The Groningen gas field in the Netherlands is experiencing induced seismicity as a result of ongoing depletion. The physical mechanisms that control seismicity have been studied through rock mechanical experiments and combined physical-statistical models to support development of a framework to forecast induced-seismicity risks. To investigate whether machine learning techniques such as Random Forests and Support Vector Machines bring new insights into forecasts of induced seismicity rates in space and time, a pipeline is designed that extends time-series analysis methods to a spatiotemporal framework with a factorial setup, which allows probing a large parameter space of plausible modelling assumptions, followed by a statistical meta-analysis to account for the intrinsic uncertainties in subsurface data and to ensure statistical significance and robustness of results. The pipeline includes model validation using e.g. likelihood ratio tests against average depletion thickness and strain thickness baselines to establish whether the models have statistically significant forecasting power. The methodology is applied to forecast seismicity for two distinctly different gas production scenarios. Results show that seismicity forecasts generated using Support Vector Machines significantly outperform beforementioned baselines. Forecasts from the method hint at decreasing seismicity rates within the next 5 years, in a conservative production scenario, and no such decrease in a higher depletion scenario, although due to the small effective sample size no statistically solid statement of this kind can be made. The presented approach can be used to make forecasts beyond the investigated 5-years period, although this requires addition of limited physics-based constraints to avoid unphysical forecasts.

由于持续耗竭，荷兰格罗宁根（Groningen）气田正遭受地震诱发。已经通过岩石力学实验和组合的物理统计模型研究了控制地震活动的物理机制，以支持开发预测地震风险的框架。为了研究诸如随机森林和支持向量机之类的机器学习技术是否为时空诱发地震率的预测带来新见解，设计了一条管道，该管道将时间序列分析方法扩展到具有因子设置的时空框架，从而可以探究合理的建模假设的大参数空间，然后进行统计荟萃分析，以说明地下数据的内在不确定性，并确保统计意义和结果的可靠性。该管道包括使用模型验证（例如，使用似然比测试针对平均耗尽层厚度和应变层厚度基准线）来确定模型是否具有统计上显着的预测能力。该方法适用于两种截然不同的天然气生产情景的预测地震活动性。结果表明，使用支持向量机生成的地震活动预报明显优于上述基准。根据该方法的预测表明，在保守的生产方案中，未来5年内地震活动率将下降，而在较高的消耗率方案中，地震活动率不会降低，尽管由于有效样本量小，所以无法做出这种统计上可靠的陈述。尽管需要增加基于物理的有限限制，以避免非物理预测，但所提出的方法可用于进行调查的5年以上的预测。