Machine learning enhanced earthquake relocation

Seismic activity provides critical insights into subsurface processes such as tectonic movements, volcanic activity, and fluid migrations, with accurate earthquake locations being essential for enhancing our understanding of these seismic behaviors. However, seismic array geometry significantly influences earthquake location accuracy. Over the past two decades, seismologists have improved earthquake catalogs by expanding seismic networks and densifying station coverage in seismically active regions, leading to more precise event detection and location accuracy. Following major seismic events, temporary seismometer deployments refine monitoring and analysis, particularly for aftershocks, enhancing the understanding of the region's seismic behavior and potential risks. In this study, we introduce a novel method that benefits from these temporary deployments to relocate hypocenters determined by a permanent seismic array, using hypocenters derived from a combination of permanent and temporary arrays with better geometry. Our method employs a random forest algorithm to learn how to relocate seismic
events detected with the permanent, low-density seismic array. We developed the method in the case of Mayotte Island, where, following the eruption in 2018, scientists deployed ocean-bottom seismometers (OBS) and land seismic sensors to build high-quality catalogs that provide a better understanding of the region's dynamics. Our findings show a significant reduction in root-mean-square error between the hypocenters located with permanent seismic stations and those located with a combination of permanent and temporary seismic stations, demonstrating the method's effectiveness in reducing systematic biases and enhancing location accuracy. This method is applicable across a range of contexts, particularly in scenarios characterized by suboptimal seismic station geometry, offering a robust framework for enhancing the location accuracy of seismic events.