日本地球惑星科学連合2023年大会

講演情報

[E] オンラインポスター発表

セッション記号 P (宇宙惑星科学) » P-EM 太陽地球系科学・宇宙電磁気学・宇宙環境

[P-EM09] Space Weather and Space Climate

2023年5月26日(金) 10:45 〜 12:15 オンラインポスターZoom会場 (2) (オンラインポスター)

コンビーナ:片岡 龍峰(国立極地研究所)、Antti A Pulkkinen(NASA Goddard Space Flight Center)、Mary Aronne中村 紗都子(名古屋大学宇宙地球環境研究所)

現地ポスター発表開催日時 (2023/5/25 17:15-18:45)

10:45 〜 12:15

[PEM09-P05] Accuracy of area detection in Coronal hole detection model using U-net

*藤谷 壮1飯田 佑輔1 (1.新潟大学)

キーワード:コロナホール、コロナホール検出、機械学習

Coronal holes are regions of open magnetic fields which observed as dark areas in the solar corona because of their low density and temperature compared to other coronas. They are the source of high-speed solar wind streams, whose interaction with Earth’s magnetosphere causes geomagnetic storms. In recent years, coronal hole detection methods based on deep learning has been reported by several researches, and detection results are expected to be more accurate than conventional detection methods with image processing technique. On the other hand, although the significant correlation between the coronal hole area and the solar wind parameter has been reported, the model accuracy for coronal hole detection has not been directly evaluated. In this study, we developed a coronal hole detection method using machine learning, evaluated, and improved it focusing on the area of coronal holes.
We developed a coronal hole detection model using U-net, a semantic segmentation model that classifies each pixel to the defined categories. The detection model uses the full-disk EUV images from SDO/AIA as input, and classifies each pixels whether it is a coronal hole. We used 754 images in the 193Å waveband for about two years. Also, we used the coronal hole identification via multi-thermal emission recognition algorithm (CHIMERA;Garton et al., 2017) to create class labels in ground-truth data. CHIMERA analyses multi-thermal images from the AIA/SDO to detect coronal holes with image processing by their intensity ratio across three passbands, e.g. 171Å, 193Å, and 211Å.
We achieved F-score=0.86 and IoU=0.77 for coronal hole detection using U-net. In addition, we evaluated using RMSE to compare the area of each coronal hole in the predicted images and the label images, as a result, we achieved RMSE=406.5[pixel]. In the next experiment, we focused on the normalization method for input data to improve the accuracy of coronal hole detection. Finally, we achieved F-score=0.88, IoU=0.79 and RMSE=316.9[pixel] for coronal hole detection by changing to the normalization method using stretch function.