Abstract

Deep learning (DL)-based seismic picking models can improve the detection of induced

seismicity compared to more conventional methods and reveal finer-scale patterns of

induced seismicity. We present a high-resolution “deep” seismic catalogue for the

Preston New Road-1z (PNR-1z) shale gas site in the UK, developed using DL phase

picking on continuous downhole data. Using PhaseNet, we detected over 49,472

events, introducing up to 11,020 new events compared to the previous beamforming-

based catalogue (38,452 events). The deep catalogue increases event cluster densities

by an average of 42% and lowers the magnitude of completeness (Mc) from -0.2 to -0.5,

adding more than 1,700 events to Gutenberg-Richter b-value analyses. The deep

catalogue also reveals new spatio-temporal patterns, indicating fault reactivation north

of the PNR wells, contrasting with previous event locations and interpretations. These

results demonstrate that DL-enhanced catalogues can refine subsurface

interpretations, identify reactivated structures more eUiciently, and contribute to

improved hazard assessment in geo-energy systems. However, further improvements

are needed, as current models struggle to detect very small events (Mw < -0.5) in

downhole data. The development of bespoke AI tools for downhole monitoring also

remains challenging due to the limited availability of publicly accessible training

datasets. To address this gap, we develop the AI-ready downhole Microseismic

Benchmark (AMBER) database. AMBER contains picked event waveforms and seismic

noise from 10 downhole microseismic datasets. Over 4,000 events and >150,000

individual P- and S-wave picks have all been manually verified. The database also

includes over 10,000 examples of manually verified noise waveforms. AMBER provides

a benchmark resource for the development and testing of robust AI tools for downhole

microseismic processing.