Chile is one of the most seismic countries in the world especially due to the subduction of the Nazca plate under the South America plate along the Chilean cost. Normally, the spatial distribution of seismic events tends to form spatial and temporal clusters around the main event including both precursor and aftershock events. However, it is very difficult to identify whether an event is a precursor, a main event or an aftershock. In the literature, only some large earthquakes are well described but it does not exist an automatic method to classify them. In this work, we propose a new density based clustering method, called ST-DBSCAN-EV (Space-time DBSCAN with Epsilon Variable), which allows the Epsilon parameter (the radius) to vary depending on the density of the points. The results of the ST-DBSCAN-EV are validated on three important earthquakes with magnitude greater than 8.0 Mw occurred in Chile in the last 20 years, by carrying out a series of experiments considering different combinations of parameters. A comparison with some traditional clustering techniques such as the DBSCAN, ST-DBSCAN, and the K-means has been implemented for assessing the performance of the proposed method. Almost in all cases ST-DBSCAN-EV outperformed traditional ones by providing an F1-Score metric higher than 0.8. Finally, the results of classification are compared with a declustering method.

智利是世界上地震最严重的国家之一，特别是由于纳斯卡板块沿智利海岸俯冲到南美洲板块之下。通常，地震事件的空间分布倾向于在主要事件（包括前震事件和余震事件）周围形成空间和时间集群。然而，识别一个事件是前兆、主要事件还是余震是非常困难的。在文献中，仅对一些大地震进行了很好的描述，但不存在自动分类方法。在这项工作中，我们提出了一种新的基于密度的聚类方法，称为 ST-DBSCAN-EV（带有Epsilon变量的时空 DBSCAN ），它允许Epsilon参数（半径）根据点的密度而变化。通过考虑不同参数组合进行一系列实验，ST-DBSCAN-EV 的结果在过去 20 年智利发生的三起震级大于 8.0 Mw 的重要地震中得到了验证。与一些传统的聚类技术（例如 DBSCAN、ST-DBSCAN 和K 均值）进行了比较，以评估所提出方法的性能。几乎在所有情况下，ST-DBSCAN-EV 的 F1 分数指标均高于 0.8，其性能均优于传统方案。最后，将分类结果与去聚类方法进行比较。