A Source Clustering Approach for Efficient Inundation Modeling and Regional Scale PTHA
A Source Clustering Approach for Efficient Inundation Modeling and Regional Scale PTHA
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用于高效洪水建模和区域尺度 PTHA 的源聚类方法
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发表时间：
2020
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通讯作者：
F. González
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作者：
A. Williamson;Donsub Rim;L. Adams;R. LeVeque;D. Melgar;F. González
对于俯冲带边缘的沿海地区，近场巨型逆冲断层地震是最极端的海啸灾害的来源，并且作为任何可能的海啸灾害评估（PTHA）的一个方面进行适当处理非常重要。通常，由于断层面上滑动的范围和模式的极端变化，任何一点的淹没深度都可能存在很大的变化。在这种情况下，我们提出了一种方法来估计淹没深度概率（在一组沿海位置的危险曲线的形式），由两个组成部分。第一部分使用Karhunen-Loève展开来表达所有可能事件的概率密度函数（PDF），PDF参数在卡斯卡迪亚俯冲带（CSZ）中是合理的。然后，从这个PDF中生成大量N个样本是很容易的，计算成本也很低;这样做并对每个样本进行完整的海啸淹没模拟，提供了一种估算淹没概率的蛮力方法。然而，为了获得合理的结果，特别是对于由于罕见事件而导致的极端洪水，N必须如此之大，以至于海啸模拟的成本过高。第二个组成部分通过使用重要性抽样技术对分布的尾部进行充分抽样并适当地重新加权分配给所产生的实现的概率，并将实现分组为我们认为将在感兴趣的区域中给出类似淹没模式的少量集群来解决这个困难。在这种方法中，只有一个细网格海啸模拟需要从每个集群的代表性成员计算。我们讨论集群的基础上，代理数量是便宜的计算大量的实现，但可以确定一个较小数量的集群的实现，将有类似的淹没深度。每个集群代表的细网格模拟也可以用于开发改进的策略，其中这些与集群其他成员的更便宜的粗网格模拟相结合。我们通过考虑两个沿海地区来说明这种方法：加利福尼亚州的新月市和华盛顿州的韦斯特波特。关键词。概率海啸灾害评估，聚类，随机地震，Karhunen-Loève展开，GeoClaw
For coastal regions on the margin of a subduction zone, near-field megathrust earthquakes are the source of the most extreme tsunami hazards, and are important to handle properly as one aspect of any Probabilistic Tsunami Hazard Assessment (PTHA). Typically, great variability in inundation depth at any point is possible due to the extreme variation in extent and pattern of slip over the fault surface. In this context, we present an approach to estimating inundation depth probabilities (in the form of hazard curves at a set of coastal locations) that consists of two components. The first component uses a Karhunen-Loève expansion to express the probability density function (PDF) for all possible events, with PDF parameters that are geophysically reasonable for the Cascadia Subduction Zone (CSZ). It is then easy and computationally cheap to generate a large N number of samples from this PDF; doing so and performing a full tsunami inundation simulation for each provides a brute force approach to estimating probabilities of inundation. However, to obtain reasonable results, particularly for extreme flooding due to rare events, N would have to be so large as to make the tsunami simulations prohibitively expensive. The second component tackles this difficulty by using importance sampling techniques to adequately sample the tails of the distribution and properly re-weight the probability assigned to the resulting realizations, and by grouping the realizations into a small number of clusters that we believe will give similar inundation patterns in the region of interest. In this approach, only one fine-grid tsunami simulation need be computed from a representative member of each cluster. We discuss clustering based on proxy quantities that are cheap to compute over a large number of realizations, but that can identify a smaller number of clusters of realizations that will have similar inundation depths. The fine-grid simulations for each cluster representative can also be used to develop an improved strategy, in which these are combined with cheaper coarse-grid simulations of other members of the cluster. We illustrate the methodology by considering two coastal locations: Crescent City, CA and Westport, WA. Key word. probabilistic tsunami hazard assessment, clustering, stochastic earthquakes, Karhunen-Loève expansion, GeoClaw