Summary Injection-induced seismicity poses a major challenge to the safety of Enhanced Geothermal Systems (EGS). We customized a deep learning model to forecast seismicity rate under prescribed injection schedules. The model adopts a two-stage strategy where injection pressure is first forecasted as an intermediate variable and subsequently used to support seismicity rate forecasting. In this way, seismicity rates at both the field-scale Utah Frontier Observatory for Research in Geothermal Energy (Utah FORGE) and the mine-scale EGS Collab projects could be successfully forecasted. While the model without seismogenic index (Σ) could attain low forecast errors, incorporating Σ markedly improves its ability to capture the transient variability of seismicity rate. The forecasts at Utah FORGE and EGS Collab may highlight the importance of integrating key physical parameters calculated from raw observations into data-driven frameworks for forecasting injection-induced seismicity, and may demonstrate the potential of customized deep learning models for cross-stage forecasting in next-generation EGS.
Zhang et al. (Sat,) studied this question.