A Study of the 2015 Mw 8.3 Illapel Earthquake and Tsunami: Numerical and Analytical Approaches
A Study of the 2015 Mw 8.3 Illapel Earthquake and Tsunami: Numerical and Analytical Approaches
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2015 年 Mw 8.3 伊拉佩尔地震和海啸研究:数值和分析方法
DOI:
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发表时间:
2016
影响因子:
2
通讯作者:
A. Villalobos
中科院分区:
文献类型:
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作者:
M. Fuentes;S. Riquelme;G. Hayes;M. Medina;D. Melgar;G. Vargas;J. Gonzalez;A. Villalobos
The September 16, 2015 Illapel, Chile earthquake triggered a large tsunami, causing both economic losses and fatalities. To study the coastal effects of this earthquake, and to understand how such hazards might be accurately modeled in the future, different finite fault models of the Illapel rupture are used to define the initial condition for tsunami simulation. The numerical code Non-hydrostatic Evolution of Ocean WAVEs (NEOWAVE) is employed to model the tsunami evolution through the Pacific Ocean. Because only a short time is available for emergency response, and since the earthquake and tsunami sources are close to the coast, gaining a rapid understanding of the near-field run-up behavior is highly relevant to Chile. Therefore, an analytical solution of the 2 + 1 D shallow water wave equations is considered. With this solution, we show that we can quickly estimate the run-up distribution along the coastline, to first order. After the earthquake and tsunami, field observations were measured in the surrounded coastal region, where the tsunami resulted in significant run-up. First, we compare the analytical and numerical solutions to test the accuracy of the analytical approach and the field observations, implying the analytic approach can accurately model tsunami run-up after an earthquake, without sacrificing the time necessary for a full numerical inversion. Then, we compare both with field run-up measurements. We observe the consistency between the two approaches. To complete the analysis, a tsunami source inversion is performed using run-up field measurements only. These inversion results are compared with seismic models, and are shown to capture the broad-scale details of those models, without the necessity of the detailed data sets they invert.