博碩士論文 103690606 詳細資訊




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姓名 庫馬(Utpal Kumar)  查詢紙本館藏   畢業系所 國際研究生博士學位學程
論文名稱 地球物理資料分析在地震學與測地學的應用
(Seismological and Geodetic Applications of Geophysical Data Analysis)
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摘要(中) 地球物理觀測涵蓋廣泛的時間尺度範圍,從地震所產生能量的瞬間變化到地質時間尺 度的板塊變形。本論文呈現探索不同時間尺度工具、複雜模型、資料迭代分析過程在 地震學與大地測量學的應用。

首先我們探索地震資料應用的過程,並使用三個研究結果來闡述與反演移動振源與地 球構造的特徵性質。在第一項研究中,透過地震波和聲下波陣列數據,我們分辨出 2013 年台灣北海岸淡水鎮的神秘爆炸聲源為流星衝擊波信號,並應用基因演算法反演 隕石軌跡的最佳解。在第二項研究中,針對印度最西部省份古吉拉特邦的雷利波相速 度在 20 到 90 秒的寬頻範圍內進行相速度異常圖的分析,我們使用計算所得測站間的 頻散曲線,將每個周期獨立地反演成高解析度均向性與隨著方位角變化的非均向性相 速度圖,結果與已知同區域的地質構造特徵相當吻合。在第三項研究中,我們開發了 一個全自動程式庫進行接收函數與剪力波分離的計算,唯一手動部分是使用者提供輸 入參數。此程式庫從全球可用的資料中心搜尋與下載資料並分別計算接收函數與剪力 波分離的結果,產生具出版品質的圖集。此程式庫已應用在 USArray 資料進行接收函 數分析與德國的測站網資料進行剪力波分離測量。

最後,我們對連續全球定位系統資料(CGPS)中的高振幅、長周期、空間相干共模誤差 (CME)的起源進行研究,分析台灣 47 個 GPS 測站所記錄到十年的日地殼變形資料來了 解 CME 的起源,其季節性證明了氣象起源。使用經驗正交函數(EOFs)分析提取 CME,發現 CME 與時域和譜域中的大氣質量負荷位移顯著相關。
摘要(英) Geophysical observations, ranging from transient earthquake oscillations to tectonic deformations at geological scales, lie in a broad temporal spectrum. The work presented in this dissertation explores the asynchronous tools and models for the complex and iterative data analysis procedures applied in the seismology and geodesy.

First, we explore the procedures applied to the seismic data and illustrate using three studies to characterize and invert for the moving source and the Earth structure. In the first study, the mysterious explosion sounds heard in the coastal town of Tamsui in Taiwan in 2013 was identified in the seismic and infrasound array data and characterized as a meteor shockwave signal and the trajectory of the meteor is inverted using Genetic Algorithm optimization scheme. In the second study, Rayleigh wave phase velocity anomaly maps of Gujarat, a westernmost province in India, is explored in a broad spectrum of 20-90s. The computed inter-station dispersion curves are inverted for high-resolution isotropic and azimuthally anisotropic phase velocity maps at each period independently, coinciding well with the known geological features in the region. In the third study, a fully automated package is developed (in Python) to conduct the Receiver Functions (RF) and Shear-wave Splitting (SWS) computation for the user-provided input parameters (the only manual part). The dataset is automatically searched and downloaded from all the available data centers around the world and is processed, and computed for RF and SWS results independently along with high-resolution figures. The package is applied to the USArray data for the RF analysis and the networks around Germany for SWS measurements.

Finally, a study is conducted to understand the origin of the high amplitude, long period, and spatially coherent common-mode error (CME) in continuous GPS (CGPS) data. Ten years of daily crustal deformations recorded at 47 CGPS stations in Taiwan are analyzed to understand the origin of CME whose seasonality evidences meteorological origin. CME is extracted using the Empirical Orthogonal Functions (EOFs) analysis and found to be significantly correlated with the atmospheric mass loading displacements in both temporal and spectral domains.
關鍵字(中) ★ 地震学
★ 大地测量学
★ 流星冲击波
★ 表面波层析成像
★ 共模误差
★ STADIUMpy
關鍵字(英) ★ Seismology
★ Geodesy
★ Meteor Shockwave
★ Surface wave Tomography
★ Common-mode Error
★ STADIUMpy
論文目次 Chinese Abstract i
English Abstract ii
Acknowledgements iii
List of Figures x
List of Tables xiv
1 Introduction 1
1.1 Motivation 1
1.2 Structure of the Thesis 2
2 Methods and Tools 5
2.1 Least-squares method 5
2.1.1 Earthquake location in homogeneous medium 5
2.1.2 Monte Carlo Methods 11
2.1.3 Genetic Algorithm 13
2.2 Wavelet Transform Vs Fourier Transform 17
2.3 Principal Component Analysis (PCA) and Empirical Orthogonal Functions (EOFs) analysis 19
2.3.1 PCA analysis 19
2.3.2 Empirical Orthogonal Functions analysis 21
2.3.3 Formulation and computation of EOFs 22
2.3.4 EOF analysis of Meinong Earthquake 23
3 A meteor shockwave event recorded at seismic and infrasound stations in northern Taiwan 26
Abstract 26
3.1 Background and Significance 26
3.2 Physics of shockwave production and subsequent ground coupling 27
3.3 Data 29
3.4 Methods 31
3.4.1 Time-frequency analysis and event recognition 31
3.4.2 Inversion for trajectory parameters 34
3.4.3 Implementation of Genetic Algorithm (GA) 35
3.5 Results 36
3.6 Discussion 38
3.7 Conclusions 40
4 Anisotropic Rayleigh Wave Phase Velocity Maps of Gujarat, India 41
4.1 Introduction 41
4.1.1 Surface waves and dispersion 43
4.1.2 Phase and group velocity dispersion 44
4.2 Seismic Data and Methodology 46
4.3 Seismic Stations and Selected Earthquakes 46
4.4 Phase Velocity Dispersion Curves 47
4.5 Inversion for Rayleigh Wave Phase Velocity Maps 53
4.5.1 Estimating the optimum value of damping and smoothing constraints 53
4.5.2 Resolution tests 56
4.6 Results 56
4.6.1 Isotropic Variations 57
4.6.2 The 2ψ Anisotropic Variations 59
4.7 Discussions 60
4.8 Conclusions 63
5 STADIUM-Py: A Python-based automated software package for receiver function and shear-wave splitting analyses 64
Abstract 64
5.1 Introduction 64
5.2 Methods 66
5.2.1 Waveforms and metadata retrieval 68
5.2.2 Receiver functions method 69
5.2.3 Shear-wave splitting (SWS) 73
5.3 Results 78
5.3.1 RFs for USArray stations 78
5.3.2 SWS measurements at stations around Germany 84
5.4 Discussion and Conclusions 90
6 What Causes the Common-Mode Error in Array GPS Displacement Fields: Case Study for Taiwan in Relation to Atmospheric Mass Loading 93
Abstract 93
6.1 Introduction 93
6.2 Data Preparation 96
6.2.1 Continuous GPS Data 96
6.2.2 CME and the seasonality of the GPS residuals 100
6.2.3 Atmospheric mass loading (AML) data 104
6.3 Results 105
6.3.1 EOF of GPS residuals to extract CME 105
6.3.2 Comparison of CME with the AML residuals 108
6.4 Discussions 118
6.5 Conclusions 122
7 Conclusions 123
7.1 Summary 123
7.2 Future works 125
8 Appendix 127
8.1 Earthquake location problem 127
8.1.1 Generalized inverse solution (Section 2.1.1) 127
8.1.2 Solution using Monte Carlo method (2.1.2) 128
8.1.3 Solution using Genetic Algorithm (2.1.3) 129
8.2 Shear wave splitting measurements around Germany (5.3.2) 132
8.3 Modeling continuous GPS position time series 138
8.3.1 Interpolate time series to obtain daily samplings 138
8.3.2 Least squares fit to the CGPS data 139
8.4 STADIUM-Py 142
8.4.1 STADIUM-Py User Instructions 142
8.4.2 Some other auto-generated results 145
Bibliography 148
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指導教授 趙丰 陳伯飛(Benjamin Fong Chao Po-Fei Chen) 審核日期 2020-11-24
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