A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models
Abstract As 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 w...
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| Format: | Article |
| Language: | English |
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Wiley
2024-05-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003764 |
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| author | Jia‐Nan Jiang Zi‐Ming Zou Yang Lu Jia Zhong Yong Wang Yu‐Zhang Ma Bian‐Long Zhao |
| author_facet | Jia‐Nan Jiang Zi‐Ming Zou Yang Lu Jia Zhong Yong Wang Yu‐Zhang Ma Bian‐Long Zhao |
| author_sort | Jia‐Nan Jiang |
| collection | DOAJ |
| description | Abstract As 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. |
| format | Article |
| id | doaj-art-5160a32b7a794c0abcbdb53c3eac58e3 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-5160a32b7a794c0abcbdb53c3eac58e32025-01-14T16:27:30ZengWileySpace Weather1542-73902024-05-01225n/an/a10.1029/2023SW003764A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation ModelsJia‐Nan Jiang0Zi‐Ming Zou1Yang Lu2Jia Zhong3Yong Wang4Yu‐Zhang Ma5Bian‐Long Zhao6National Space Science Center CAS Beijing ChinaNational Space Science Center CAS Beijing ChinaNational Space Science Center CAS Beijing ChinaNational Space Science Center CAS Beijing ChinaShandong Provincial Key Laboratory of Optical Astronomy and Solar‐Terrestrial Environment Weihai ChinaShandong Provincial Key Laboratory of Optical Astronomy and Solar‐Terrestrial Environment Weihai ChinaShandong Provincial Key Laboratory of Optical Astronomy and Solar‐Terrestrial Environment Weihai ChinaAbstract As 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.https://doi.org/10.1029/2023SW003764auroraevolutionsubstormdeep learningsegmentation |
| spellingShingle | Jia‐Nan Jiang Zi‐Ming Zou Yang Lu Jia Zhong Yong Wang Yu‐Zhang Ma Bian‐Long Zhao A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models Space Weather aurora evolution substorm deep learning segmentation |
| title | A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models |
| title_full | A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models |
| title_fullStr | A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models |
| title_full_unstemmed | A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models |
| title_short | A Superposed Epoch Analysis of Auroral Oval Coverage During Substorms Using Deep Learning‐Based Segmentation Models |
| title_sort | superposed epoch analysis of auroral oval coverage during substorms using deep learning based segmentation models |
| topic | aurora evolution substorm deep learning segmentation |
| url | https://doi.org/10.1029/2023SW003764 |
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