Rapid prediction of alongshore run-up distribution from near-field tsunamis
Rapid prediction of alongshore run-up distribution from near-field tsunamis
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DOI：
10.1007/s11069-020-04209-z
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发表时间：
2020-08
期刊：
影响因子：
3.7
通讯作者：
Jun‐Whan Lee;J. Irish;R. Weiss
中科院分区：
文献类型：
--
作者：
Jun‐Whan Lee;J. Irish;R. Weiss
快速预测近场海啸上升的空间分布对于海啸灾害特征描述至关重要。尽管在过去十年中取得了重大进展，但基于物理的数值模型仍然是计算密集型的。在这里，我们提出了一个响应面方法（RSM）为基础的模型称为海啸助跑响应函数（TRRF）。TRRF是从一组离散的海啸模拟数据中得出的，它可以对近场海啸的上升分布作出快速预测，其中考虑到特定区域可变的局部地形和水深特征的影响。这种新的方法减少了所需的模拟建立一个响应面模型，分别建模的领先的订单的贡献和海啸助跑分布的剩余部分。使用北方地区的波多黎各作为一个案例研究，我们调查的性能（精度，计算时间）的TRRF。结果表明，TRRF实现了可靠的预测，同时减少了6个数量级的预测时间（计算时间：s/地震）。
Rapid prediction of the spatial distribution of the run-up from near-field tsunamis is critically important for tsunami hazard characterization. Even though significant advances have been made over the last decade, physics-based numerical models are still computationally intensive. Here, we present a response surface methodology (RSM)-based model called the tsunami run-up response function (TRRF). Derived from a discrete set of tsunami simulations, TRRF can produce a rapid prediction of a near-field tsunami run-up distribution that takes into account the influence of variable local topographic and bathymetric characteristics in a given region. This new method reduces the number of simulations required to build an RSM model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Using the northern region of Puerto Rico as a case study, we investigated the performance (accuracy, computational time) of the TRRF. The results reveal that the TRRF achieves reliable prediction while reducing the prediction time by six orders of magnitude (computational time:s per earthquake).