Probabilistic Near‐Field Tsunami Source and Tsunami Run‐up Distribution Inferred From Tsunami Run‐up Records in Northern Chile
Probabilistic Near‐Field Tsunami Source and Tsunami Run‐up Distribution Inferred From Tsunami Run‐up Records in Northern Chile
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根据智利北部海啸涌动记录推断的概率近场海啸源和海啸涌动分布
DOI：
10.1029/2021jc017289
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Weiss, Robert
中科院分区：
文献类型：
--
作者：
Lee, Jun‐Whan;Irish, Jennifer L.;Weiss, Robert
了解海啸来源及其影响对于评估海啸危险至关重要。由于海啸调查小组的努力，目前已有针对当代事件的高质量海啸启动数据。尽管如此，它还没有被广泛用于推断海啸来源及其影响，这主要是由于海啸向前模型的计算负担。在这项研究中，我们提出了一个TRRF-INV(Tsunami Run-Up Response Function-Based Invertion)模型，它可以根据少量的启动记录提供近场海啸震源和海啸启动分布的概率估计。我们在智利北部的人工海啸情景中测试了TRRF-INV模型，并将其应用于2014年智利伊基克海啸事件。结果表明，TRRF-INV模型能够提供合理的一阶海啸震源估计，并能较好地估计海啸的启动分布。此外，实例研究结果与美国地质调查局报告和全球质心矩张量解吻合较好。我们还分析了TRRF-INV模型的性能依赖于启动记录的数量和不确定性。我们相信，TRRF-INV模型有潜力通过以下方式支持准确的危险评估：(1)从海啸启动记录中提供对海啸源及其影响的新见解，(2)将TRRF-INV模型用作支持现有海啸反演模型的工具，以及(3)在没有根据沉积物沉积数据估计的启动数据的情况下，估计海啸源及其对古代事件的影响。
Understanding a tsunami source and its impact is vital to assess a tsunami hazard. Thanks to the efforts of the tsunami survey teams, high‐quality tsunami run‐up data exist for contemporary events. Still, it has not been widely used to infer a tsunami source and its impact mainly due to the computational burden of the tsunami forward model. In this study, we propose a TRRF‐INV (Tsunami Run‐up Response Function‐based INVersion) model that can provide probabilistic estimates of a near‐field tsunami source and tsunami run‐up distribution from a small number of run‐up records. We tested the TRRF‐INV model with synthetic tsunami scenarios in northern Chile and applied it to the 2014 Iquique, Chile, tsunami event as a case study. The results demonstrated that the TRRF‐INV model can provide a reasonable tsunami source estimate to first order and estimate tsunami run‐up distribution well. Moreover, the case‐study results agree well with the United States Geological Survey report and the global Centroid Moment Tensor solution. We also analyzed the performance of the TRRF‐INV model depending on the number and the uncertainty of run‐up records. We believe that the TRRF‐INV model has the potential for supporting accurate hazard assessment by (1) providing new insights from tsunami run‐up records into the tsunami source and its impact, (2) using the TRRF‐INV model as a tool to support existing tsunami inversion models, and (3) estimating a tsunami source and its impact for ancient events where no data other than estimated run‐up from sediment deposit data exist.
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