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An improved K -means clustering algorithm for global earthquake catalogs and earthquake magnitude prediction
Journal of Seismology ( IF 1.6 ) Pub Date : 2021-03-16 , DOI: 10.1007/s10950-021-09999-8
Rui Yuan

The occurrences of earthquakes have no any explicit warning, and earthquake magnitude prediction is still extremely challenging now. Therefore, this study proposes a seismic prediction model based on clustering of global earthquake data. First, an improved K-means clustering algorithm for global earthquake catalogs is proposed. Traditional K-means clustering has several limitations, i.e., the number of clusters needs to be initialized, the initial cluster centers are arbitrarily selected, and there is currently no magnitude parameter in the K-means clustering algorithm. To improve the algorithm, this study employs the space–time–magnitude (STM) distance and then proposes a maximum–minimum STM distance for the selection of the initial cluster centers. Additionally, the sum of squares error, Davies–Bouldin index, Calinski–Harabasz index, and silhouette coefficient are applied to determine the number of clusters. Subsequently, a seismic prediction model based on the clustering result combined with an artificial neural network is presented. Application of the improved clustering algorithm to the global seismic catalog from 1900–2019 obtained from the United States Geological Survey reveals better clustering accuracy than the traditional K-means algorithm and is also effective for seismic risk analysis in the local region. Furthermore, the seismic prediction model based on the clustering result also exhibits good performance, which has practical significance and reference value for future predictions of earthquake magnitude.



中文翻译:

改进的K均值聚类算法在全球地震目录和地震烈度预测中的应用

地震的发生没有任何明确的警告,现在地震烈度的预测仍然极具挑战性。因此,本研究提出了一种基于全球地震数据聚类的地震预测模型。首先,提出了一种用于全球地震目录的改进的K均值聚类算法。传统的K均值聚类有几个局限性,即需要初始化的聚类数量,任意选择初始聚类中心,并且当前K中没有大小参数。-均值聚类算法。为了改进算法,本研究采用时空幅度(STM)距离,然后为选择初始聚类中心提出了最大最小STM距离。此外,平方和,Davies-Bouldin指数,Calinski-Harabasz指数和轮廓系数的总和被用于确定聚类数。随后,提出了基于聚类结果并结合人工神经网络的地震预测模型。改进的聚类算法在从美国地质调查局获得的1900-2019年全球地震目录中的应用显示出比传统的K更好的聚类精度。-均值算法,并且对于本地区域的地震风险分析也有效。此外,基于聚类结果的地震预测模型也表现出良好的性能,对未来地震烈度的预测具有实际意义和参考价值。

更新日期:2021-03-17
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