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研究生： 澤亞爾
Htoo, Zeyar Min Thwin
論文名稱： 利用多元資料架構台灣中部濁水溪地層下陷區之時變三維地表變形模型
Construction of a Time-varying 3D Deformation Model for Land Subsidence Areas Near Choshui River in Central Taiwan Using Multiple Data Sources
指導教授： 景國恩
Ching, Kuo-En
學位類別： 碩士
Master
系所名稱： 工學院 - 自然災害減災及管理國際碩士學位學程
International Master Program on Natural Hazards Mitigation and Management
論文出版年： 2026
畢業學年度： 114
語文別： 英文
論文頁數： 97
中文關鍵詞： 地層下陷 、雲林－彰化 、三維變形建模 、GNSS、InSAR、水準測量、地下水 、奇異頻譜分析 、空間內插 、含水層記憶效應
外文關鍵詞： Land Subsidence, Yunlin-Changhua, 3D Deformation Modeling, GNSS, InSAR, Levelling,Groundwater, Singular Spectrum Analysis, Spatial Interpolation, Aquifer Memory
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    • 台灣中部雲林－彰化地區之地層下陷，對基礎建設、農業生產力及地下水永續性構成重大威脅。本研究整合多源測地與水文地質資料（包含連續GNSS、Sentinel-1 InSAR、水準測量及高密度地下水位觀測），建立2014年至2020年間具高解析度且具時間變化特性的三維地表變形模型。研究方法結合奇異頻譜分析（Singular Spectrum Analysis, SSA）進行時間序列分解，並採用交叉相關與時滯分析，以及徑向基函數 （Radial Basis Functions, RBF）與克利金法（kriging）等空間內插技術。

    奇異頻譜分析可有效分離GNSS與地下水時間序列中的長期趨勢與季節性訊號，並揭示含水層降水位與地表垂直變形之間具有明顯相位延遲的高度相關性。基於此關係建立之經驗方程，可用於預測缺乏直接測地觀測之地下水井位置的地表變形量。此外，部分保留之GNSS測站被用於驗證最終建立之三維變形模型。

    研究成果之變形分布圖與三維模型顯示兩種主要變形機制：（1）乾旱期間深層含水層發生快速且不可逆之壓密；（2）季風補注後淺層含水層出現約1至2公分的季節性回彈。經驗參數之空間內插結果指出，超過70%的總變形量可歸因於受限含水層中黏土層之固結作用，且在厚層黏土區域呈現顯著的空間非均質性與延遲壓密現象（即「含水層記憶效應」）。所建立之時間變化三維模型在表現上可與傳統模型相比擬，除可支援敏感區域之場址風險評估外，亦提供另一種建構此類模型之方法途徑。

    透過連結地表變形型態與地下水抽取循環及含水層性質，本研究強化了適應性地下水管理與基礎設施災害減緩之分析架構。此方法展示多感測資料融合與訊號分解技術在解析複雜非線性地質災害中的應用潛力，並為全球其他三角洲與沖積平原地區提供參考。

    Land subsidence in central Taiwan's Yunlin-Changhua region poses a significant threat to infrastructure, agricultural productivity, and groundwater sustainability. This research develops a high-resolution, time-varying 3D surface deformation model for 2014–2020 by integrating multiple geodetic and hydrogeological datasets—including continuous GNSS, Sentinel-1 InSAR, precise leveling, and dense groundwater level measurements. The methodology combines advanced time-series decomposition using Singular Spectrum Analysis (SSA), cross-correlation and lag analysis, and spatial interpolation techniques such as Radial Basis Functions (RBF) and kriging.

    Singular Spectrum Analysis enables the separation of long-term trends and seasonal signals in both GNSS and groundwater time series, revealing strong, phase-lagged correlations between aquifer drawdown and vertical ground deformation. Empirical equations derived from these relationships are used to predict deformation at groundwater well locations lacking direct geodetic observations. Some of the reserved GNSS Stations are used to validate the final 3D deformation model.

    The resulting maps and 3D model highlight two principal deformation regimes: (1) rapid, irreversible compaction in deep aquifers during drought periods, and (2) seasonal rebound (1–2 cm) in shallow aquifers following monsoon recharge. Spatial interpolation of empirical parameters reveals that over 70% of total deformation is attributed to clay layer consolidation in confined aquifers, with significant spatial heterogeneity and delayed compaction ("aquifer memory") in thick clay zones. The resulting time-varying 3D model is comparable to traditional models, supporting site-specific risk assessment for sensitive zones as well as pointing out another path to be taken to construct such models.

    By linking deformation patterns to groundwater extraction cycles and aquifer properties, this research reinforces a framework for adaptive groundwater management and infrastructure hazard mitigation. The methodology shows the usage of multi-sensor data fusion and signal decomposition techniques in resolving complex, nonlinear geohazards, providing some insights for other deltaic and alluvial regions worldwide.

    Defense Certificate i Abstract ii 摘要 iii ACKNOWLEDGEMENT iv TABLE OF CONTENTS v LIST OF TABLES viii LIST OF FIGURES viii CHAPTER 1. INTRODUCTION 1 1.1 Research Motivation 1 1.2 Research Purpose 1 1.3 Background 2 1.3.1 Land Subsidence in Taiwan: National Context 2 1.3.2 The Yunlin-Changhua Region: Hydrogeology and Subsidence Hotspot 4 1.4 Problem Statement 5 1.4.1 Limitations of Traditional Subsidence Monitoring and Modeling 6 1.4.2 Challenges in Multi-Sensor and Hydrological Data Integration 6 1.4.3 Consequences of Incomplete Modeling for Risk Management 6 1.5 Knowledge Gap 7 1.6 Research Objectives 9 CHAPTER 2. LITERATURE REVIEW 10 2.1 Information Review 10 2.1.1 Key Concepts and Definitions 11 2.1.2 Historical and Regional Context 14 2.2 Paper Reviews 15 2.2.1 Multi-sensor observations and monitoring strategies 16 2.2.2 Subsidence patterns and regional contrasts 17 2.2.3 Hydrogeological interpretation and controlling mechanisms 19 2.2.4 Methodological contributions and implications for this thesis 21 2.3 Theoretical Framework Review 21 2.3.1 Hydro-Mechanical Foundations of Land Subsidence 22 2.3.2 Signal Decomposition and Correlation Analysis 22 2.3.3 Empirical Modeling of Seasonal Parameters 23 2.3.4 Addressing Theoretical and Practical Gaps 24 2.4 Critical Analysis Review 24 2.4.1 Methodological Strengths and Limitations 25 2.4.2 Theoretical and Interpretive Gaps 25 2.4.3 Practical Implications and Unmet Needs 26 2.4.4 Synthesis and Justification for this Research 26 CHAPTER 3. METHODOLOGY 27 3.1 Data Acquisition and Preprocessing 27 3.1.1 GNSS Data 28 3.1.2 Groundwater Level (GWL) Data 29 3.1.3 Levelling Data 29 3.1.4 INSAR Data 30 3.2 Time-Series Decomposition 32 3.2.1 Singular Spectrum Analysis (SSA) Framework 32 3.2.2 Implementation for GNSS and GWL Data 33 3.3 Correlation and Time Lag Analysis 36 3.4 Spatial Interpolation and Model Construction 37 3.5 Model Validation against Reserved GNSS stations 39 3.6 Summary 40 CHAPTER 4. RESULTS AND DISCUSSION 41 4.1 Introduction 41 4.2 Data Quality Assessment and Pre-Analysis Diagnostics 42 4.2.1 Data Coverage and Completeness 42 4.2.2 Uncertainty Quantification and Quality Control 43 4.3 Long-Term and Seasonal Subsidence Patterns 44 4.3.1 Spatial Distribution of Long-Term Subsidence 44 4.3.2 Temporal Evolution and Trends 45 4.3.3 Seasonal Subsidence Dynamics 45 4.3.4 Discussion: Controls and Implications 46 4.4 Groundwater–Subsidence Coupling: Correlation and Lag Analysis 46 4.4.1 Empirical Equations Formulation between Seasonal GNSS and GWL 46 4.4.2 Seasonal Parameters Estimation 58 4.4.3 Seasonal Parameters Mapping 59 4.5 Time-varying 3D Deformation Modeling and Validation 63 4.5.1 Construction and Visualization of the 3D Model 63 4.5.2 Validation of the Results by Using InSAR Findings and Reserved GNSS Stations 68 4.5.3 Discussion: Model Strengths, Limitations, and Sources of Error 71 4.6 Synthesis and Thematic Discussion 71 4.6.1 Integration of Spatial, Temporal, and Mechanistic Findings 72 4.6.2 Comparison with Previous Studies and Broader Context 72 4.6.3 Policy and Management Implications 72 4.6.4 Theoretical Implications and Future Directions 73 4.7 Limitations and Gaps 73 4.7.1 Data Coverage and Resolution 73 4.7.2 Model Parameterization and Physical Representation 74 4.7.3 Integration of Multi-Sensor Data 74 4.7.4 Temporal and Mechanistic Gaps 74 4.7.5 Broader Impacts and Unresolved Hazards 75 4.8 Time Series Extraction at Any Coordinate on the 3D Model 75 4.9 Summary 77 CHAPTER 5. CONCLUSION AND RECOMMENDATION 78 5.1 Conclusion 78 5.2 Future Direction Recommendation 79 REFERENCES 80

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