This paper evaluates existing geospatial liquefaction models using the observed land performance across four events of the Canterbury Earthquake Sequence and analyses the influence of region specific input variables on the models' potential to predict liquefaction manifestation. The aggregated performance for each event is assessed using receiver operating characteristic (ROC) analysis and the liquefaction spatial extent (LSE). The assessment results in high ROC performance and shows that the models are able to capture the areas of the most severe liquefaction manifestation observed following each earthquake. Replacing the input variables for distance to closest river, time averaged shear wave velocity over the upper 30 m (Vs₃₀) and water table depth with New Zealand specific datasets does not significantly impact the outcomes of the ROC analysis across the study areas. However, the improved spatial accuracy of the LSE maps based on these specific datasets supports their use, especially for regional or local hazard assessments, and demonstrates the potential for further development of the models. The findings of this study provide an improved understanding of the performance of geospatial liquefaction models and their ability to identify exposed areas for regional and large scale hazard assessments.

本文使用在坎特伯雷地震序列的四个事件中观察到的土地表现来评估现有的地理空间液化模型，并分析区域特定输入变量对模型预测液化表现的潜力的影响。使用接收器工作特性（ROC）分析和液化空间范围（LSE）评估每个事件的综合性能。评估结果显示出较高的ROC性能，并且表明该模型能够捕获每次地震后观察到的最严重的液化表现区域。替换最靠近河流的距离，上方30 m（Vs _30）的平均剪切波速度的输入变量）和地下水位深度（与新西兰特定的数据集一起使用）不会显着影响整个研究区域内ROC分析的结果。但是，基于这些特定数据集的LSE映射提高的空间准确性支持将其使用，尤其是用于区域或局部灾害评估，并显示出进一步开发模型的潜力。这项研究的结果使人们对地理空间液化模型的性能及其识别区域和大规模灾害评估暴露区域的能力有了更好的了解。