A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models

Abstract

AbstractAs an effect of solar‐terrestrial activity, the aurora has always been a focus of substorm research. The expansion of the aurora is one of the important characteristics during substorms, which could be directly reflected by the change of auroral oval coverage. However, the related studies were limited because of the absence of a reliable automated method to obtain auroral ovals from a large number of images. In this paper, we propose a new strategy to achieve this. Based on the Segment Anything Model, we design a process for annotating the auroral oval region that requires little manual work. A new aurora segmentation model, HrSeg, is then developed to obtain auroral ovals more efficiently and accurately. Through 5‐fold cross‐validation, it is determined that the average intersection over union, Dice coefficient, and pixel accuracy are all greater than 0.97. Furthermore, images of 590 substorms observed by the Polar satellite Ultraviolet Imager are segmented. We present superposed epoch analyses of the auroral oval coverage calculated from the segmentation results. Generally, the coverage decreases slightly before onset, then rapidly increases for tens of minutes after onset, and finally decreases gradually. Moreover, the auroras in different magnetic local time (MLT) sectors exhibit different evolutions in coverage. It is also revealed that the evolution pattern of auroral coverage depends on interplanetary magnetic field orientations and seasonal conditions. The results quantify the variation of auroral morphology in terms of coverage, which complete the evolution pattern of aurora during substorms and provide a more comprehensive understanding of substorms.