探索電離層剖面之變化： 氣輝、電漿不規則體和閃爍指數的全面研究

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姓名

段儀(Yi Duann) 查詢紙本館藏

畢業系所

太空科學與工程研究所

論文名稱

探索電離層剖面之變化：氣輝、電漿不規則體和閃爍指數的全面研究
(Exploring the Variations of Ionospheric Profiles: A Comprehensive Study of Airglow Brightness, Plasma Irregularities and Ionospheric Scintillation)

相關論文

★ 電離層赤道異常區之電子濃度季節性震盪及日變化	★ Development and Validation of an Airglow Photometer for Upper Atmospheric Chemistry
★ Tidal Variability Due to the Quasi-Biennial Oscillation and Ionospheric Responses	★ 自地面觀測氣輝反演氧原子離子光化學模型
★ 福衛三號S4閃爍指數時空變化與潮汐分析	★ 飛鼠號立方衛星電力次系統設計
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★ 適用於小型衛星二階段展開太陽能板的鎖定鉸鏈的結構設計，分析以及測試	★ 中央大學地面系統設計、整測與驗證
★ 太空飛行器電力次系統硬體迴路測試平台之建立	★ 縮裝型小衛星氧原子酬載：實作、功能與環境驗證
★ 應用先進電離層探測儀與類神經網路以建立初步電漿泡預測模型	★ 飛鼠號立方衛星之飛行軟體及韌體設計
★ IDEASSat任務的經驗教訓：大學立方衛星的設計、測試、在軌運行和異常分析	★ 以立方衛星與微衛星進行GNSS-R/RO觀測的可行性研究

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至系統瀏覽論文 (2024-7-31以後開放)

摘要(中)

這篇博士論文由三個相互關聯的專題組成，旨在提升我們對電離層廓線變化和結構的理解。專題一構築了利用氣輝來觀測電離層的基礎，利用中緯度地面觀測之630.0納米氣輝輻射，反演在150-450公里高度範圍內的氧原子離子密度，並且由電離層探測儀DPS-4測得的電子密度廓線對開發的光化學反演模型進行驗證，增進以氣輝體積輻射率作研究全球尺度電離層的可能性。基於這項技術，專題二探討了將反演模型應用於全球衛星觀測之氣輝的方法。由於需要在切面上獲得精確的體積輻射率廓線，幾何校正是一個不可或缺的步驟。為此，我們訓練了深度學習模型以優化阿貝爾(Abel)反演，並重建氣輝強度廓線中大於300公里的上段缺失部分。通過查普曼函數(Chapman)和三種機器學習演算法的比較，以及使用GOLD任務觀測之135.6奈米輝光之剖面進行驗證，我們的系統訓練得以輸出最佳的氣輝厚度與查普曼型態係數組合，這對於提升衛星觀測之氣輝亮度反演電離層組成的精確度至關重要。最後，專題三利用FORMOSAT-7/COSMIC-2得到的電離層閃爍指數(S4)和電子密度廓線觀測數據，來探索地磁活動期間的電離層變化。比較低緯度O/N2密度比與S4指數的分佈變化，O/N2密度比的減少與恢復期間的S4受抑制區域一致。此研究將這些數據與2022年發生的SpaceX磁暴進行對比分析，並且透過在赤道電離層異常峰值距離的變化中提供了快速穿透電場和擾動風場效應對S4指數影響的證據。整合這三個相互關聯的專題，每個專題都建立在前一個專題的成果上，為我們提供了對電離層剖面變化和組成的全面而細緻的理解，包括干擾因素和操作機制。

摘要(英)

This dissertation comprises three interconnected projects, all tailored to advance our understanding of the variations and structure of ionospheric profiles. The first project forms the foundation by using mid-latitudinal 630.0 nm airglow emission to calculate atomic oxygen ions ([O+]) density within an altitude range of 150–450 km. Electron density (Ne) measured by digisonde DPS-4 validates the developed inversion model, unlocking the potential to use the airglow volume emission rate (VER) as ionospheric tracers on a global scale. Building on this foundational knowledge, the second project addresses the challenges of applying the inversion model to global satellite observations. As the accuracy of the VER profile at the tangent point is paramount, it requires geometry calibration. To this end, we deploy deep learning to optimize Abel inversion and reconstruct the missing upper segment (>300 km) of airglow intensity profiles. Through the Chapman function and three training algorithms: GDX (Gradient descent with momentum and adaptive learning rate), SCG (Scaled conjugate gradient), and LM (Levenberg-Marguardt), our system is trained to output an optimal set of slab thickness and type coefficient, essential for accurate global applications. The final project studies the ionospheric scintillation index (S4) and Ne profile observations obtained from the F7/C2 satellite constellation, to explore the variations of the ionospheric profile during geomagnetic events. These data are juxtaposed with a significant geomagnetic storm that affected SpaceX′s 2022 launches. Comparing the variation of the O/N2 ratio to the S4 distribution, a suppression of the S4 occurred when the O/N2 ratio decreased during the recovery phase. Additionally, the analysis illuminates the impacts of prompt penetration electric (PPE) fields and disturbance dynamo (DD) effects on the S4 index, as evidenced in the variation of the EIA crests. In summary, the integration of these three projects, each building on the achievements of the previous, provides a comprehensive and nuanced understanding of the variation and composition of the ionospheric profile, including disruptive factors and operative mechanisms.

關鍵字(中)

★ 電離層
★ 氣輝
★ 電漿不規則體
★ 磁暴
★ 深度學習

關鍵字(英)

★ Ionosphere
★ Airglow
★ Plasma Irregularity
★ Geomagnetic Storm
★ Deep Learning

論文目次

Abstract i
摘要 ii
誌謝 iii
List of Figures v
List of Tables vii
Chapter 1. Introduction 1
1.1 Overview of the Ionosphere 2
1.2 Motivations 5
1.3 Objectives 6
Chapter 2. Literature Review 8
2.1 Theoretical Background on The Ionosphere 8
2.2 Previous Studies 8
2.2.1 Studying The Ionosphere with Airglow Observations and Deep Learning Technique 8
2.2.2 Ionospheric Scintillation and The 2022 SpaceX Strom 9
Chapter 3. Project 1 and 2: Observational Study and Deep Learning-Based Data Reconstruction 12
3.1 Project 1: Retrieval of Ionospheric O+ Density Using Ground-based Airglow Observations 12
3.1.1 Photochemical Inversion Technique of 630.0 nm Emission 12
3.1.2 The Observing System Simulation Experiment (OSSE) and Sensitivity Analysis 20
3.1.3 Results and Comparison 22
3.2 Project 2: Reconstruction of Missing Airglow Intensity Data Using Machine Learning 30
3.2.1 Deep Learning Technique 32
3.2.2 Abel Inversion 36
3.2.3 Photochemical Inversion Technique of 135.6 nm Emission 38
3.2.4 Results and Validation 40
Chapter 4. Project 3: A Report on The 2022 SpaceX Storm and The S4 Index Response During February 3-4 Geomagnetic Event 50
4.1 Data and Methodology 51
4.2 Changes in S4 Distribution and Occurrence 52
4.3 The Depletion and Enhancement of the O/N2 Density ratio 56
4.4 Disturbed Co-located Ne Profiles and GIM-TEC Crests 58
Chapter 5. Discussion and Conclusion 65
Bibliography 68
Appendix 77

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指導教授

張起維(Loren C. Chang)

審核日期

2023-7-31

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