Abstract An earthquake of moment magnitude 5.4 occurred on November 15, 2017 in Pohang, South Korea. This earthquake is the second largest instrumented earthquake ever recorded in South Korea and caused extensive damage to the ground and infrastructures. Hundreds of liquefaction-induced sand boils were observed in the rice fields near the epicenter. We calculate the liquefaction potential index (LPI) and liquefaction severity number (LSN) using the existing borehole data from the area, which includes 166 soil borings, to predict local and regional sand boils. Three of the soil borings considered in the dataset are within 47 m from the sand boils, and one is within 174 m from the sand boil. We provide detailed soil profiles at 20 sites (four of which are considered to be at liquefied sites) and soil properties at three sites (two of which are at liquefied sites). The calculated LPI and LSN values are in good agreement with the actual sand boil locations at 19 out of the 20 sites. Then, we use the existing borehole data to produce a regional liquefaction map using interpolation methods. As is common in regional seismic hazard maps, geotechnical and ground motion data are limited; therefore, we assess the uncertainty of important input parameters, such as the peak ground acceleration, water table, and N-value. For the regional maps, the maximum accuracy of the LPI is 69% at an LPI threshold of 2, and that of the LSN is 71% at an LSN threshold of 10. We report some ground settlements in the areas with high LPI and LSN values. Furthermore, we propose models for predicting the sizes and occurrence probabilities of sand boils.

中文翻译：

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韩国浦项液化诱发砂沸的液化潜力指数和液化严重度数的局部和区域评估

摘要 2017 年 11 月 15 日，韩国浦项发生 5.4 级地震。此次地震是韩国有记录以来发生的第二大地震，对地面和基础设施造成了广泛破坏。在震中附近的稻田中观察到了数百个液化引起的沙子沸腾。我们使用该地区现有的钻孔数据（包括 166 个土壤钻孔）计算液化潜力指数 (LPI) 和液化严重程度数 (LSN)，以预测当地和区域的砂沸。数据集中考虑的三个土钻孔距砂沸点 47 m 以内，一个距砂沸点 174 m 以内。我们提供了 20 个地点（其中四个被认为在液化地点）的详细土壤剖面和三个地点（其中两个在液化地点）的土壤特性。计算出的 LPI 和 LSN 值与 20 个站点中 19 个站点的实际砂沸位置非常一致。然后，我们使用现有钻孔数据使用插值方法生成区域液化图。与区域地震危险图一样，岩土和地面运动数据有限；因此，我们评估重要输入参数的不确定性，例如峰值地面加速度、地下水位和 N 值。对于区域地图，LPI 阈值为 2 时 LPI 的最大精度为 69%，LSN 阈值为 10 时 LPI 的最大精度为 71%。我们报告了一些具有高 LPI 和 LSN 值的地区的地面定居点.