Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

TL;DR

This paper introduces a transformer-based deep learning model that accurately forecasts the spatiotemporal evolution of fluid-induced microearthquakes, providing uncertainty estimates to improve seismic risk assessment in geo-engineering.

Contribution

The paper presents a novel transformer-based deep learning approach for real-time forecasting of microearthquake evolution with uncertainty quantification, applied to geothermal reservoir data.

Findings

Achieves high R^2 (>0.98) for 1-second forecasts

Provides reliable uncertainty estimates

Enables real-time seismic risk assessment

Abstract

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.