Grouping of earthquakes into distinct clusters is applied to improve mechanism identification and pattern recognition for active seismicity in a region. One of the important issues concerning earthquake data clustering is determining the optimum number of clusters (ONC) at the early stages of algorithms. In this paper a robust method based on K-nearest neighbor search (KNNS) is presented to achieve three goals: improving output accuracy, improving output stability, and adding the ability to weight the features used in ONC determination. By introducing a new formula, the proposed method utilizes the error calculated for clustered data based on the similarity between the members in each cluster. An outlier attenuation algorithm is also used to improve the performance of the method. Both the Krzanowski–Lai Index (KLI) and the silhouette coefficient (SC), as two conventional methods, were used to compare the results and evaluate the performance. Experiments on synthetic data sets verified the effectiveness of the method, with considerable differences found. The clustering of a real earthquake catalogue related to the seismogenic province of Zagros in Persia using our proposed methodology suggests using 13-cluster analysis for clustering based on the spatiotemporal features with the same weights, and seven-cluster analysis for a case where priority is given only to the spatial parameters of the epicenters. Under the same circumstances, the KLI and SC methods suggest three and 18 clusters, respectively. The results of the experiments on synthetic data sets indicate that the proposed method is quantitatively more stable and more accurate than the other two methods.

中文翻译：

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基于K-最近邻搜索的鲁棒地震聚类分析

将地震分组为不同的集群可用于改进区域内活动地震活动的机制识别和模式识别。关于地震数据聚类的重要问题之一是在算法的早期阶段确定最佳聚类数（ONC）。在本文中，提出了一种基于 K 近邻搜索 (KNNS) 的稳健方法，以实现三个目标：提高输出精度、提高输出稳定性以及增加对 ONC 确定中使用的特征进行加权的能力。通过引入一个新公式，该方法利用基于每个簇中成员之间的相似性为聚类数据计算的误差。还使用异常值衰减算法来提高该方法的性能。Krzanowski-Lai 指数 (KLI) 和轮廓系数 (SC) 作为两种常规方法用于比较结果和评估性能。在合成数据集上的实验验证了该方法的有效性，发现了相当大的差异。使用我们提出的方法对与波斯扎格罗斯地震发生省相关的真实地震目录进行聚类，建议使用基于具有相同权重的时空特征的 13 聚类分析进行聚类，并在给予优先权的情况下使用七聚类分析仅与震中的空间参数有关。在相同情况下，KLI 和 SC 方法分别建议 3 个和 18 个集群。