Effective identification of induced seismicity and real-time management of seismic risks are hot topics due to increasing induced seismicity in areas related to energy exploitation. Existing decision-making tool for managing seismic risks, known as the traffic light system, is not robust enough. To meet the increasing needs for safe mining of energy at production sites, finding an advanced and efficient method to improve the traffic light system is essential. In recent years, machine learning, an advanced inductive and analytical method, has been widely used in seismology. In this context, research gaps associated with the identification and management of induced seismicity, as well as the current achievements of machine learning in addressing induced seismicity problems, are reviewed. A basic framework of using machine learning method to optimize the traffic light system in the industrial production process is first proposed. Then, its feasibility and rationality are demonstrated by similar cases. This framework may provide a reference for the development of a risk-based adaptive traffic light management system.

由于与能源开发有关的地区的地震活动性不断提高，有效识别地震活动性和实时管理地震风险成为热门话题。现有的用于管理地震风险的决策工具（称为交通信号灯系统）不够强大。为了满足生产现场安全开采能源的不断增长的需求，找到一种先进且有效的方法来改善交通信号灯系统至关重要。近年来，机器学习是一种先进的归纳和分析方法，已广泛用于地震学中。在这种情况下，回顾了与感应地震活动的识别和管理相关的研究差距，以及机器学习在解决感应地震活动问题方面的最新成就。首先提出了使用机器学习方法优化工业生产过程中交通信号灯系统的基本框架。然后，通过类似的案例证明了其可行性和合理性。该框架可以为基于风险的自适应交通灯管理系统的开发提供参考。