乔峰,薄景山,张兆鹏,常晁瑜,王亮. 2020. 广西柳州地区常见土类剪切波速与埋深之间的相关性. 地震学报,42(1):109−119. doi:10.11939/jass.20190062. doi: 10.11939/jass.20190062
引用本文: 乔峰,薄景山,张兆鹏,常晁瑜,王亮. 2020. 广西柳州地区常见土类剪切波速与埋深之间的相关性. 地震学报,42(1):109−119. doi:10.11939/jass.20190062. doi: 10.11939/jass.20190062
Qiao F,Bo J S,Zhang Z P,Chang C Y,Wang L. 2020. Correlation between shear wave velocity and buried depth of common soils in Liuzhou city of Guangxi region. Acta Seismologica Sinica42(1):109−119. doi:10.11939/jass.20190062. doi: 10.11939/jass.20190062
Citation: Qiao F,Bo J S,Zhang Z P,Chang C Y,Wang L. 2020. Correlation between shear wave velocity and buried depth of common soils in Liuzhou city of Guangxi region. Acta Seismologica Sinica42(1):109−119. doi:10.11939/jass.20190062. doi: 10.11939/jass.20190062

广西柳州地区常见土类剪切波速与埋深之间的相关性

Correlation between shear wave velocity and buried depth of common soils in Liuzhou city of Guangxi region

  • 摘要: 基于广西柳州地区地震安全性评价中实测所获的钻孔资料,利用线性模型、幂函数模型和二次函数模型分别对该地区土层剪切波速与埋深之间的相关性进行了拟合分析,通过对比发现幂函数模型为二者间相关性拟合的最优选择,同时探讨了土体状态对二者相关性的影响。结果表明:除人工填土外,柳州地区内常见土层剪切波速与埋深之间具有较强的相关性,区域性对其相关性也具有影响。最后以实测钻孔为例,验证了本文模型的预测精度和可靠性,而且模型的预测精度可以通过区分土体状态得到明显提高。

     

    Abstract: Based on the measured borehole data in the earthquake safety assessment of Liuzhou region, Guangxi, three models (linear, power function, quadratic function models) were used to fit and analyze the correlation between shear wave velocity of soil layer and burial depth in this area. And then the power function model was selected to analyze the correlation between the depth of the soil layer and corresponding shear wave velocity in the area, and the influence of the soil state on the correlation between the two parameters was also discussed, and finally the actual drilling was taken as an example to verify the accuracy and reliability of the model. The obtained results are as follows: ① except artificial filling, there is a strong correlation between the shear wave velocity and the buried depth of common soils in the area, and the correlation can be affected by the region where it is; ② the soil state can significantly improve the prediction accuracy of the model.

     

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