Completing Indonesia Earthquake Catalog for Better Earthquake and Tsunami Hazard Assessments

Indonesia is located in one of the most seismically active regions in the world and is monitored by more than 550 broadband and short-period seismic sensors. On average, around 40,000 earthquakes with magnitudes greater than 2 occur each year. However, this number of earthquakes with magnitudes larger than 2 has only been observable in recent years due to the significant expansion of the seismic network, for example in 2025. In earlier years, the recorded number of earthquakes was significantly lower, with the magnitude of completeness (Mc) reaching only about 4. Completing earthquake catalogs in a region is extremely important for revealing detailed main and secondary fault structures, which is essential for improved earthquake and tsunami hazard assessment. Recently, earthquake event recognition and phase picking using machine learning (ML) have proven highly successful in detecting smaller-magnitude earthquakes that are often overlooked by conventional methods such as STA/LTA. This study presents high-resolution earthquake catalogs generated using ML-based earthquake detection. The ML algorithms were pre-trained across various regions, tectonic settings, and environmental conditions using data from the last decade of combined BMKG and temporary seismic networks across Indonesia. We compare a three-catalog framework consisting of ML-derived, real-time, and analyst-reviewed catalogs. The results show that ML-based detection identifies significantly more earthquakes at lower magnitudes, with Mc reaching approximately 2, compared to real-time processing and human-reviewed catalogs. Using a recently published ML-based focal mechanism model, our results also show a substantially larger focal mechanism catalog, including many events with magnitudes smaller than 5 that are often not reviewed in conventional processing. This study demonstrates the importance of ML-based earthquake detection in improving the efficiency and completeness of earthquake detection and highlights its strong potential for operational integration into Indonesia’s seismic monitoring systems.