Active Crustal and Slab Seismicity in Hispaniola Revealed Through a Unified Machine-Learning-Derived Earthquake Catalog

Abstract

Hispaniola lies at the complex boundary between the North American (NOAM) and Caribbean plates, where subduction, collision, and strike-slip tectonic processes generate frequent and damaging earthquakes. However, existing network operations have limited real-time access to stations and, as a result, catalogs are not sufficiently comprehensive to resolve fault structures and subducting slab geometries, constraining seismic hazard assessment. In this study, we applied three widely used deep-learning phase pickers (EQTransformer, generalized phase detection, and PhaseNet) to continuous seismic data from 2014 to 2023. After phase association using PyOcto and event location with Hypoinverse, depth resolution improved significantly, revealing a southeastward-deepening seismicity trend consistent with the subduction processes in Hispaniola. Merging the individual catalogs produced an aggregated machine-learning (ML) catalog containing over 47,000 earthquakes within the Hispaniola region, representing roughly three times more than those reported by local seismic networks over the same period. This ML catalog shows strong agreement with local network catalogs in phase arrival times and event locations, while also capturing low-magnitude seismicity concentrated along major fault zones, including the Enriquillo–Plantain Garden and Septentrional–Oriente fault zone, as well as previously undercharacterized clusters near San José de Ocoa and the margins of Enriquillo Lake. After relocation with hypoDD, the aggregated catalog also enabled imaging of the subducting NOAM slab, which may extend westward to the Haiti–Dominican Republic border. Near ∼70° W, seismicity becomes diffuse and does not clearly define a coherent slab geometry. East of this longitude, seismicity delineates a collision zone where the north-dipping Caribbean slab meets the south-dipping NOAM slab, supporting earlier double-slab interaction models. These results demonstrate that ML-based workflows can generate high-quality seismic catalogs that resolve slab geometries and tectonic processes, thereby improving seismic hazard assessment and supporting real-time monitoring in complex plate boundary regions like Hispaniola.