Space-Time Clustering of Seismicity in Algeria

Each year, earthquakes pose a significant threat to human life, attributed to their sudden and unpredictable nature. Over time, a heightened awareness of this phenomenon has driven increased attention from researchers and experts. This paper seeks to demonstrate the applicability of the k-means algorithm to seismic data, focusing on the identification of seismic zones in Algeria. Initially, we conducted a comprehensive review of existing literature on clustering seismic data, revealing an unexplored niche in the context of Algeria’s seismicity. Subsequently, we introduce our dataset comprising 5876 seismic events. A detailed explanation of the k-means algorithm is provided, with a breakdown of each parameter. Visualization of our findings, including determining the optimal value for k using Elbow and Silhouette scores, is presented and thoroughly discussed. In conclusion, we identify and delineate the seismic zones in Algeria, highlighting the four most critical regions encapsulating these zones. This study contributes to a better understanding of seismic patterns in Algeria, potentially aiding in the development of more effective earthquake preparedness and mitigation strategies.