Optimizing seismic sequence clustering with rapid cube-based spatiotemporal approach - Hasana, Silviya; Sari, Wina Permana; Rojali, Rojali; Fitrianah, Devi (IAES International Journal of Artificial Intelligence (IJ-AI), 2025)
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.