InaFocMec: Early Focal Mechanism Estimation from P-Wave Polarity using Strong-Motion Accelerograph Data from Indonesian Earthquakes

Rapid focal mechanism estimation plays a critical role in earthquake and tsunami hazard assessment, although it is not the sole parameter, particularly in time-sensitive early warning scenarios. Conventional approaches based on full waveform inversion generally require substantial processing time, limiting their applicability for early warning when timely information is essential for decisionmaking considerations. In the initial stage following an earthquake, rapid indication of tsunami potential is critically needed; therefore, complementary approaches based on P-wave polarity can help fill this information gap. Although Indonesia operates an extensive Strong-Motion Accelerograph (SMA) network, its utilization for focal mechanism determination remains limited. To address this challenge, we propose InaFocMec, a novel method integrating deep learning-based polarity detection with the SKHASH algorithm for focal mechanism inversion. The model was trained using optimized P-wave time windows ranging from 0.20 to 0.05 seconds and evaluated on a comprehensive dataset representing Indonesian tectonic settings, achieving 99.76% classification accuracy. The resulting focal mechanism solutions for significant earthquakes show strong agreement with global and national reference catalogs, demonstrating robustness. These findings highlight the potential of InaFocMec as a rapid tool to enhance earthquake source characterization and support more effective tsunami early warning systems in Indonesia.