Evaluation of a geospatial liquefaction model using land damage data from the 2016 Kaikōura earthquake

Amelia Lin, L. Wotherspoon, J. Motha
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Abstract

The paper uses two geospatial liquefaction models based on (1) global and (2) New Zealand specific variables such as Vs30, precipitation and water table depth to estimate liquefaction probability and spatial extent for the 2016 Kaikōura earthquake. Results are compared to observational data, indicating that the model based on global variables underestimates liquefaction manifestation in the Blenheim area due to the low resolution of the input datasets. Furthermore, a tendency for underprediction is evident in both models for sites located in areas with rapidly changing elevation (mountainous terrain), which is likely caused by the low resolution of the elevation-dependent variables Vs30 and water table depth leading to incorrect estimates. The New Zealand specific model appears to be less sensitive to this effect as the variables provide a higher resolution and a better representation of region specific characteristics. However, the results suggest that the modification might lead to an overestimation of liquefaction manifestation along rivers (e. g. Kaikōura). An adjustment of the model coefficients and / or the integration of other resources such as geotechnical methods can be considered to improve the model performance. The evaluation of the geospatial liquefaction models demonstrates the importance of high resolution input data and leads to the conclusion that the New Zealand specific model should be preferred over the original model due to better prediction performance. The findings provide an overall better understanding on the models’ applicability and potential as a tool to predict liquefaction manifestation for future hazard assessments.
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基于2016年Kaikōura地震土地破坏数据的地理空间液化模型评价
本文使用两个基于(1)全球和(2)新西兰特定变量(如Vs30、降水量和地下水位深度)的地理空间液化模型来估计2016年Kaikōura地震的液化概率和空间范围。结果与观测数据进行了比较,表明基于全局变量的模型低估了Blenheim地区的液化表现,因为输入数据集的分辨率较低。此外,对于位于海拔快速变化地区(山区地形)的场地,这两个模型都存在预测不足的趋势,这可能是由于海拔相关变量Vs30和地下水位深度的低分辨率导致了错误的估计。新西兰特有的模型似乎对这种影响不太敏感,因为变量提供了更高的分辨率和更好地代表特定地区的特征。然而,结果表明,这种修改可能会导致高估河流沿线的液化表现(例如Kaikōura)。可以考虑调整模型系数和/或整合其他资源,如岩土工程方法,以提高模型性能。对地理空间液化模型的评估表明了高分辨率输入数据的重要性,并得出结论,由于预测性能更好,新西兰特定模型应优于原始模型。研究结果使人们对模型的适用性和潜力有了全面更好的了解,可以作为预测液化表现的工具,用于未来的危险评估。
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来源期刊
CiteScore
2.50
自引率
17.60%
发文量
14
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