ANALYSIS OF SEISMICITY ANOMALIES IN SULAWESI USING DBSCAN BASED ON USGS DATA (2021–2026)

Abstract

Sulawesi is one of Indonesia's most tectonically complex regions, situated at the meeting point of major plates and active fault systems, which results in significant seismic activity. This study aims to analyze seismicity anomalies in Sulawesi from 2021 to 2026 using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm applied to United States Geological Survey (USGS) data. The methodology involves a hybrid approach for parameter optimization, utilizing both the K-Distance Graph (KneeLocator) and Grid Search to ensure results are geologically representative. From an initial dataset of 859 events, 839 earthquake records were processed after geographic filtering, showing an average magnitude of 4.80 and an average depth of 70.15 km. The analysis reveals that the optimal parameters (ε = 0.2897 and MinPts = 3) produced 25 distinct clusters that align with real geological structures such as the Palu-Koro Fault and the Molucca Sea subduction zone. The algorithm successfully identified 66 points (7.87%) as seismic anomalies or noise, representing sporadic tectonic activity outside primary density zones. Furthermore, Convex Hull visualization identified a significant "seismic gap" between the Palu-Koro segment and Southeast Sulawesi, indicating a high-risk zone for potential future energy release. These findings demonstrate that density-based clustering is highly effective for mapping seismic hazards in complex tectonic regions, providing vital data for sustainable disaster mitigation planning in Sulawesi.