Complex earthquake networks: Hierarchical organization and assortative mixing

TL;DR

This paper models seismic activity as a complex network, revealing hierarchical organization and assortative mixing, which differ from other networks like the Internet, providing insights into earthquake dynamics.

Contribution

It introduces a novel network-based approach to analyze seismicity, demonstrating hierarchical structure and assortative mixing in earthquake networks.

Findings

Clustering coefficient shows power-law decay, indicating hierarchy.

Earthquake network exhibits assortative mixing, unlike the Internet.

Supports the idea of stress release and vertex fitness influencing network structure.

Abstract

To characterize the dynamical features of seismicity as a complex phenomenon, the seismic data is mapped to a growing random graph, which is a small-world scale-free network. Here, hierarchical and mixing properties of such a network are studied. The clustering coefficient is found to exhibit asymptotic power-law decay with respect to connectivity, showing hierarchical organization. This structure is supported by not only main shocks but also small shocks, and may have its origin in the combined effect of vertex fitness and deactivation by stress release at faults. The nearest-neighbor average connectivity and the Pearson correlation coefficient are also calculated. It is found that the earthquake network has assortative mixing. This is a main difference of the earthquake network from the Internet with disassortative mixing. Physical implications of these results are discussed.