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Analysis of macroseismic fields using statistical data depth functions: considerations leading to attenuation probabilistic modelling

机译:使用统计数据深度函数分析宏观地震场:导致衰减概率建模的考虑因素

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Modelling seismic attenuation is one of the most critical points in the hazard assessment process. In this article we consider the spatial distribution of the effects caused by an earthquake as expressed by the values of the macroseismic intensity recorded at various locations surrounding the epicentre. Considering the ordinal nature of the intensity, a way to show its decay with distance is to draw curves-isoseismal lines-on maps, which bound points of intensity not smaller than a fixed value. These lines usually take the form of closed and nested curves around the epicentre, with highly different shapes because of the effects of ground conditions and of complexities in rupture propagation. Forecasting seismic attenuation of future earthquakes requires stochastic modelling of the decay on the basis of a common spatial pattern. The aim of this study is to consider a statistical methodology that identifies a general shape, if it exists, for isoseismal lines of a set of macroseismic fields. Data depth is a general nonparametric method for analysis of probability distributions and datasets. It has arisen as a statistical method to order points of a multivariate space, e.g., Euclidean space , , according to the centrality with respect to a distribution or a given data cloud. Recently, this method has been extended to the ordering of functions and trajectories. In our case, for a fixed intensity decay , we build a set of convex hulls that enclose the sites of felt intensity , one for each macroseismic field of a set of earthquakes that are considered as similar from the attenuation point of view. By applying data depth functions to this functional dataset, it is possible to identify the most central curve, i.e., the attenuation pattern, and to consider other properties like variability, outlyingness, and possible clustering of such curves. Results are shown for earthquakes that occurred on the Central Po Plain in May 2012, and on the eastern flank of Mt. Etna since 1865.
机译:对地震衰减建模是危害评估过程中最关键的方面之一。在本文中,我们考虑了地震造成的影响的空间分布,该分布由震中周围各个位置记录的宏观地震烈度值表示。考虑到强度的序数性质,显示强度随距离衰减的一种方法是在映射图上绘制曲线-等震线,这些图的强度点绑定不小于固定值。这些线通常采取围绕震中的闭合和嵌套曲线的形式,由于地面条件的影响以及破裂传播的复杂性,形状具有截然不同的形状。预测未来地震的地震衰减需要根据共同的空间模式对衰减进行随机建模。这项研究的目的是考虑一种统计方法,该方法可以识别一组宏观地震场的等震线的一般形状(如果存在)。数据深度是一种用于概率分布和数据集分析的通用非参数方法。作为一种统计方法,已经出现了根据相对于分布或给定数据云的中心性来对多元空间(例如,欧几里得空间)的点进行排序的方法。最近,该方法已扩展到函数和轨迹的排序。在我们的情况下,对于固定的强度衰减,我们构建了一组包围感觉强度位置的凸包,对于一组地震的每个大地震场,从衰减的角度看,它们相似。通过将数据深度函数应用于此功能数据集,可以识别最中心的曲线(即衰减模式),并考虑其他属性,例如变异性,离群性以及此类曲线的可能聚类。显示了2012年5月在中埔平原和山东翼发生的地震的结果。自1865年以来的埃特纳火山。

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