An Approach for Clustering of Seismic Events using Unsupervised Machine Learning

Abstract

New and effective approaches for the analysis of seismic data make it possible to identify the distribution of earthquakes helping further to assess frequency of occurrence any associated risks. This paper proposes an effective approach for detecting areas with increased spatial density of seismic events and zoning territories on the map based on the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN algorithm). The validity of the choice of this clustering algorithm is explained by the fact that the DBSCAN algorithm can detect clusters of complex shapes including geographical coordinates. This study uses seismic data from the seismic catalog of the Republic of Kazakhstan from 2011 to 2021 inclusive. Finally, the clusters detected over a certain period of time allowed for the presentation of a spatial model of the distribution of earthquakes and the detection of areas with increased spatial density on the map. In general, the results of the study were also compared and well associated with the general map of the seismic zoning of the Republic of Kazakhstan showing reliable results of clustering based on density. In addition, the architecture of intelligent information and the analytical system for analyzing seismic data is based on the proposed approach.