A Robust and Rapid Grid-Based Machine Learning Approach for Inside and Off-Network Earthquakes Classification in Dynamically Changing Seismic Networks

Abstract Earthquake location and magnitude estimation are critical for seismic monitoring and emergency response. However, accurately determining the location and the magnitude of off-network earthquakes remains challenging. Seismic stations receive signals from various sources, and it is crucial to quickly discern whether events originated within the area of interest. Location determination relies on obtaining ample P- and S-wave readings to ensure accurate and dependable results. Seismic networks vary due to station changes or outages, and their variable geometry represents a constraint for traditional machine learning models, which rely on fixed data structures. This study presents a novel approach for real-time classification of local and off-network earthquakes using the first three associated P picks within an early warning scenario, and also identifying the event’s direction. To handle variable network geometry, we employ a grid structure over the seismic area. The effectiveness of our method was initially validated with data from the Italian National Seismic Network, selecting Central Italy and Messina Strait subnetworks, and from a subnetwork of the Southern California Seismic Network; it achieves an inside–outside accuracy of 95%, 93%, and 96%, and a location region accuracy of 93%, 82%, and 97%, respectively. Its robustness was further demonstrated using picks from an earthquake early warning (EEW) system, the PRobabilistic and Evolutionary early warning SysTem (PRESTo) software, to simulate real and noncataloged input data. Our method outperforms PRESTo’s first localization, showing an inside versus outside classification improvement of 9.1% for Central Italy and 20.7% for the Messina Strait. This approach provides advanced seismic monitoring that can be implemented in systems devoted to reduce the impact of damaging events as the EEW system, but also shows promise for enhancing emergency response. Indeed, being able to quickly classify earthquakes is crucial for responding promptly and effectively during emergencies, minimizing risks, and for limiting false alarms.