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

Simone Cesca

doi:10.1007/s10950-020-09921-8

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.

Highlights

In recent years, the global densification of seismic stations, the growing interest in microseismicity monitoring, with the deployment of dense local networks to identify natural and anthropogenic microseismicity, and the implementation of powerful and unsupervised algorithms to scan large seismic datasets have allowed seismologists to detect, locate, and characterize increasingly weakearthquakes
The application of a temporal seismicity clustering, aimed at the identification of seismicity bursts occurring within short time frames, can be used to identify seismic sequences and swarms or, in the frame of monitoring issues, to early detect anomalous seismicity rates, potentially revealing stress or pore pressure transients, anticipating larger earthquakes or volcanic unrests
This paper describes the seiscloud software, implementing a density-based clustering approach for seismicity

Summary

Introduction

The global densification of seismic stations, the growing interest in microseismicity monitoring, with the deployment of dense local networks to identify natural and anthropogenic microseismicity, and the implementation of powerful and unsupervised algorithms to scan large seismic datasets have allowed seismologists to detect, locate, and characterize increasingly weak (micro)earthquakes. Clustering algorithms are useful tools to automatically identify families of similar items out of large datasets: applied to seismicity, they can be used to detect earthquakes with similar features, such as hypocentral locations, origin times, magnitudes, or focal mechanisms. Such type of seismicity classification is important to support seismic monitoring programs and seismicity interpretation studies. The algorithm has a flexible implementation, with several different metrics available, offering to cluster seismicity upon the similarity of the spatial, temporal, or focal mechanism earthquake parameters, or to use user-defined metrics by providing distance matrices. The software is implemented in Python, open source, it uses and requires the installation of the pyrocko libraries and it is available through an open git platform (https://git. pyrocko.org/cesca/seiscloud)

Density-based clustering for seismicity

Metrics

Sample application: seismicity at the northern Chile subduction

Discussion and conclusions