Cluster analysis of earthquake hypocenters in Azerbaijan and surrounding territories

TL;DR

This study applies cluster analysis to earthquake hypocenters in Azerbaijan, revealing heterogeneous spatial distributions and identifying clusters that relate to regional fault networks and depth distribution patterns.

Contribution

The paper introduces a novel procedure combining Morisita Index, DBSCAN, HDBSCAN, and similarity measures for clustering earthquake hypocenters in seismic regions.

Findings

Hypocenter distribution is heterogeneous with distinct clusters.

Identified clusters correspond to regional fault structures.

Depth distributions show bimodal patterns within clusters.

Abstract

The research focuses on seismic events that occurred in Azerbaijan and adjacent territories, regions known for strong seismic activity. We analyze a catalog of recorded earthquakes between 2010 and 2023, extracting the locations of the earthquake hypocenters for study purposes. Using statistical methods and cluster analysis tools, we developed a procedure for partitioning hypocenter clusters. The procedure begins with estimates of the Morisita Index, which is suitable for preliminary assessments of the statistical properties of hypocenter sets. Analysis of the Morisita Index indicates that the spatial distribution of hypocenters is heterogeneous, containing denser domains referred to as clusters. The next stage involves identifying spatial clusters using the DBSCAN and HDBSCAN algorithms. Due to the strong dependence of results on the algorithm's parameters, we selected several partitions with 5-8 clusters that provided maximal or near-maximal Silhouette Index values. The final stage assesses the similarity of the resulting partitions, using the Adjusted Rand Index to identify partitions with a specified degree of similarity. The final set of partitions was compared to the fault network of the region. Based on the selected partition, the earthquake depth distributions were studied. Specifically, approximate probability density functions were constructed in the form of mixtures of normal distributions, leading to the identification of several bimodal distributions.