Earthquakes are known to occur at plate boundaries, yet these natural hazards can also occur within stable continental regions beyond the plate boundary, such as in eastern North America. The hazard and risk posed by earthquakes in eastern North America is great because the region includes half of the top ten most populous metropolitan areas in the United States and generates 25% of its GDP. Though earthquakes are rare in the region, the recent April 2024 Mw4.8 earthquake in New Jersey highlights the importance of studying earthquake seismicity within stable continental regions. However, the earthquake rate in eastern North America is low, and sparse seismic networks have hampered progress in understanding the nature of faults and the earthquakes they produce. In this project, scientists will develop new machine-learning and cross-correlation methods to detect previously undetected earthquake events at a significantly lower magnitude detection threshold and higher location precision compared to existing catalogs, providing fundamental new data to study seismogenesis and seismotectonics in eastern North America at a broad range of spatial scales. This project aims to significantly improve on and expand currently available catalogs of earthquake parameters (including location, magnitude, focal mechanism) for eastern North America by applying advanced machine-learning and cross-correlation based earthquake detection and characterization methods to decades of continuous wav