The confluence of our ability to handle big data, significant increases in instrumentation density and quality, and rapid advances in machine learning (ML) algorithms have placed Earth Sciences at the threshold of dramatic progress. ML techniques have been attracting increased attention within the seismic community, and, in particular, in microseismic monitoring where they are now being considered a game-changer due to their real-time processing potential. In our review of the recent developments in microseismic monitoring and characterisation, we find a strong trend in utilising ML methods for enhancing the passive seismic data quality, detecting microseismic events, and locating their hypocenters. Moreover, they are being adopted for advanced event characterisation of induced seismicity, such as source mechanism determination, cluster analysis and forecasting, as well as seismic velocity inversion. These advancements, based on ML, include by-products often ignored in classical methods, like uncertainty analysis and data statistics. In our assessment of future trends in ML utilisation, we also see a strong push toward its application on distributed acoustic sensing (DAS) data and real-time monitoring to handle the large amount of data acquired in these cases.

我们处理大数据的能力、仪器密度和质量的显着提高以及机器学习 (ML) 算法的快速进步的融合使地球科学处于巨大进步的门槛。ML 技术在地震界引起了越来越多的关注，特别是在微震监测领域，由于其实时处理潜力，它们现在被认为是游戏规则的改变者。在我们对微震监测和表征的最新发展的回顾中，我们发现利用 ML 方法来提高被动地震数据质量、检测微震事件和定位其震源的强烈趋势。此外，它们正被用于诱发地震活动的高级事件表征，例如震源机制确定、聚类分析和预测，以及地震速度反演。这些基于 ML 的进步包括在经典方法中经常被忽略的副产品，例如不确定性分析和数据统计。在我们对 ML 使用的未来趋势的评估中，我们还看到了它在分布式声学传感 (DAS) 数据和实时监控上的应用的强大推动力，以处理在这些情况下获取的大量数据。