性。缩放和/或调整应代表目标站点的源、路径和站点效应所涉及的特定特征和不确定性。主干模型的识别需要针对地面运动危险问题的性质进行不同的考虑。在本文中，我们提出了一种缩放主干建模方法，该方法考虑了幅度和距离缩放预测因子以及它们的相关性，以解决中值地面运动预测中的认知不确定性。这种方法导致三变量正态分布以完全定义模型样本空间中的认知不确定性范围。在定义认知不确定性时同时考虑幅度和距离缩放，以及地面运动逻辑树中三变量正态分布的简化表示所遵循的方法是我们程序中的两个重要特征。我们首先介绍了所提出的方法，然后是北美中部和东部 (CENA) 稳定大陆地区的案例研究。