A review on clustering algorithms for spatiotemporal seismicity analysis

Spatiotemporal seismicity analysis has been conducted for a long time, yet significant effort is still needed to mitigate the adverse effects of earthquakes. Seismicity analysis also encompasses fundamental research into seismic patterns, for understanding the frequency, magnitude, temporal and spatial distribution of seismic events. Over the past few decades, it has been carried out through empirical relations, physics-based approaches, stochastic modeling, various machine learning algorithms, and deep learning algorithms for any given seismically active region. Clustering is an essential aspect of seismicity analysis, making it more complex, difficult, and challenging due to significant deviation from the stochastic phenomenon. In this paper, a comprehensive review of all potential data-driven earthquake clustering algorithms, models, and mechanisms are encapsulated for a variety of applications in seismology. The paper also describes the importance of an earthquake catalog with a short review of the fundamental empirical laws frequently used in statistical seismology. This paper also highlights the problem of seismicity declustering and reviews all the available algorithms to deal with it.