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タイトル: | Statistical Analysis of Medium-Scale Traveling Ionospheric Disturbances Over Japan Based on Deep Learning Instance Segmentation |
著者: | Liu, Peng Yokoyama, Tatsuhiro https://orcid.org/0000-0001-8392-6455 (unconfirmed) Fu, Weizheng Yamamoto, Mamoru |
著者名の別形: | 劉, 鵬 横山, 竜宏 傅, 維正 山本, 衛 |
キーワード: | MSTID ionospheric irregularity wavelike perturbation statistical analysis deep learning instance segmentation |
発行日: | Jul-2022 |
出版者: | American Geophysical Union (AGU) |
誌名: | Space Weather |
巻: | 20 |
号: | 7 |
論文番号: | e2022SW003151 |
抄録: | Medium-scale traveling ionospheric disturbances (MSTIDs) are observed as parallelly arrayed wavelike perturbations of Total Electron Content (TEC) in ionospheric F region leading to satellite navigation error and communication signal scintillation. The observation method for MSTIDs, detrended TEC (dTEC) map, summarizes the perturbation component of TEC having the merits of full-time and two-dimensional. However, previous automatic processing methods for dTEC map cannot discriminate MSTIDs from other irregular ionospheric perturbations intelligently. With the development of artificial intelligence in recent years, deep learning approach is expecting to clarify the controversy of MSTID external dependence (season and solar/geomagnetic activity) under debating for decades. Therefore, this research proposes a real-time processing algorithm for dTEC maps based on Mask Region-Convolutional Neural Network (R-CNN) model of deep learning instance segmentation to detect wavelike perturbations intelligently with an accuracy of about 80% and a processing speed of about 8 fps. Then isolated perturbations are eliminated and only MSTID waveforms are chosen to obtain statistical characteristics of MSTIDs. With this algorithm, we analyzed up to 1, 209, 600 dTEC maps from 1997 to 2019 over Japan automatically and established a database of hourly averaged MSTID characteristics. This research introduces the partial correlation coefficient for the first time to clarify the solar/geomagnetic activity dependence of MSTID characteristics which is independent with each other. |
著作権等: | © 2022. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
URI: | http://hdl.handle.net/2433/276978 |
DOI(出版社版): | 10.1029/2022SW003151 |
出現コレクション: | 学術雑誌掲載論文等 |
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