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Identification of rock discontinuity sets based on a modified affinity propagation algorithm

机译:基于改进的亲和传播算法的岩石不连续集的识别

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摘要

Identification of rock discontinuity sets is an important foundation for stability analysis of rock engineering applications. A modified affinity propagation (AP) algorithm is proposed for identifying rock discontinuity sets based on discontinuity orientations. The method considers all data points as potential clustering centers simultaneously, which could avoid the hard selection on initial clustering centers as well as achieve the global optimization. Euclidean distance measure in the original algorithm is not suitable for the clustering of discontinuity orientations and therefore a similarity measure based on the negative sine-squared value of the acute angle between discontinuity unit normal vectors was used. Moreover, the Silhouette validity index was introduced to determine the optimal clustering number. The validity of the new method was tested by using artificial data, and in-situ data compiled from the literature. Finally, the proposed method was applied to discontinuity grouping in an underground water-sealed oil storage cavern in Liaoning Province, China. The results show that the new method can effectively filter noisy data and achieve good clustering results with stronger robustness than other methods.
机译:岩石不连续集的识别是岩石工程应用稳定性分析的重要基础。提出了一种修改的亲和传播(AP)算法,用于基于不连续方向识别岩石不连续组。该方法同时认为所有数据点作为潜在的聚类中心,这可以避免初始聚类中心的艰难选择以及实现全局优化。原始算法中的欧几里德距离测量不适用于不连续取向的聚类,因此使用基于不连续单元正常矢量之间的锐角的负正弦方形值的相似性度量。此外,引入了轮廓有效性指数以确定最佳聚类数。通过使用人工数据测试新方法的有效性,以及从文献编译的原位数据。最后,将所提出的方法应用于中国辽宁省地下水封油储物洞中的不连续性分组。结果表明,新方法可以有效地滤除噪声数据并实现良好的聚类结果,比其他方法更强的鲁棒性。

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