  Fusion power production in tokamaks uses discharge configurations that risk
producing strong Type I Edge Localized Modes. The largest of these modes will
likely increase impurities in the plasma and potentially damage plasma facing
components such as the protective heat and waste divertor. Machine
learning-based prediction and control may provide for online mitigation of
these damaging modes before they grow too large to suppress. To that end, large
labeled datasets are required for supervised training of machine learning
models. We present an algorithm that achieves 97.7% precision when
automatically labeling Edge Localized Modes in the large DIII-D tokamak
discharge database. The algorithm has no user controlled parameters and is
largely robust to tokamak and plasma configuration changes. This
automatically-labeled database of events can subsequently feed future training
of machine learning models aimed at autonomous Edge Localized Mode control and
suppression.
