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Empirical nonergodic shaking scenarios based on spatial correlation models: An application to central Italy

机译：基于空间相关模型的经验非精通摇动场景：意大利中部的应用

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This paper provides a new methodological framework to generate empirical ground shaking scenarios, designed for engineering applications and civil protection planning. The methodology is useful both to reconstruct the ground motion pattern of past events and to generate future shaking scenarios, in regions where strong-motion datasets from multiple events and multiple stations are available. The proposed methodology combines (1) an ad-hoc nonergodic ground motion model (GMM) with (2) a spatial correlation model for the source region-, site-, and path-systematic residual terms, and (3) a model of the remaining aleatory error to take into account for directivity effects. The associated variability is a function of the type of scenario generated (bedrock or site, past or future event) and it is minimal for source areas where several events have occurred and for sites where recordings are available. In order to develop the region-specific fully nonergodic GMM and to compute robust estimation of the residual terms, the approach is calibrated on a highly dense dataset compiled for the area of central Italy. Example tests demonstrate the validity of the approach, which allows to simulate acceleration response spectra at unsampled sites, as well as to capture peculiar physical features of ground motion patterns in the region. The proposed approach could be usefully adopted for data-driven simulations of ground shaking maps, as alternative or complementary tool to physic-based and stochastic-based approaches.
机译：本文提供了一种新的方法论框架，用于产生专为工程应用和民用保护规划而设计的经验地面摇动场景。该方法是有用的，都可以重建过去事件的地面运动模式并在来自多个事件和多个站的强运动数据集可用的区域中重建过去事件的地面运动模式并生成未来的摇动场景。所提出的方法与（1）组合（1）ad-hoc非精通地面运动模型（GMM），其中（2）用于源区，站点和路径系统残留术语的空间相关模型，以及（3）的模型剩余的aleagory错误考虑方向性效果。相关的可变性是生成的场景类型（基岩或站点，过去或未来事件）的函数，对于源区域而言，它是最小的几个事件以及录制的网站。为了开发特异性特异性的完全非原始GMM并计算残余术语的稳健估计，该方法在为意大利中部地区编制的高度密度数据集上校准。示例测试证明了方法的有效性，其允许模拟未夹杂地点的加速度响应光谱，以及捕获该区域中地面运动模式的特殊物理特征。可以使用该方法用于地面摇动地图的数据驱动模拟，作为物理和基于随机的方法的替代或互补工具。

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