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Deep-Sea Trenches of the Pacific Ocean

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Deep-Sea Trenches of the Pacific Ocean Deep-Sea Trenches of the Pacific Ocean: a Comparative Analysis of the Submarine Geomorphology Using Data Modeling by GMT, Python and R by Polina Lemenkova June 2, 2021 Electronic copy available at: https://ssrn.com/abstract=3858289 Disclaimer I do certify that the content of this thesis is my own, original work, written by me personally. All maps and cartographic visualization have been plotted by me personally in GMT and QGIS. All graphical illustrations have been plotted by me personally in Python, Octave, LATEX and R. Literature sources are cited where referenced. June 2, 2021 i Electronic copy available at: https://ssrn.com/abstract=3858289 Contents Front Page ............................................. i Disclaimer ............................................. i Table of Content .......................................... ii Abstract .............................................. ix Keywords ............................................. xi Lists xii List of Acronyms ......................................... xii List of Equations ......................................... xv List of Figures ........................................... xvi List of Tools . xviii List of Maps ............................................ xx List of Tables . xxiii Document Outline 1 1 Introduction 2 1.1 Background ......................................... 4 1.1.1 Submarine geomorphology ............................. 4 1.1.2 Seafloor spreading ................................. 6 1.1.3 Historical milestones ................................ 6 1.1.4 Importance ..................................... 7 1.2 Research problem ...................................... 8 1.3 Research gap identification ................................. 9 1.4 Research objective ..................................... 10 1.4.1 General objective .................................. 10 1.4.2 Specific objectives ................................. 10 1.5 Research questions ..................................... 10 1.6 Hypothesis ......................................... 11 1.7 Technical approach ..................................... 12 2 Trenches of the Pacific Ocean 13 2.1 Kuril-Kamchatka Trench .................................. 13 ii Electronic copy available at: https://ssrn.com/abstract=3858289 Contents Contents 2.2 Aleutian Trench ....................................... 18 2.3 Middle America Trench ................................... 21 2.4 Peru-Chile Trench ...................................... 26 2.5 Kermadec Trench ...................................... 30 2.6 Tonga Trench ........................................ 31 2.7 Vanuatu (New Hebrides) Trench .............................. 32 2.8 Vityaz Trench ........................................ 33 2.9 New Britain (Bougainville) Trench ............................. 35 2.10 San Cristobal Trench .................................... 37 2.11 Philippine Trench ...................................... 39 2.12 Manila Trench ........................................ 45 2.13 Ryukyu Trench ....................................... 48 2.14 Palau Trench ........................................ 53 2.15 Yap Trench ......................................... 55 2.16 Mariana Trench ....................................... 57 2.17 Izu-Bonin Trench ...................................... 64 2.18 Japan Trench ........................................ 67 2.19 Puysegur Trench ...................................... 72 2.20 Hikurangi Trench ...................................... 75 3 Methodology 78 3.1 Data ............................................. 78 3.2 Approaches ......................................... 81 3.3 Generic Mapping Tools (GMT) ............................... 83 3.3.1 Bathymetric mapping ................................ 86 3.3.2 Seismic mapping .................................. 88 3.3.3 Velocity mapping .................................. 92 3.3.4 Histogram equalization ............................... 94 3.3.5 Geological mapping ................................ 97 3.3.6 Geoid modelling .................................. 98 3.3.7 3D mesh grid modelling .............................. 99 3.3.8 Gravity modelling .................................101 3.3.9 Automatic digitizing of the cross-section transecting profiles . 103 3.3.10 Modelling 2D statistical histograms . 105 3.3.11 Modelling 3D statistical histograms . 106 3.3.12 Modelling statistical trends of the profiles gradient . 106 3.3.13 Surface modelling .................................108 3.4 AWK ............................................110 3.4.1 Advantages of AWK ................................110 3.4.2 Data pre-processing ................................111 3.4.3 Table reshaping ...................................111 3.5 Octave/MATLAB ......................................114 3.5.1 Advantages of Octave ...............................114 3.5.2 Data processing ...................................115 3.5.3 Data analysis ....................................115 3.6 Quantum GIS ........................................117 3.7 Statistical Algorithms ....................................119 3.8 R language for marine geology ...............................120 iii Electronic copy available at: https://ssrn.com/abstract=3858289 Contents Contents 3.8.1 Analysis of bathymetric data distribution . 120 3.8.1.1 Box plots .................................120 3.8.1.2 Histograms ................................121 3.8.1.3 Violin plots ...............................121 3.8.1.4 Notched boxplots ............................122 3.8.2 KDE ........................................122 3.8.3 Regression analysis .................................123 3.8.4 Comparative geospatial analysis by tectonic plates . 126 3.8.5 Ridgeline, categorywise and circular plots . 127 3.8.6 Dumbbell charts for data pairwise comparison by tectonic plates . 128 3.8.7 Ranking dot plots by data grouping . 129 3.8.8 Ternary diagrams, radar and pairwise double-Y axis charts . 129 3.8.9 Ranking data for variation of the geomorphic steepness . 130 3.8.10 Compositional charts of the determinants variations . 131 3.8.11 Mosaic, silhouette and association plots . 131 3.8.12 Clustering (k-means method), correlation ellipses and data partition . 133 3.8.13 Hexagonal plots and data partition . 134 3.8.14 Correlation analysis of impact factors . 135 3.8.14.1 Pearson correlation . 135 3.8.14.2 Spearman rank correlation coefficient r . 136 3.8.14.3 Kendall t coefficient . 137 3.8.15 Calculation of the normalized steepness angle of the Mariana Trench . 137 3.8.16 Principal Component Analysis (PCA) . 138 3.8.17 Hierarchical cluster analysis ............................138 3.8.18 Scatterplot Matrices ................................140 3.8.19 Exploratory Factor Analysis (EFA) . 142 3.8.20 Analysis of Variance (ANOVA) to assess hypothesis testing . 145 3.8.21 Euler-Venn Diagram ................................147 3.8.22 Word cloud .....................................147 3.9 Python language for marine geology ............................148 3.9.1 Advantages of Python ...............................148 3.9.2 Installing Python libraries .............................150 3.9.3 Data preprocessing .................................150 3.9.4 Pandas: a Python library for creating DataFrames . 151 3.9.5 Python libraries: Matplotlib, NumPy, SciPy, Seaborn, StatsModels, Bokeh . 152 3.9.6 Data Analysis ....................................154 3.9.6.1 Letter-value plots ............................154 3.9.6.2 Violin plots ...............................155 3.9.6.3 Hierarchically-clustered heatmaps . 156 3.9.6.4 Histograms plotted by Python . 156 3.9.6.5 Strip plots by Python . 157 3.9.6.6 Euler-Venn diagrams by Python . 157 3.9.6.7 Regression Analysis . 158 3.9.6.8 Cluster heatmap and hexagonal data distribution and Kernel Density estimation ................................160 3.9.6.9 Correlograms by Python . 160 3.9.6.10 Data distribution .............................162 iv Electronic copy available at: https://ssrn.com/abstract=3858289 Contents Contents 3.9.6.11 Probability of the depths distribution by Kernel Density Estimation (KDE) plots ...............................162 3.9.6.12 Data sorting and grouping . 163 3.9.6.13 Hierarchical dendrogram and clustermap . 164 3.9.6.14 Principal Component Analysis (PCA) . 165 3.9.6.15 3D scatterplot matrices for fata correlation . 166 3.9.6.16 ANOVA .................................167 3.9.6.17 Flowchart network ............................168 3.9.6.18 Visualizing bathymetric pattern by stacked area charts . 168 3.9.6.19 Stacked bar plots .............................169 3.9.6.20 Radar charts for analysis of depth distribution . 170 3.9.6.21 Stacked bar charts for analysis of sediment thickness . 170 3.9.6.22 Pie charts for circular visualization of the bathymetry vs tectonic plates 171 3.9.6.23 Design matrix and model fit summary by Ordinary Least Squares . 171 3.9.6.24 Quantile statistics (QQ) . 172 3.9.6.25 Weighted Least Squares . 174 3.9.6.26 Isotonic regression ............................175 3.9.6.27 Dynamic regression model: SARIMA . 176 3.10 LATEXfor processing bathymetric data ............................177 4 Results 178 4.1 Classification of the submarine geomorphology . 179 4.2 Kuril-Kamchatka Trench ..................................186 4.3 Aleutian Trench .......................................191 4.4 Middle America Trench ...................................194 4.5 Peru-Chile Trench ......................................196 4.6 Kermadec Trench ......................................198 4.7 Tonga Trench ........................................199
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