We develop a data-driven clustering method to evaluate a velocity model using surface wave velocity dispersion. This is done by first computing theoretical dispersion curves for 1-D velocity profiles of all the grid locations and then splitting the resulting dispersion curves into a certain number of groups via the K-means clustering. The observed dispersion curves are also clustered following the same procedure and the velocity model is assessed by comparing the spatial patterns obtained for the observed and synthetic data sets. The method is applied to evaluate two community velocity models in southern California, CVM-S4.26 and CVM-H15.1, using phase velocity maps derived for 3–16 s Rayleigh waves. We found a good correlation in the spatial distribution of clusters between the result of CVM-S4.26 and that of the observed data, suggesting that the CVM-S4.26 fits the observed dispersion maps better than the CVM-H15.1 in terms of features extracted from the clustering analysis.

中文翻译：

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使用 K 均值聚类的数据驱动速度模型评估

我们开发了一种数据驱动的聚类方法来评估使用表面波速度色散的速度模型。这是通过首先计算所有网格位置的一维速度剖面的理论色散曲线，然后通过 K 均值聚类将产生的色散曲线分成一定数量的组来完成的。观察到的分散曲线也按照相同的程序进行聚类，并且通过比较观察到的和合成的数据集获得的空间模式来评估速度模型。该方法用于评估加利福尼亚南部的两个社区速度模型，CVM-S4.26 和 CVM-H15.1，使用为 3-16 s 瑞利波导出的相速度图。我们发现 CVM-S4.26 的结果与观测数据的结果之间的聚类空间分布具有良好的相关性，