Artificial Intelligence-Driven Seismic Event Detection and Association for Enhanced Monitoring at KOERI

This study explores the integration of advanced artificial intelligence (AI) techniques into the seismic monitoring framework of the Kandilli Observatory and Earthquake Research Institute (KOERI), enhancing the accuracy and reliability of seismic event detection, location, and magnitude determination. The implementation leverages graph neural networks (GNNs) for seismic phase association and location problems, alongside pretrained AI models for phase picking. GNN is trained using datasets from both the Marmara and Maraş regions, and the resulting AI-based earthquake catalogs are compared against KOERI's legacy catalogs to assess performance and reliability.

 

Key innovations include:

• Application of GNNs to capture spatial and temporal relationships in seismic networks for improved event association.
• Enhanced phase picking and hypocenter localization accuracy, reducing uncertainty in earthquake catalogs.

Preliminary results indicate significant improvements in detecting low-magnitude events, reducing processing latency, and generating consistent and reliable earthquake catalogs. These advancements allow KOERI to provide high-resolution, AI-processed seismic data and earthquake catalogs, offering the seismological community access to more comprehensive and reliable seismic information while contributing to global research efforts.

The presentation will discuss the technical challenges encountered during integration and compare the new system's performance metrics to traditional methods used by KOERI. It will also explore the implications for future seismic monitoring practices.