Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes
This addresses seismic-risk assessment and mitigation in geo-engineering applications like enhanced geothermal systems and CO2 sequestration, representing a strong specific gain but incremental in method.
The paper tackles forecasting the spatiotemporal evolution of fluid-induced microearthquakes using a transformer-based deep learning model, achieving R^2 >0.98 for 1-second and >0.88 for 15-second forecasts on key quantities like cumulative count and seismic moment.
Microearthquakes (MEQs) generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their full spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems (EGS), CO$_2$ sequestration and other geo-engineering applications. We present a transformer-based deep learning model that ingests hydraulic stimulation history and prior MEQ observations to forecast four key quantities: cumulative MEQ count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents ($P_{50}, P_{95}$) of the MEQ cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves $R^2 >0.98$ for the 1-second forecast horizon and $R^2 >0.88$ for the 15-second forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.