基于t-SNE的多参数岩体结构面分步聚类方法

A stepwise clustering method of rock discontinuities dominated by multivariate parameters based on t-SNE

结构面聚类是进行岩体稳定性评价的重要步骤。常用聚类方法多以产状作为分组依据,忽略了结构面物理特性指标对岩体稳定性的影响。针对分组依据单一化的不足,综合考虑结构面倾向、倾角、迹长、张开度、填充状态和粗糙度的影响,提出一种基于学生分布随机邻近嵌入(student-distributed stochastic neighbor embedding,简称t-SNE)的多参数岩体结构面分步聚类方法。首先,利用t-SNE算法对除产状外的结构面特征进行数据降维;进而利用模拟退火算法搜索K-means算法的全局最优初始值,并采用分步聚类思想完成聚类。研究表明:所提方法有效地解决了高维空间样本稀疏的问题,同时保留了数据的局部结构与全局结构。新方法相比于传统方法能对空间分布相似区内结构面的物理特性进行精确划分,分组精度更高,且在避免复杂权重值计算的条件下,能有效区分产状与物理特性参数对岩体稳定性的影响差异。最后,将所提方法应用于中国新疆某露天矿坡结构面实测数据分析中,所得分组结果合理可靠,进一步证明该方法在实际工程中的有效性。研究方法可为多参数岩体结构面的分步聚类提供参考。

Clustering of rock discontinuities is crucial for evaluating rock mass stability.The conventional clustering methods often rely on the orientations of rock discontinuities,without considering the influence of physical characteristics on rock mass stability.To address the limitations of single-factor grouping,a stepwise clustering method of rock discontinuities dominated by multivariate parameters based on student-distributed stochastic neighbor embedding(t-SNE)is proposed.This method takes into account the effects of dip direction,dip angle,trace length,opening,filling state and roughness of rock discontinuities.Firstly,the t-SNE algorithm is used to reduce the dimensionality of discontinuity characteristics except for the orientations.Subsequently,the simulated annealing algorithm is employed to search for the global optimal initial values of the K-means algorithm,and the stepwise clustering idea is utilized to accomplish the clustering.The research shows that the proposed method addresses the sparsity issue of high-dimensional data while preserving the local and global structures of the data.Compared to the conventional methods,the proposed method achieves more accurate partitioning of physical characteristics within the spatial distribution similarity zone,resulting in higher grouping accuracy.Furthermore,the proposed method effectively distinguishes the differences between orientations and physical characteristic parameters on rock mass stability without the need for complex weight value calculations.Finally,the proposed method is applied to the measured data of rock discontinuities in an open-pit slope in Xinjiang,China.The grouping results are found to be reasonable and reliable,which further validates the effectiveness of the proposed method in practical engineering.This research provides a reference for stepwise clustering of multi-parameter rock discontinuities.