Optimizing seismic sequence clustering with rapid cube-based spatiotemporal approach

Abstract

Due to their extensive volume and range of features, seismic data is regarded as highly complex data. Earthquakes that typically composed of foreshocks, mainshocks, and aftershocks, exhibit a unique sensitivity to temporal dimension, a characteristic that differs them from other natural hazards. Foreshocks and aftershocks that emanate from a similar epicenter, often display temporal patterns that contribute significantly to determining a sequence. This study introduces a density cube-based approach to cluster spatiotemporal seismic data. It addresses spatial irregularities observed in earthquake clusters and incorporates temporal aspects, acknowledging that seismic events originating from a similar epicenter could occur in separate time frames. We achieved the highest Silhouette score of 0.935 in daily-based clustering and 0.782 in weekly-based clustering. Notably, our analysis reveals a trend where weekly clustering lambda λ tend to be lower (λ=0.01) than in daily clustering (λ=0.1, λ=0.5), thus emphasizing the significance of temporal granularity where daily clustering requires higher λ to capture rapid fluctuations, while weekly clustering benefits from lower λ to cover broader trends. These findings enhance the understanding of the nuanced interplay of temporal dynamics in seismic sequence analysis.