Clustering algorithms can be applied to seismic catalogs to automatically classify earthquakes upon the similarity of their attributes, in order to extract information on seismicity processes and faulting patterns out of large seismic datasets. We describe here a Python open-source software for density-based clustering of seismicity named seiscloud, based on the pyrocko library for seismology. Seiscloud is a tool to dig data out of large local, regional, or global seismic catalogs and to automatically recognize seismicity clusters, characterized by similar features, such as epicentral or hypocentral locations, origin times, focal mechanisms, or moment tensors. Alternatively, the code can rely on user-provided distance matrices to identify clusters of events sharing indirect features, such as similar waveforms. The code can either process local seismic catalogs or download selected subsets of seismic catalogs, accessing different global seismicity catalog providers, perform the seismic clustering over different steps in a flexible, easily adaptable approach, and provide results in form of declustered seismic catalogs and a number of illustrative figures. Here, the algorithm usage is explained and discussed through an application to Northern Chile seismicity.

中文翻译：

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Seiscloud，用于基于密度的地震活动聚类和可视化的工具

可以将聚类算法应用于地震目录，以根据地震属性的相似性对地震进行自动分类，以便从大型地震数据集中提取有关地震活动过程和断层模式的信息。我们在此描述基于Python的pyrocko库的Python开源软件，用于基于密度的地震活动聚类称为seiscloud。Seiscloud是一种工具，可从大型的本地，区域或全球地震目录中挖掘数据，并自动识别具有类似特征（例如震中或震中位置，起始时间，震源机制或矩张量）的地震活动性群集。替代地，代码可以依赖于用户提供的距离矩阵来识别共享间接特征（例如类似波形）的事件簇。该代码既可以处理本地地震目录，也可以下载选定的地震目录子集，访问不同的全球地震活动目录提供者，以灵活，易于适应的方法在不同步骤上进行地震聚类，并以分散的地震目录和许多形式提供结果说明图。在此，通过对智利北部地震活动的应用来解释和讨论算法的用法。