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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃致展 | zh_TW |
| dc.contributor.advisor | Jyh-Jaan Huang | en |
| dc.contributor.author | 林均庭 | zh_TW |
| dc.contributor.author | Jun-Ting Lin | en |
| dc.date.accessioned | 2024-07-30T16:10:43Z | - |
| dc.date.available | 2024-07-31 | - |
| dc.date.copyright | 2024-07-30 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-28 | - |
| dc.identifier.citation | Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459.
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S., & Prentice, C. S. (1996). Introduction to special section: Paleoseismology. Journal of Geophysical Research: Solid Earth, 101(B3), 5847-5853. Zhao, D., Huang, Z., Umino, N., Hasegawa, A., & Kanamori, H. (2011). Structural heterogeneity in the megathrust zone and mechanism of the 2011 Tohoku‐oki earthquake (Mw 9.0). Geophysical Research Letters, 38(17). Zhao, D., Yanada, T., Hasegawa, A., Umino, N., & Wei, W. (2012). Imaging the subducting slabs and mantle upwelling under the Japan Islands. Geophysical Journal International, 190(2), 816-828. Ziegler, M., Jilbert, T., de Lange, G. J., Lourens, L. J., & Reichart, G. J. (2008). Bromine counts from XRF scanning as an estimate of the marine organic carbon content of sediment cores. Geochemistry, Geophysics, Geosystems, 9(5). | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93370 | - |
| dc.description.abstract | 諸如2011年東日本大地震等巨型隱沒帶地震對人類社會具有深遠的影響，這類地震通常具有較長的再現週期，短時間尺度的儀器及歷史記錄常無法一窺其再現模型之全貌。為了建立可信的長時間古地震記錄，國際海洋發現計畫 (IODP)第386航次針對日本海溝沿線之11個盆地採集了超過800公尺的沉積物岩芯，其中的事件層記錄對於重建該區古地震的時空分布至關重要。因此，本研究利用X光螢光掃描結合多變量統計之主成分和群集分析，在中部日本海溝建立了精確且詳盡的化學地層對比。其中，在同個盆地的岩芯M0083D和M0089D共有八個事件層可被對比，這些事件層可被分為兩類，分別為擁有較高的鈣和鍶且其變化趨勢為向上漸少的事件層，以及擁有較高的矽、銣、鉀且無向上減少趨勢的事件層。這兩類不同的化學特徵指示著事件層成分和沉積過程的差異。而在岩芯M0089D與跨盆地岩芯M0090D的化學地層對比結果顯示，儘管兩根岩芯的八個事件層可被對比，但其元素趨勢和內部結構有所差異，這代表事件層的來源在空間上相似，但其侵蝕和搬運的過程有所不同。此外，不同時間沉積的事件層擁有不同的化學特徵，這不僅暗示了其時間上的來源差異，且進一步指示觸發這些濁流的機制可能是表層沉積物再搬運，而非海底山崩。因此，透過化學地層對比可以識別來自同一事件的沉積物，這證明了化學地層對比在確定事件地層來源上的實用性。在此基礎之上，岩芯中事件層的厚度與空間分布可用以揭示濁流的流動路徑，並有助於確定古地震的可能震央。例如，有高含量矽、銣、鉀的事件層向南變薄，暗示濁流可能源於北方；而有較高含量鈣和鍶的事件層在南方顯著增厚，表明震央可能位於南方。本研究採用X光螢光掃描和多變量統計方法進行精確的化學地層對比，不僅揭示了中部日本海溝事件層的沉積過程和來源，且深化了我們對該地區古地震時空分佈的理解。此外，本研究也為全球其他類似地質環境的古地震學研究提供了重要參考。 | zh_TW |
| dc.description.abstract | Megathrust earthquakes in subduction zones, such as the AD 2011 Mw 9.1 Tohoku-oki earthquake, have a significant impact on human society. Such earthquakes usually occur cyclically with long recurrence intervals, which makes it challenging to reconstruct recurrence models by short instrumental and historical records. Holding the mission to extend the long-term giant paleo-earthquake record over wide spatial extents, the International Ocean Discovery Program (IODP) Expedition 386, Japan Trench Paleoseismology, retrieved over 800 meters of sediment cores containing event deposits from 11 trench-fill basins along the Japan Trench. To establish reliable paleo-earthquake records, the spatiotemporal distributions of event deposits through precise correlation are pivotal. This study examines chemostratigraphic correlations in the central Japan Trench, focusing on cores M0083D and M0089D in the northern basin and M0090D in the southern basin. Employing X-ray Fluorescence Core Scanning (XRF-CS) along with Principal Component Analysis (PCA) and Cluster Analysis (CA), efficient and high-resolution chemostratigraphic correlations were achieved. Within the same basin, eight thick event deposits in M0083D are chemically correlated with those in M0089D. These event deposits can be characterized by higher Ca and Sr with decreasing trends upward, or higher Si, Rb, and K without decreasing trends upward, indicating different compositions and depositional processes of the turbidity current. Across basins, eight event deposits in M0090D showed the same cluster sequence but different elemental trends and internal structures compared to their correlated ones in M0089D, suggesting that while the source of the deposits is spatially similar, the erosion and transport processes may be different. On the other hand, the distinct chemical characteristics of event deposits from different times suggest temporal source differences along stratigraphic sequences. These temporal differences may indicate surficial sediment remobilization rather than landslides as the primary triggering mechanism of turbidity currents. This concept may further confirm the reliability of chemostratigraphic correlation for event-stratigraphic correlation especially in the context of paleoseismology. The relationship between event deposit thickness and spatial distribution across the cores can be further used to indicate the paths of turbidity currents and, thus, potential rupture areaof paleo-earthquakes. Southward thinning of higher Si, Rb, and K event deposits suggests a northern source, while thicker Ca and Sr deposits in the southern core imply southern rupture area. This study therefore establishes a robust chemostratigraphic correlation that enhances our understanding of the spatiotemporal distribution of paleo-earthquakes in the central Japan Trench and provides valuable insights for paleoseismology research in similar geological settings globally. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-07-30T16:10:43Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-07-30T16:10:43Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 論文口試委員審定書 i
誌謝 ii 中文摘要 iii Abstract iv List of Figures x List of Tables xiv 1. Introduction 1 2. Literature Review 6 2.1 Subduction Zone Submarine Paleoseismology 6 2.2 Event Deposits Identification and Characterization 9 2.3 Event-Stratigraphic Correlation 13 3. Background 18 3.1 Geological Setting 18 3.2 Japan Trench Paleoseismology 21 3.3 Study Sites 24 4. Method 30 4.1 X-ray Fluorescence Core Scanning (XRF-CS) 30 4.2 Scanning Campaign 33 4.2.1 The Procedure of XRF-CS 33 4.2.2 Incomplete Sediment Part Exclusion 33 4.2.3 Duplicate Scanning 34 4.2.4 Spectrum Fitting 35 4.3 Quality Control 35 4.3.1 Depth Correction and Invalid Data Removal 36 4.3.2 Element Selection 36 4.3.3 Incomplete Sediment Parts Removal 37 4.4 Data Transformation 39 4.4.1 Dividing by the Mean (dmean) 39 4.4.2 Centred Log-ratio (clr) 40 4.5 Multivariate Statistics 42 4.5.1 Principal Component Analysis (PCA) 42 4.5.2 Cluster Analysis (CA) 43 5. Result 44 5.1 Quality Control 44 5.1.1 Element Selection 45 5.1.2 Incomplete Sediment Parts Removal 51 5.2 Data Transformation 53 5.3 Dimensionality and Cluster Selection in Multivariate Statistics 59 5.3.1 Dimensionality Selection in PCA 59 5.3.2 Cluster Selection in K-means Clustering 62 5.4 Event Deposit Identification 65 5.5 Results of M0083D 67 5.5.1 Background Sediments (Orange Cluster) in M0083D 71 5.5.2 Mn Peaks (Green Cluster) in M0083D 72 5.5.3 Event Deposit 83-TD1 (Purple Cluster) 74 5.5.4 Event Deposit 83-TD2 (Blue Cluster) 76 5.5.5 Event Deposit 83-TD3 (Red Cluster) 78 5.5.6 Event Deposit 83-TD4 (Red Cluster) 80 5.5.7 Event Deposit 83-TD5 (Red and Blue Clusters) 82 5.5.8 Event Deposit 83-TD6 (Purple Cluster) 84 5.5.9 Event Deposit 83-TD7 (Red Cluster) 86 5.5.10 Event Deposit 83-TD8 (Blue Cluster) 88 5.6 Results of M0089D 92 5.6.1 Background Sediments (Orange/Purple/Red Clusters) in M0089D 96 5.6.2 Mn Peaks (Green Cluster) in M0089D 96 5.6.3 Overview of Event Deposits in M0089D 97 5.6.4 Event Deposit 89-TD1 (Purple Cluster) 98 5.6.5 Event Deposit 89-TD2 (Purple and Blue Cluster) 100 5.6.6 Event Deposit 89-TD3 (Red Cluster) 102 5.6.7 Event Deposit 89-TD4 (Red and Purple/Blue Cluster) 104 5.6.8 Event Deposit 89-TD5 (Red and Blue Clusters) 106 5.6.9 Event Deposit 89-TD6 (Purple Cluster with Blue Cluster Base) 108 5.6.10 Event Deposit 89-TD7 (Red Cluster) 110 5.6.11 Event Deposit 89-TD8 (Blue Cluster) 112 5.7 Results of M0090D 115 5.7.1 Background Sediments (Orange/Red Clusters) and Mn Peaks (Green Cluster) in M0090D 119 5.7.2 Event Deposit 90-TD1 (Blue Cluster) 120 5.7.3 Event Deposit 90-TD2 (Blue Cluster) 122 5.7.4 Event Deposit 90-TD3 (Red Cluster) 124 5.7.5 Event Deposit 90-TD4 (Red and Purple Cluster) 126 5.7.6 Event Deposit 90-TD5 (Red Cluster) 128 5.7.7 Event Deposit 90-TD6 (Blue Cluster with Purple Cluster Top) 130 5.7.8 Event Deposit 90-TD7 (Red Cluster) 132 5.7.9 Event Deposit 90-TD8 (Blue Cluster) 133 5.8 Chemostratigraphic Correlation within a Basin - M0083D and M0089D 136 5.8.1 Multivariate Statistics Result of M0083D and M0089D 137 5.8.2 Chemostratigraphic Correlation Results of M0083D and M0089D 140 5.9 Chemostratigraphic Correlation across Basins - M0089D and M0090D 144 5.9.1 Multivariate Statistics Result of M0089D and M0090D 145 5.9.2 Chemostratigraphic Correlation Results of M0089D and M0090D 150 6. Discussion 155 6.1 Feasibility of XRF-CS Combined with Multivariate Statistics for Chemostratigraphic Correlation 156 6.1.1 Feasibility of Chemostratigraphic Correlation within a Site: M0083B and M0083D 157 6.1.2 Feasibility of Chemostratigraphic Correlation within a Basin: M0083D and M0089D 157 6.1.3 Feasibility of Chemostratigraphic Correlation across Basins: M0089D and M0090D 158 6.1.4 Advantages 159 6.1.5 Limitation 160 6.2 Application of Chemostratigraphy – Example of Sample Strategy 163 6.3 Composition of Event Deposits and Background Sediments 169 6.3.1 Composition of Background Sediments 169 6.3.2 Composition of Event Deposit in Different Clusters 171 6.3.3 Differentiation between Homogeneous Clay of Event Deposit and Background Sediments 173 6.4 Possible Depositional Processes of Event Deposits 174 6.4.1 Type 1: 83-TD1, 89-TD1, 83-TD2, 89-TD2, 83-TD6, and 89-TD6 175 6.4.2 Type 2: 83-TD8 and 89-TD8 177 6.4.3 Type 3: 83-TD3, 89-TD3, 83-TD4, 89-TD4, 83-TD5, 89-TD5, 83-TD7, and 89-TD7 178 6.5 Possible Temporal Source Differences Inferred by Chemical Characteristics Relationships of Event Deposits 180 6.6 Event-stratigraphic Correlation and Inferred Possible Spatial Distribution of Paleo-earthquakes 183 6.6.1 Correlating Event Deposits with Earthquakes 183 6.6.2 The Spatial Distribution of Earthquake Inferred by Thickness of Correlated Event Deposits 187 7. Conclusion 196 8. References 201 | - |
| dc.language.iso | en | - |
| dc.title | X光螢光掃描結合多變量統計之化學地層對比在中部日本海溝之古地震學應用 | zh_TW |
| dc.title | XRF-CS-based Multivariate Statistical Chemostratigraphic Correlation for Submarine Paleoseismology in the Central Japan Trench | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | Michael Strasser;池原研;張詠斌;蘇志杰 | zh_TW |
| dc.contributor.oralexamcommittee | Michael Strasser;Ken Ikehara;Yuan-Pin Chang;Chih-Chieh Su | en |
| dc.subject.keyword | X光螢光掃描,多變量統計,對比,日本海溝,古地震學, | zh_TW |
| dc.subject.keyword | XRF Core Scanning,Multivariate Statistics,Correlation,Japan Trench,Submarine Paleoseismology, | en |
| dc.relation.page | 217 | - |
| dc.identifier.doi | 10.6342/NTU202401026 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-07-28 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 海洋研究所 | - |
| 顯示於系所單位： | 海洋研究所 | |
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