Seiscloud, a tool for density-based seismicity clustering and visualization

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 userprovided 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.

Earthquake Swarms, Slow Slip and Fault Interactions at the Western‐End of the Hellenic Subduction System Precede the Mw 6.9 Zakynthos Earthquake, Greece

“…The most interesting result of the MT inversion is that it demonstrates a high variability of MT configurations and faulting style over a quite compact region, extending laterally less than 60 km (Figures 1 and 5). Most of the 102 MT solutions could be classified (Cesca, 2020; see Figure S5) into eight families, each sharing similar focal mechanisms, spanning from pure strike-slip to pure thrust, and normal faulting. The variability in these mechanisms is consistent with a NE-SW trending pressure axes, in agreement with the convergence direction, and a NW-SE tension axis (Figure 5c).…”

Regional centroid MT inversion of small to moderate earthquakes in the Alps using the dense AlpArray seismic network: challenges and seismotectonic insights

“…We used a clustering algorithm (Cesca, 2020) based on the Kagan angles between all focal mechanisms obtained in this study and reported by gCMT, GEOFON, INGV, SED, EM-RCMT and ARSO to define classes of similar mechanisms (Fig. 9).…”

Seismicity clusters in Central Chile: investigating the role of repeating earthquakes and swarms in a subduction region

“…For the detection of spatial earthquake clusters, we use a density-based clustering algorithm (Cesca et al 2014;Maghsoudi et al 2013;Cesca 2020). The method scans the hypocentral distribution of the CSN catalogue, searching for regions with high earthquake density to detect seismicity clusters:…”

“…The definition of clustering parameters has been chosen with the objective to simultaneously resolve a limited number of clusters and reduce as much as possible the amount of unclustered events (Cesca 2020).…”

“…7illustrates the waveform similarity for the GO05 station, considering 57 events with M L 3.5 -5.2 for the Navidad cluster and 60 events with M L 3.5 -5.2 for the Vichuquén cluster. We applied a waveform-based clustering procedure, based on the DBSCAN algorithm(Cesca et al 2014;Cesca 2020), parameterized to require five events with a cross-correlation higher than 0.95 to define a cluster. Two families of waveforms are identified at the Navidad cluster.…”