A New Statistical Method to distinguish Different Earthquake Cluster Types

Earthquake clusters can be broadly classified into two types: swarm-like sequences and mainshock–aftershock sequences. The spatial organization of the two types provides important insights into underlying tectonic processes and fluid migration in earthquake source regions. In this study, we apply the nearest-neighbor distance approach on the Southern California focal-mechanism earthquake catalog (the CNN_SoCal catalog) and introduce two new statistical indicators-skewness and kurtosis to distinguish between these two classes of earthquake clusters. We find that the square root of kurtosis and skewness provide effective and interpretable indicators for clusters classification. In the kurtosis–skewness diagram, swarm-like sequences and mainshock–aftershock sequences tend to occupy distinct regions, enabling a practical distinction between the two sequence types without relying on subjective inspection of individual clusters. Overall, the proposed approach offers an efficient way to differentiate swarm-like and mainshock–aftershock seismicity in large catalogs. The method is computationally light, easy to implement, and suitable for rapid screening of earthquake sequence types in high-resolution regional datasets.