Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H–L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only three discharges are initially labeled by the user. Lower yet reasonable performance (∼75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

物理事件数据库已用于各种融合研究应用程序中，包括缩放定律的开发和避免干扰算法，但构建起来可能既耗时又乏味。本文介绍了标签扩展半监督学习算法的一种新颖应用，它通过检测大型放电数据集中的不同事件来加速此过程，给出了几个手动标记的示例。在DIII-D数百次放电的数据集上，通过手动识别的事件显示了H–L向后过渡和初始旋转锁定模式的高检测精度（> 85％），用户最初仅对其标记了三个放电。对于核心辐射塌陷也证明了较低但合理的性能（〜75％），该事件在数据集中的患病率低得多。此外，对性能敏感性的分析表明，对于每个事件，相同的算法参数集是最佳的。这表明该方法可用于检测本文中未包括的各种其他事件，因为该事件已被可在许多放电中可靠使用的一组0D信号很好地描述了。演示了分析新事件的过程，显示了随着用户策略性地添加手动标记的示例而以更高的保真度进行自动事件检测的过程。还显示了对Alcator C-Mod和EAST的检测，表明了将其用于多托卡马克数据集的潜力。这表明该方法可用于检测本文中未包括的各种其他事件，因为该事件已被可在许多放电中可靠使用的一组0D信号很好地描述了。演示了分析新事件的过程，显示了随着用户策略性地添加手动标记的示例而以更高的保真度进行自动事件检测的过程。还显示了对Alcator C-Mod和EAST的检测，表明了将其用于多托卡马克数据集的潜力。这表明该方法可用于检测本文中未包括的各种其他事件，因为该事件已被可在许多放电中可靠使用的一组0D信号很好地描述了。演示了分析新事件的过程，显示了随着用户策略性地添加手动标记的示例而以更高的保真度进行自动事件检测的过程。还显示了对Alcator C-Mod和EAST的检测，表明了将其用于多托卡马克数据集的潜力。