Deep Learning-Enhanced Catalogue of Induced Microseismicity at Preston New Road-1z, UK: New Insights into Spatio-Temporal Patterns and Structural Control

DL-based earthquake detection can detect substantially more induced microseismic events than conventional detection methods, revealing finer-scale patterns of hydraulic fracturing-induced seismicity (HFIS). We implement a novel workflow for the Preston New Road-1z (PNR-1z) shale gas site, UK, that integrates DL phase picking, Oct-Tree-based phase association, and probabilistic non-linear location to generate a high-resolution “deep” seismic catalogue from a single high-frequency (2000 Hz) borehole array. PhaseNet has detected 11,021 previously undetected events (49,472 events total), compared to the previous catalogue, in which 38,452 events were identified by a beamforming method. We find that the enhanced catalogue increases event cluster densities by an average of 42% and lowers the magnitude of completeness (Mc) from -0.2 to -0.5, adding thousands of additional events to Gutenberg-Richter b-value analyses. The spatial patterns suggest that HFIS interacted with a fault or fracture zone north of the PNR wells, contrasting with previous event locations and their interpretations. Combining the largest and best recorded events, using locations from our independently derived catalogue, with 3D seismic reflection data reveals clustering along previously unmapped seismic discontinuities ~300 m north of the injection wells, indicating strong structural controls on HFIS. In addition, we re-estimate moment magnitudes (Mw) using continuous downhole waveforms, finding that magnitudes reported in the previous operational catalogue were systematically underestimated by an average of 0.93 units. The temporal b-values revealed higher b-values during active injection, consistent with tensile HF-dominated microseismicity, and relatively lower b-values during pauses and certain mid-stages, indicating increased shear failure and interaction with a pre-existing fracture zone. The deep catalogue allows for more robust b-value estimation and temporal tracking, potentially reflecting evolving source characteristics and fluid-driven processes during stimulation. This study demonstrates that DL-enhanced catalogues can refine subsurface interpretations, identify re-activated structures efficiently, and contribute to improved hazard assessments in geo-energy systems.