Abstract
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.