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.
Abstract (translated)
微地震（MEQs）是由地下流体注入生成的，它们记录了储层应力状态和渗透率的变化。因此，预测其时空演变对于增强地热系统（EGS）、二氧化碳封存和其他地球工程技术等应用至关重要。我们提出了一种基于变压器的深度学习模型，该模型可以摄入水力刺激历史和先前的MEQ观测数据，以预测四个关键参数：累积微地震次数、累计对数地震矩以及微震云团的第50百分位和第95百分位范围（$P_{50}, P_{95}$）。当应用于EGS Collab实验1的数据集时，该模型在1秒预报时间范围内所有目标上的$R^2> 0.98$，在15秒预报时间范围内所有目标上的$R^2 >0.88$，并通过学习的标准偏差项提供不确定性估计。这些精确且量化了不确定性的预测使得可以实时推断裂缝扩展和渗透率的变化，并展示了深度学习方法在未来流体注入操作中改善地震风险评估和指导缓解策略方面的巨大潜力。
URL
https://arxiv.org/abs/2506.14923
