# Deep-Sea Trenches of the Pacific Ocean: a Comparative Analysis of the Submarine Geomorphology Using Data Modeling by GMT, Python and R

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Deep-Sea Trenches of the Pacific Ocean: a Comparative Analysis of the Submarine Geomorphology Using Data Modeling by GMT, Python and R. 2021, pp.431. 10.5281/zenodo.4892142. hal-03245754

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Submitted on 2 Jun 2021

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Distributed under a Creative Commons Attribution 4.0 International License
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
# 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
# Contents

<table><tr><td>Front Page</td><td>i</td></tr><tr><td>Disclaimer</td><td>i</td></tr><tr><td>Table of Content</td><td>ii</td></tr><tr><td>Abstract</td><td>ix</td></tr><tr><td>Keywords</td><td>xi</td></tr></table>

## Lists

xii

<table><tr><td>List of Acronyms</td><td>xii</td></tr><tr><td>List of Equations</td><td>xv</td></tr><tr><td>List of Figures</td><td>xvi</td></tr><tr><td>List of Tools</td><td>xviii</td></tr><tr><td>List of Maps</td><td>xx</td></tr><tr><td>List of Tables</td><td>xxiii</td></tr></table>

## Document Outline

1

### 1 Introduction

2

<table><tr><td>1.1 Background</td><td>4</td></tr><tr><td>1.1.1 Submarine geomorphology</td><td>4</td></tr><tr><td>1.1.2 Seafloor spreading</td><td>6</td></tr><tr><td>1.1.3 Historical milestones</td><td>6</td></tr><tr><td>1.1.4 Importance</td><td>7</td></tr><tr><td>1.2 Research problem</td><td>8</td></tr><tr><td>1.3 Research gap identification</td><td>9</td></tr><tr><td>1.4 Research objective</td><td>10</td></tr><tr><td>1.4.1 General objective</td><td>10</td></tr><tr><td>1.4.2 Specific objectives</td><td>10</td></tr><tr><td>1.5 Research questions</td><td>10</td></tr><tr><td>1.6 Hypothesis</td><td>11</td></tr><tr><td>1.7 Technical approach</td><td>12</td></tr></table>

### 2 Trenches of the Pacific Ocean

13

<table><tr><td>2.1 Kuril-Kamchatka Trench</td><td>13</td></tr></table>
<table><tr><td>2.2</td><td>Aleutian Trench</td><td>18</td></tr><tr><td>2.3</td><td>Middle America Trench</td><td>21</td></tr><tr><td>2.4</td><td>Peru-Chile Trench</td><td>26</td></tr><tr><td>2.5</td><td>Kermadec Trench</td><td>30</td></tr><tr><td>2.6</td><td>Tonga Trench</td><td>31</td></tr><tr><td>2.7</td><td>Vanuatu (New Hebrides) Trench</td><td>32</td></tr><tr><td>2.8</td><td>Vityaz Trench</td><td>33</td></tr><tr><td>2.9</td><td>New Britain (Bougainville) Trench</td><td>35</td></tr><tr><td>2.10</td><td>San Cristobal Trench</td><td>37</td></tr><tr><td>2.11</td><td>Philippine Trench</td><td>39</td></tr><tr><td>2.12</td><td>Manila Trench</td><td>45</td></tr><tr><td>2.13</td><td>Ryukyu Trench</td><td>48</td></tr><tr><td>2.14</td><td>Palau Trench</td><td>53</td></tr><tr><td>2.15</td><td>Yap Trench</td><td>55</td></tr><tr><td>2.16</td><td>Mariana Trench</td><td>57</td></tr><tr><td>2.17</td><td>Izu-Bonin Trench</td><td>64</td></tr><tr><td>2.18</td><td>Japan Trench</td><td>67</td></tr><tr><td>2.19</td><td>Puysegur Trench</td><td>72</td></tr><tr><td>2.20</td><td>Hikurangi Trench</td><td>75</td></tr><tr><td>3</td><td>Methodology</td><td>78</td></tr><tr><td>3.1</td><td>Data</td><td>78</td></tr><tr><td>3.2</td><td>Approaches</td><td>81</td></tr><tr><td>3.3</td><td>Generic Mapping Tools (GMT)</td><td>83</td></tr><tr><td>3.3.1</td><td>Bathymetric mapping</td><td>86</td></tr><tr><td>3.3.2</td><td>Seismic mapping</td><td>88</td></tr><tr><td>3.3.3</td><td>Velocity mapping</td><td>92</td></tr><tr><td>3.3.4</td><td>Histogram equalization</td><td>94</td></tr><tr><td>3.3.5</td><td>Geological mapping</td><td>97</td></tr><tr><td>3.3.6</td><td>Geoid modelling</td><td>98</td></tr><tr><td>3.3.7</td><td>3D mesh grid modelling</td><td>99</td></tr><tr><td>3.3.8</td><td>Gravity modelling</td><td>101</td></tr><tr><td>3.3.9</td><td>Automatic digitizing of the cross-section transecting profiles</td><td>103</td></tr><tr><td>3.3.10</td><td>Modelling 2D statistical histograms</td><td>105</td></tr><tr><td>3.3.11</td><td>Modelling 3D statistical histograms</td><td>106</td></tr><tr><td>3.3.12</td><td>Modelling statistical trends of the profiles gradient</td><td>106</td></tr><tr><td>3.3.13</td><td>Surface modelling</td><td>108</td></tr><tr><td>3.4</td><td>AWK</td><td>110</td></tr><tr><td>3.4.1</td><td>Advantages of AWK</td><td>110</td></tr><tr><td>3.4.2</td><td>Data pre-processing</td><td>111</td></tr><tr><td>3.4.3</td><td>Table reshaping</td><td>111</td></tr><tr><td>3.5</td><td>Octave/MATLAB</td><td>114</td></tr><tr><td>3.5.1</td><td>Advantages of Octave</td><td>114</td></tr><tr><td>3.5.2</td><td>Data processing</td><td>115</td></tr><tr><td>3.5.3</td><td>Data analysis</td><td>115</td></tr><tr><td>3.6</td><td>Quantum GIS</td><td>117</td></tr><tr><td>3.7</td><td>Statistical Algorithms</td><td>119</td></tr><tr><td>3.8</td><td>R language for marine geology</td><td>120</td></tr></table>
<table><tr><td>3.8.1</td><td>Analysis of bathymetric data distribution</td><td>120</td></tr><tr><td>3.8.1.1</td><td>Box plots</td><td>120</td></tr><tr><td>3.8.1.2</td><td>Histograms</td><td>121</td></tr><tr><td>3.8.1.3</td><td>Violin plots</td><td>121</td></tr><tr><td>3.8.1.4</td><td>Notched boxplots</td><td>122</td></tr><tr><td>3.8.2</td><td>KDE</td><td>122</td></tr><tr><td>3.8.3</td><td>Regression analysis</td><td>123</td></tr><tr><td>3.8.4</td><td>Comparative geospatial analysis by tectonic plates</td><td>126</td></tr><tr><td>3.8.5</td><td>Ridgeline, categorywise and circular plots</td><td>127</td></tr><tr><td>3.8.6</td><td>Dumbbell charts for data pairwise comparison by tectonic plates</td><td>128</td></tr><tr><td>3.8.7</td><td>Ranking dot plots by data grouping</td><td>129</td></tr><tr><td>3.8.8</td><td>Ternary diagrams, radar and pairwise double-Y axis charts</td><td>129</td></tr><tr><td>3.8.9</td><td>Ranking data for variation of the geomorphic steepness</td><td>130</td></tr><tr><td>3.8.10</td><td>Compositional charts of the determinants variations</td><td>131</td></tr><tr><td>3.8.11</td><td>Mosaic, silhouette and association plots</td><td>131</td></tr><tr><td>3.8.12</td><td>Clustering (k-means method), correlation ellipses and data partition</td><td>133</td></tr><tr><td>3.8.13</td><td>Hexagonal plots and data partition</td><td>134</td></tr><tr><td>3.8.14</td><td>Correlation analysis of impact factors</td><td>135</td></tr><tr><td>3.8.14.1</td><td>Pearson correlation</td><td>135</td></tr><tr><td>3.8.14.2</td><td>Spearman rank correlation coefficient ρ</td><td>136</td></tr><tr><td>3.8.14.3</td><td>Kendall τ coefficient</td><td>137</td></tr><tr><td>3.8.15</td><td>Calculation of the normalized steepness angle of the Mariana Trench</td><td>137</td></tr><tr><td>3.8.16</td><td>Principal Component Analysis (PCA)</td><td>138</td></tr><tr><td>3.8.17</td><td>Hierarchical cluster analysis</td><td>138</td></tr><tr><td>3.8.18</td><td>Scatterplot Matrices</td><td>140</td></tr><tr><td>3.8.19</td><td>Exploratory Factor Analysis (EFA)</td><td>142</td></tr><tr><td>3.8.20</td><td>Analysis of Variance (ANOVA) to assess hypothesis testing</td><td>145</td></tr><tr><td>3.8.21</td><td>Euler-Venn Diagram</td><td>147</td></tr><tr><td>3.8.22</td><td>Word cloud</td><td>147</td></tr><tr><td>3.9</td><td>Python language for marine geology</td><td>148</td></tr><tr><td>3.9.1</td><td>Advantages of Python</td><td>148</td></tr><tr><td>3.9.2</td><td>Installing Python libraries</td><td>150</td></tr><tr><td>3.9.3</td><td>Data preprocessing</td><td>150</td></tr><tr><td>3.9.4</td><td>Pandas: a Python library for creating DataFrames</td><td>151</td></tr><tr><td>3.9.5</td><td>Python libraries: Matplotlib, NumPy, SciPy, Seaborn, StatsModels, Bokeh</td><td>152</td></tr><tr><td>3.9.6</td><td>Data Analysis</td><td>154</td></tr><tr><td>3.9.6.1</td><td>Letter-value plots</td><td>154</td></tr><tr><td>3.9.6.2</td><td>Violin plots</td><td>155</td></tr><tr><td>3.9.6.3</td><td>Hierarchically-clustered heatmaps</td><td>156</td></tr><tr><td>3.9.6.4</td><td>Histograms plotted by Python</td><td>156</td></tr><tr><td>3.9.6.5</td><td>Strip plots by Python</td><td>157</td></tr><tr><td>3.9.6.6</td><td>Euler-Venn diagrams by Python</td><td>157</td></tr><tr><td>3.9.6.7</td><td>Regression Analysis</td><td>158</td></tr><tr><td>3.9.6.8</td><td>Cluster heatmap and hexagonal data distribution and Kernel Density estimation</td><td>160</td></tr><tr><td>3.9.6.9</td><td>Correlograms by Python</td><td>160</td></tr><tr><td>3.9.6.10</td><td>Data distribution</td><td>162</td></tr></table>
<table><tbody><tr><td>3.9.6.11</td><td>Probability of the depths distribution by Kernel Density Estimation (KDE) plots</td><td>162</td></tr><tr><td>3.9.6.12</td><td>Data sorting and grouping</td><td>163</td></tr><tr><td>3.9.6.13</td><td>Hierarchical dendrogram and clustermap</td><td>164</td></tr><tr><td>3.9.6.14</td><td>Principal Component Analysis (PCA)</td><td>165</td></tr><tr><td>3.9.6.15</td><td>3D scatterplot matrices for fata correlation</td><td>166</td></tr><tr><td>3.9.6.16</td><td>ANOVA</td><td>167</td></tr><tr><td>3.9.6.17</td><td>Flowchart network</td><td>168</td></tr><tr><td>3.9.6.18</td><td>Visualizing bathymetric pattern by stacked area charts</td><td>168</td></tr><tr><td>3.9.6.19</td><td>Stacked bar plots</td><td>169</td></tr><tr><td>3.9.6.20</td><td>Radar charts for analysis of depth distribution</td><td>170</td></tr><tr><td>3.9.6.21</td><td>Stacked bar charts for analysis of sediment thickness</td><td>170</td></tr><tr><td>3.9.6.22</td><td>Pie charts for circular visualization of the bathymetry vs tectonic plates</td><td>171</td></tr><tr><td>3.9.6.23</td><td>Design matrix and model fit summary by Ordinary Least Squares</td><td>171</td></tr><tr><td>3.9.6.24</td><td>Quantile statistics (QQ)</td><td>172</td></tr><tr><td>3.9.6.25</td><td>Weighted Least Squares</td><td>174</td></tr><tr><td>3.9.6.26</td><td>Isotonic regression</td><td>175</td></tr><tr><td>3.9.6.27</td><td>Dynamic regression model: SARIMA</td><td>176</td></tr><tr><td>3.10</td><td>LATEX for processing bathymetric data</td><td>177</td></tr><tr><td>4</td><td>Results</td><td>178</td></tr><tr><td>4.1</td><td>Classification of the submarine geomorphology</td><td>179</td></tr><tr><td>4.2</td><td>Kuril-Kamchatka Trench</td><td>186</td></tr><tr><td>4.3</td><td>Aleutian Trench</td><td>191</td></tr><tr><td>4.4</td><td>Middle America Trench</td><td>194</td></tr><tr><td>4.5</td><td>Peru-Chile Trench</td><td>196</td></tr><tr><td>4.6</td><td>Kermadec Trench</td><td>198</td></tr><tr><td>4.7</td><td>Tonga Trench</td><td>199</td></tr><tr><td>4.8</td><td>Vanuatu (New Hebrides) Trench</td><td>200</td></tr><tr><td>4.9</td><td>Vityaz Trench</td><td>201</td></tr><tr><td>4.10</td><td>New Britain (Bougainville) Trench</td><td>203</td></tr><tr><td>4.11</td><td>San Cristobal Trench</td><td>204</td></tr><tr><td>4.12</td><td>Philippine Trench</td><td>207</td></tr><tr><td>4.13</td><td>Manila Trench</td><td>213</td></tr><tr><td>4.14</td><td>Ryukyu Trench</td><td>216</td></tr><tr><td>4.15</td><td>Palau Trench</td><td>219</td></tr><tr><td>4.16</td><td>Yap Trench</td><td>221</td></tr><tr><td>4.17</td><td>Mariana Trench</td><td>224</td></tr><tr><td>4.18</td><td>Izu-Bonin Trench</td><td>231</td></tr><tr><td>4.19</td><td>Japan Trench</td><td>234</td></tr><tr><td>4.20</td><td>Puysegur Trench</td><td>237</td></tr><tr><td>4.21</td><td>Hikurangi Trench</td><td>239</td></tr><tr><td>5</td><td>Discussion</td><td>242</td></tr><tr><td>5.1</td><td>Novelty</td><td>242</td></tr><tr><td>5.2</td><td>Actuality</td><td>243</td></tr><tr><td>5.3</td><td>Reflections on submarine geomorphology</td><td>246</td></tr><tr><td>5.4</td><td>Perspectives of the IT tools for geology</td><td>247</td></tr></tbody></table>
<table><tr><td>A.5.18</td><td>Python code for the facetted plot of the radar charts</td><td>xxxvi</td></tr><tr><td>A.5.19</td><td>Python code for the distribution of the bathymetric data by stacked bar plots</td><td>xxxvii</td></tr><tr><td>A.5.20</td><td>Python code for analysis of the distribution of sediment thickness, stacked bar charts</td><td>xxxviii</td></tr><tr><td>A.5.21</td><td>Python code for pie charts visualizing bathymetry vs tectonic plates</td><td>xxxix</td></tr><tr><td>A.5.22</td><td>Python code for QQ plots visualizing descriptive statistics for the bathymetry</td><td>xxxix</td></tr><tr><td>A.5.23</td><td>Python code for WLS calculations</td><td>xl</td></tr><tr><td>A.5.24</td><td>Python code for quantile (QQ) regression</td><td>xlii</td></tr><tr><td>A.5.25</td><td>Python code for SARIMA model</td><td>xliii</td></tr><tr><td>A.5.26</td><td>Python code for Euler-Venn diagrams</td><td>xliv</td></tr><tr><td>A.6</td><td>R language scripts</td><td>xlvi</td></tr><tr><td>A.6.1</td><td>R code to create histograms using (ggplot) library</td><td>xlvi</td></tr><tr><td>A.6.2</td><td>R code to generate boxplots 'whiskerplots' using default boxplot function</td><td>xlvii</td></tr><tr><td>A.6.3</td><td>R code for notched boxplot using (ggboxplot)</td><td>xlviii</td></tr><tr><td>A.6.4</td><td>R code to generate Quantile-Quantile (QQ) quantile statistics for facetted wrapped plot</td><td>xlix</td></tr><tr><td>A.6.5</td><td>R code for regression analysis for the bathymetric profiles</td><td>l</td></tr><tr><td>A.6.6</td><td>R code for cluster analysis, dendrograms</td><td>li</td></tr><tr><td>A.6.7</td><td>R code for correlogram by (ggplot) library</td><td>lii</td></tr><tr><td>A.6.8</td><td>R code for Principal Component Analysis (PCA)</td><td>liii</td></tr><tr><td>A.6.9</td><td>R code for k-means clustering</td><td>liv</td></tr><tr><td>A.6.10</td><td>R code for Pairwise Standard Scatter Plots of k-means Cluster Correlation (comparing 4 tectonic plates)</td><td>lv</td></tr><tr><td>A.6.11</td><td>R code for 'lollipop' charts and diverging bars by (ggplot) library</td><td>lvii</td></tr><tr><td>A.6.12</td><td>R code for Dumbbell chart by (ggalt) and (ggplot2) libraries</td><td>lix</td></tr><tr><td>A.6.13</td><td>R code for categorywise bar charts</td><td>lx</td></tr><tr><td>A.6.14</td><td>R code for dot plot with encircling by (ggalt) library</td><td>lxi</td></tr><tr><td>A.6.15</td><td>R code for double-Y axis using (LatticeExtra) library to perform comparative analysis of categories</td><td>lxiii</td></tr><tr><td>A.6.16</td><td>R code for circular barplot by library tidyverse</td><td>lxiv</td></tr><tr><td>A.6.17</td><td>R code for multiple strip plots divided groups, by LatticeExtra library</td><td>lxvi</td></tr><tr><td>A.6.18</td><td>R code for multiple panels by groups (tectonic plates, depths, slope angles)</td><td>lxvii</td></tr><tr><td>A.6.19</td><td>R code for density curves: data distribution</td><td>lxvii</td></tr><tr><td>A.6.20</td><td>R code to generate brackets for the specific regions along the Mariana Trench using library (pBrackets)</td><td>lxviii</td></tr><tr><td>A.6.21</td><td>R code for radar chart using (fmsb) library</td><td>lxx</td></tr><tr><td>A.6.22</td><td>R code for highlighted group in lollipop chart by libraries (ggsignif) and (tidyverse)</td><td>lxx</td></tr><tr><td>A.6.23</td><td>R code for Euler-Venn Diagram (logical correlation of objects) using (venn) library</td><td>xxii</td></tr><tr><td>A.6.24</td><td>R code for animation using (gganimate), (gapminder) and (ggplot2) libraries: regression analysis plots for 25 bathymetric profiles</td><td>xxiii</td></tr><tr><td>A.6.25</td><td>R code for 'violin' plots using (violinplot) library: statistical analysis of bathymetric data distribution</td><td>xxiv</td></tr><tr><td>A.6.26</td><td>R code for Exploratory Factor Analysis (EFA) using (FactoMiner), (factoextra) and (psych) libraries</td><td>xxv</td></tr><tr><td>A.6.27</td><td>R code for hexagonal plot by library(hexbin): bathymetric data distribution, Mariana Trench</td><td>xxvi</td></tr><tr><td>A.6.28</td><td>R code for ANOVA for Mariana Trench</td><td>xxvii</td></tr><tr><td>A.6.29</td><td>R code for Ternary Diagram by (ggtern) library</td><td>xxviii</td></tr></table>
<table><tbody><tr><td>A.6.30</td><td>R code for silhouette plot by (cluster) library</td><td>lxxx</td></tr><tr><td>A.6.31</td><td>R code for mosaic and association plots</td><td>lxxx</td></tr><tr><td>A.6.32</td><td>R code plot correlation ellipses using library(ellipse)</td><td>lxxxi</td></tr><tr><td>A.6.33</td><td>R code for level plot using (ggplot2) library: sediment thickness</td><td>lxxxi</td></tr><tr><td>A.6.34</td><td>R code for hierarchical edge funding by (ggraph), (igraph), (tidyverse), (RColor-Brewer) libraries: Mariana Trench project</td><td>lxxxii</td></tr><tr><td>A.6.35</td><td>R code for ridgeline plots by (ggridges) and (ggplot2) libraries</td><td>lxxxiii</td></tr><tr><td>A.6.36</td><td>R code for word could by libraries (wordcloud), (tm), (RXKCD): Mariana Trench project</td><td>lxxxiv</td></tr><tr><td>A.6.37</td><td>R code for 'waffle' plot by library (ggplot2): bathymetric data distribution</td><td>lxxxiv</td></tr><tr><td>A.6.38</td><td>R code for 'waffle' plot by library (waffle): bathymetric data distribution</td><td>lxxxv</td></tr><tr><td>A.6.39</td><td>R code for treemap by library (treemap)</td><td>lxxxvi</td></tr><tr><td>A.6.40</td><td>R code for network by (igraph) library: Mariana Trench project</td><td>lxxxvi</td></tr><tr><td>A.6.41</td><td>R code for scatterplot matrices by (GGally) library to assess data distribution, Mariana Trench</td><td>lxxxvii</td></tr><tr><td>A.6.42</td><td>R code for diagonal scatterplot matrices by (car) library</td><td>lxxxix</td></tr><tr><td><strong>G</strong></td><td><strong>Indices</strong></td><td><strong>xc</strong></td></tr><tr><td>G.1</td><td>Index of Places</td><td>xci</td></tr><tr><td>G.2</td><td>Index of Concepts</td><td>xciv</td></tr><tr><td>G.3</td><td>Index of Authors</td><td>xcix</td></tr><tr><td><strong>Bibliography</strong></td><td></td><td><strong>cxii</strong></td></tr></tbody></table>
Abstract

Geomorphological landforms of the oceanic trenches, their formation and variation of the geometric shapes is a question of special importance to the scientific community in marine geology. The actuality of this question has significantly increased since the beginning of the rapid development of the IT tools and methods of the advanced data analysis, yet its understanding remains patchy. Since the majority of the oceanic trenches are located along the margins of the Pacific Ocean, it plays a central role for their analysis and understanding their formation oceanic trenches.

Specific geological conditions, presence of the tectonic subduction zones, vast territory of the Pacific Ocean with complex circulation system, extension of the 'Ring of Fire', a seismically active belt of the earthquakes and volcanic, make the trenches of the Pacific Ocean highly sensitive to the factors affecting their formation which cause variations in their geomorphic shape forms. In this context, the most representative indicators of the variations in the deep-sea trenches are geological and tectonic factors, such as dynamics of lithosphere crust affecting speed and intensity of plates subduction, magnitude and frequency of the submarine volcanoes causing active sedimentation.

Nowadays, studying marine geological phenomena and complex processes by programming and scripting has been a powerful method. Rapid development of the advanced methods of data analysis presented such effective tools as GMT, Octave/MATLAB, R and Python. It is particularly efficient when applied to the massive amounts of marine geological data. Big data processing by advanced scripting is a crucial approach, as algorithms of libraries give access to the accurate and rapid data analysis [373]. Specific information about distant and hard-to-reach deep-sea trenches can be gained for precise visualization and analysis of their submarine geomorphology from local to regional and global scales.

However, despite all the efforts, there is a lack of uniformity in studying deep-sea trenches, a shortage of systematic mapping of the Pacific trenches and a lack of understanding of the geomorphological variation between the trench profiles in different parts of the ocean: southern and northern, eastern and western, and their response to the geological and tectonic local settings in the places of formation.

Therefore, this dissertation develops a systematic approach to monitoring and comparative analysis of the geomorphological shape forms of the deep-sea trenches formed under specific geological and tectonic conditions along the margins of the Pacific Ocean. The study area encompasses Pacific Ocean, and more specifically, includes 20 selected target trenches: Aleutian, Mariana, Philippine, Kuril-Kamchatka, Middle America, Peru-Chile, Palau, Japan, Kermadec, Tonga, Izu-Bonin, New Britain, San Cristobal, Manila, Yap, New Hebrides, Puysegur, Hikurangi, Vityaz and Ryukyu. These are the major trenches of the Pacific Ocean and, therefore, the most representative for the geomorphological modelling.

This dissertation identifies tectonic plates formation, slab subduction, historical geological development, earthquakes and submarine volcanoes as the primary types of impact factors affecting trenches formation. Secondary factors include ocean currents, sedimentation and biota contributing to the sedimentation. Seafloor geomorphology in hadal trenches is strongly affected by a variety of factors that necessarily affect the shape of their landforms. Using data modelling, the shapes of the profiles transecting the trenches in an orthogonal direction were compared and analyzed in order to highlight the differences and variations in their geomorphology.
The objective of this PhD study is to perform a geomorphological classification of the shape forms of the trenches through ordering them into groups based on the common characteristics of the trenches' landforms in plan and attaching labels to these groups. Following geomorphological profile shape types have been identified and trenches are classified into seven types: U-formed (in plan), V-formed (in plan), asymmetric, crescent-formed, sinuous-formed, elongated, cascade-formed. For each type (U, V, asymmetric and so on) characteristic steepness sub-types are identified: strong, very strong, extreme, steep, very steep. Valley slopes are classified as follows: very high, high, moderate, low, based on the curvature degree. Size and valley slope classes are analyzed in the context of physical environment and tectonic degree. Size and valley slope classes are analyzed in the context of physical environment and tectonic and geological development of the area of trench formation.

Technical aim of this PhD study was to experiment with and extend current methods of geospatial modelling for geomorphological classification of the submarine landforms of the trenches. Using methods of the advanced data analysis is crucial for the precise and reliable data processing, since understanding seafloor landforms can only be based on the computer-based data modelling due to their unreachable location. The selection of the methodology, tools and algorithms is explained by research objectives and goals. The specifics of the marine geology consists in the high requirements towards data processing. Datasets were processed, computed and analyzed in semi-automatic regime by Machine Learning (ML) approaches, using advanced algorithms of data analysis and effective visualization through application of the advanced programming tools and Generic Mapping Tools (GMT) scripting toolsets.

This dissertation presents an automated workflow enabling large-scale profile cross-sectioning aimed at transect geomorphological mapping, quantitative comparative analysis and classification of the 20 trenches of the Pacific Ocean. The methodology of the GMT includes algorithms of sequential scripting for the cartographic visualization and mapping, automatic digitizing of the cross-section transect profiles, and geomorphic modelling of the trenches. In total 50 modules of GMT scripting toolset were trained on extensive datasets collected from 20 trenches across the whole region of the Pacific Ocean. Using high-resolution bathymetric datasets (General Bathymetric Chart of the Oceans (GEBCO), ETOPO1 and Shuttle Radar Topographic Mission (SRTM)), sample transects of the trenches were modeled, analyzed and compared. Variations in shape forms, steepness and curvature were analyzed by computed models for each trench. The tables were converted from QGIS plugins to Python libraries and R packages, and from GMT to Octave via AWK languages.

The results revealed variation in the shape and steepness of the submarine geomorphology in 20 trenches of the Pacific Ocean. A strong correlation between the geomorphic profile shapes with geological factors and level of tectonic activities (earthquakes, volcanism, speed of tectonic plate subduction) and the scale of trench steepness, curvature and shape unevenness is confirmed and analyzed. Geomorphological structure of the trenches and dynamics of the tectonic plates subduction are analyzed and assessed at each trench regionally (north, south, west and east Pacific).

The novelty of the study consists in presented systematic classification and comparative modelling of the geomorphic profiles of the deep-sea trenches by means of the sequential usage of the advanced scripting toolsets. Technical innovativeness consists in a combination of GIS, GMT, Python, AWK, R and

The actuality of this dissertation lies in its strongly multi-disciplinary nature demonstrating a combination of the following approaches: 1) systematic multi-source geospatial data analysis; 2) statistical data modelling and processing by libraries of the Python and R, AWK and Octave/Matlab; 3) geological literature analysis; 4) cartographic mapping and modelling by GMT shell scripts and visualization in QGIS. The dissertation contributes to the studies on

Technical scripts used for advanced statistical analysis are presented in full in the Appendix A for future replication and reproducible analysis in other trenches of the World Ocean.
**Keywords**

**Keywords:** Pacific Ocean; geomorphology; bathymetry; Aleutian Trench; Mariana Trench; Philippine Trench; Kuril-Kamchatka Trench; Middle America Trench; Peru-Chile Trench; Palau Trench; Japan Trench; Kermadec Trench; Tonga Trench; Izu-Bonin Trench; New Britain Trench; San Cristobal Trench; Manila Trench; Yap Trench; New Hebrides Trench; Puysegur Trench; Hikurangi Trench; Vityaz Trench; Ryukyu Trench; New Zealand; Central America; R; Python; Matplotlib; NumPy; SciPy; Pandas; Seaborn; geomorphic modelling, cartography, visualization, data analysis, programming language; statistics; spatial analysis; statistical analysis; cluster analysis; factor analysis; marine geology; geomorphology; bathymetry; algorithm; oceanography; GMT; GIS; QGIS, geoinformatics; GEBCO; ETOPO1; geodata; dataset;
# Lists

## List of Acronyms<fnref n="1" />

<table><tr><td>AI</td><td>Artificial Intelligence</td><td>85</td></tr><tr><td>AOI</td><td>Area of Interest</td><td>104</td></tr><tr><td>ASL</td><td>Above Sea Level</td><td>188</td></tr><tr><td>ANOVA</td><td>Analysis of Variance</td><td>iv</td></tr><tr><td>ASCII</td><td>American Standard Code for Information Interchange</td><td>78</td></tr><tr><td>AT</td><td>Aleutian Trench</td><td>18</td></tr><tr><td>BAB</td><td>Back-Arc Basin</td><td>48</td></tr><tr><td>bash</td><td>Bourne Again SHell</td><td>104</td></tr><tr><td>BODC</td><td>British Oceanographic Data Centre</td><td>78</td></tr><tr><td>BSL</td><td>below sea level</td><td>49</td></tr><tr><td>CMT</td><td>Centroid Moment Tensor</td><td>41</td></tr><tr><td>CTAN</td><td>The Comprehensive TEX Archive Network</td><td>177</td></tr><tr><td>CTD</td><td>Conductivity-Temperature-Depth profiler</td><td>8</td></tr><tr><td>DCDB</td><td>Data Center for Digital Bathymetry</td><td>80</td></tr><tr><td>DEM</td><td>Digital Elevation Model</td><td>37</td></tr><tr><td>EPSG</td><td>European Petroleum Survey Group</td><td>117</td></tr><tr><td>GDAL</td><td>Geospatial Data Abstraction Library</td><td>83</td></tr><tr><td>GEBCO</td><td>General Bathymetric Chart of the Oceans</td><td>x</td></tr><tr><td>GIS</td><td>Geographic Information System</td><td>1</td></tr><tr><td>GLM</td><td>Generalized Linear Models</td><td>125</td></tr><tr><td>GMT</td><td>Generic Mapping Tools</td><td>x</td></tr><tr><td>GNU</td><td>GNU's Not Unix</td><td>114</td></tr><tr><td>GPS</td><td>Global Positioning System</td><td>77</td></tr><tr><td>GRASS GIS</td><td>Geographic Resources Analysis Support System</td><td>12</td></tr><tr><td>GSFC</td><td>Goddard Space Flight Center</td><td>99</td></tr><tr><td>GSHHGD</td><td>Global Self-consistent, Hierarchical, High-resolution Geography Database</td><td>78</td></tr><tr><td>GUI</td><td>Graphical User Interface</td><td>83</td></tr><tr><td>GVP</td><td>Global Volcanism Program</td><td>80</td></tr><tr><td>EFA</td><td>Exploratory Factor Analysis</td><td>iv</td></tr><tr><td>EGM96</td><td>Earth Gravitational Model of 1996</td><td>80</td></tr><tr><td>ENVISAT</td><td>Environmental Satellite</td><td>81</td></tr><tr><td>ESRI</td><td>Environmental Systems Research Institute</td><td>81</td></tr><tr><td>ETOPO1</td><td>1 Arc-Minute Global Relief Model of Earth's topography</td><td>3</td></tr><tr><td>ETOPO5</td><td>5 Arc-Minute Global Relief Model of Earth's topography</td><td>80</td></tr><tr><td>FP</td><td>Fault Plane</td><td>91</td></tr></table>

<footnote n="1">¹The page is given where the glossary is first entered and defined</footnote>
<table><tr><td>HCA</td><td>Hierarchical Cluster Analysis</td><td>164</td></tr><tr><td>HSD</td><td>Honest Significant Differences</td><td>145</td></tr><tr><td>HcT</td><td>Hypocenter Time</td><td>90</td></tr><tr><td>HT</td><td>Hikurangi Trench</td><td>75</td></tr><tr><td>IBM</td><td>Izu-Bonin Mariana arc</td><td>41</td></tr><tr><td>IBT</td><td>Izu-Bonin Trench</td><td>64</td></tr><tr><td>IDLE</td><td>Integrated Development and Learning Environment</td><td>150</td></tr><tr><td>IHO</td><td>International Hydrographic Organization</td><td>3</td></tr><tr><td>IRIS</td><td>Incorporated Research Institutions for Seismology</td><td>5</td></tr><tr><td>ISC</td><td>International Seismological Centre</td><td>80</td></tr><tr><td>IT</td><td>Information Technology</td><td>3</td></tr><tr><td>JT</td><td>Japan Trench</td><td>67</td></tr><tr><td>KT</td><td>Kermadec Trench</td><td>30</td></tr><tr><td>KDE</td><td>Kernel Density Estimation</td><td>v</td></tr><tr><td>KKT</td><td>Kuril-Kamchatka Trench</td><td>13</td></tr><tr><td>KMZ</td><td>Keyhole Markup language Zipped</td><td>90</td></tr><tr><td>kwargs</td><td>Keyword arguments</td><td>93</td></tr><tr><td>KPR</td><td>Kyushu-Palau Ridge</td><td>41</td></tr><tr><td>LIPs</td><td>Large Igneous Provinces</td><td>97</td></tr><tr><td>LOESS</td><td>Locally Estimated Scatterplot Smoothing</td><td>119</td></tr><tr><td>OLS</td><td>Ordinary Least Square</td><td>153</td></tr><tr><td>MRC</td><td>Macquarie Ridge Complex</td><td>72</td></tr><tr><td>MAT</td><td>Middle America Trench</td><td>21</td></tr><tr><td>ML</td><td>Machine Learning</td><td>x</td></tr><tr><td>MnT</td><td>Manila Trench</td><td>45</td></tr><tr><td>MORB</td><td>Mid-Ocean Ridge Basalts</td><td>60</td></tr><tr><td>MORVEL</td><td>Mid-Ocean Ridge VELocity</td><td>92</td></tr><tr><td>MT</td><td>Mariana Trench</td><td>32</td></tr><tr><td>NBT</td><td>New Britain Trench</td><td>35</td></tr><tr><td>NGA</td><td>National Geospatial Intelligence Agency</td><td>80</td></tr><tr><td>NGDC</td><td>National Geophysical Data Center</td><td>80</td></tr><tr><td>NetCDF</td><td>Network Common Data Form</td><td>101</td></tr><tr><td>NumPy</td><td>Numerical Python</td><td>8</td></tr><tr><td>NASA</td><td>National Aeronautics and Space Administration</td><td>78</td></tr><tr><td>NIMA</td><td>National Imagery and Mapping Agency</td><td>99</td></tr><tr><td>NOAA</td><td>National Oceanic and Atmospheric Administration</td><td>78</td></tr><tr><td>PaT</td><td>Palau Trench</td><td>53</td></tr><tr><td>PCA</td><td>Principal Component Analysis</td><td>iv</td></tr><tr><td>PCT</td><td>Peru-Chile Trench</td><td>26</td></tr><tr><td>POSIX</td><td>Portable Operating System Interface</td><td>150</td></tr><tr><td>PSB</td><td>Philippine Sea Basin</td><td>xx</td></tr><tr><td>PSP</td><td>Philippine Sea Plate</td><td>39</td></tr><tr><td>PsT</td><td>Puysegur Trench</td><td>72</td></tr><tr><td>PT</td><td>Philippine Trench</td><td>39</td></tr><tr><td>PVEL</td><td>Pacific VELocity</td><td>92</td></tr><tr><td>QGIS</td><td>Quantum GIS</td><td>7</td></tr><tr><td>QQ</td><td>Quantile-Quantile</td><td>vii</td></tr><tr><td>QP</td><td>Quadratic Program</td><td>175</td></tr></table>
<table><thead><tr><th>Acronyms</th><th>Contents</th></tr></thead><tbody><tr><td>R/V Research Vessel</td><td>79</td></tr><tr><td>ROV Remotely Operated underwater Vehicle</td><td>81</td></tr><tr><td>RS Remote Sensing</td><td>7</td></tr><tr><td>RT Ryukyu Trench</td><td>48</td></tr><tr><td>SARIMA State Autoregressive Moving Average</td><td>176</td></tr><tr><td>SCT San Cristobal Trench</td><td>37</td></tr><tr><td>SIO Scripps Institution of Oceanography</td><td>79</td></tr><tr><td>SRTM Shuttle Radar Topographic Mission</td><td>x</td></tr><tr><td>SSH Sea Surface Height</td><td>203</td></tr><tr><td>SSL Secure Sockets Layer</td><td>153</td></tr><tr><td>SciPy Scientific Python</td><td>8</td></tr><tr><td>SPSG South Pacific Subtropical Gyre</td><td>31</td></tr><tr><td>SPOT Satellite Pour l'Observation de la Terre</td><td>81</td></tr><tr><td>SPSS Statistical Package for the Social Sciences</td><td>105</td></tr><tr><td>StatsModels Statistical Models</td><td>8</td></tr><tr><td>TT Tonga Trench</td><td>31</td></tr><tr><td>UPGMA Unweighted Pair Group Method with Arithmetic Mean</td><td>161</td></tr><tr><td>USGS United States Geological Survey</td><td>24</td></tr><tr><td>UTM Universal Transverse Mercator coordinate system</td><td>117</td></tr><tr><td>VT Vanuatu Trench</td><td>32</td></tr><tr><td>VtT Vityaz Trench</td><td>33</td></tr><tr><td>WPGMA Weighted Pair Group Method with Arithmetic Mean</td><td>164</td></tr><tr><td>WGS84 World Geodetic System of 1984</td><td>90</td></tr><tr><td>WLS Weighted Least Squares</td><td>125</td></tr><tr><td>YT Yap Trench</td><td>55</td></tr></tbody></table>
List of Equations

<table><tr><td>3.1</td><td>Surface wave magnitude (Ms)</td><td>89</td></tr><tr><td>3.2</td><td>Body wave magnitude (mb)</td><td>89</td></tr><tr><td>3.3</td><td>GMT regression analysis formula (1)</td><td>106</td></tr><tr><td>3.4</td><td>GMT regression analysis formula (2)</td><td>107</td></tr><tr><td>3.5</td><td>GMT regression analysis formula (3)</td><td>107</td></tr><tr><td>3.6</td><td>GMT plotting residuals formula (4)</td><td>107</td></tr><tr><td>3.7</td><td>Linear regression analysis formula</td><td>125</td></tr><tr><td>3.8</td><td>Tri-cube weight function for LOESS</td><td>125</td></tr><tr><td>3.9</td><td>Savitzky-Golay smoothing filter for LOESS algorithm</td><td>125</td></tr><tr><td>3.10</td><td>G-test of maximum likelihood statistical significance</td><td>132</td></tr><tr><td>3.11</td><td>Euclidean distance metric</td><td>132</td></tr><tr><td>3.12</td><td>k-means Clustering equation</td><td>134</td></tr><tr><td>3.13</td><td>Pearson correlation coefficient</td><td>135</td></tr><tr><td>3.14</td><td>Spearmans ρ rank correlation coefficient</td><td>136</td></tr><tr><td>3.15</td><td>Kendall τ correlation coefficient</td><td>137</td></tr><tr><td>3.16</td><td>Smooth bootstrapping of dataset in hierarchical cluster analysis</td><td>139</td></tr><tr><td>3.17</td><td>Covariance matrix equality</td><td>140</td></tr><tr><td>3.18</td><td>Correlation matrix definition</td><td>141</td></tr><tr><td>3.19</td><td>Covariance 2D matrix mapping</td><td>142</td></tr><tr><td>3.20</td><td>Definition of factor analysis for random variables</td><td>143</td></tr><tr><td>3.21</td><td>Tukey's honest significance test (ANOVA)</td><td>145</td></tr><tr><td>3.22</td><td>Levene's test (ANOVA) to assess the equality of variances</td><td>145</td></tr><tr><td>3.23</td><td>Welch unequal variances t-test (ANOVA)</td><td>145</td></tr><tr><td>3.24</td><td>Shapiro-Wilk test of normality (ANOVA)</td><td>146</td></tr><tr><td>3.25</td><td>Kruskal-Wallis rank sum test (ANOVA)</td><td>146</td></tr><tr><td>3.26</td><td>Mathematical definition for statistical histogram</td><td>156</td></tr><tr><td>3.27</td><td>Regression model</td><td>158</td></tr><tr><td>3.28</td><td>Hierarchical clustering method (simple agglomerative approach)</td><td>161</td></tr><tr><td>3.29</td><td>Covariance matrix formula</td><td>161</td></tr><tr><td>3.30</td><td>Kernel density estimator of the probability density function</td><td>162</td></tr><tr><td>3.31</td><td>Hierarchical dendrogram and clustermap algorithm WPGMA</td><td>164</td></tr><tr><td>3.32</td><td>PCA formula for the 1<sup>st</sup> weight vector</td><td>165</td></tr><tr><td>3.33</td><td>PCA formula for the n<sup>th</sup> weight vector</td><td>165</td></tr><tr><td>3.34</td><td>ANOVA definitional equation of sample variance</td><td>167</td></tr><tr><td>3.35</td><td>Computing Ordinary Least Square (OLS)</td><td>171</td></tr><tr><td>3.36</td><td>QQ statistics calculation</td><td>172</td></tr><tr><td>3.37</td><td>Gauss-Newton algorithm for non-linear least squares problem</td><td>175</td></tr><tr><td>3.38</td><td>Isotonic regression function of quadratic program</td><td>175</td></tr><tr><td>3.39</td><td>Simply ordered case for isotonic regression</td><td>175</td></tr><tr><td>3.40</td><td>Dynamic regression model (SARIMA) definition</td><td>176</td></tr></table>
List of Figures

<table>
  <tbody>
    <tr>
      <td>3.3.1 Kuril-Kamchatka Trench: 2D histograms and rose diagrams of depths [409]</td>
      <td>105</td>
    </tr>
    <tr>
      <td>3.3.2 Kuril-Kamchatka Trench and Sea of Okhotsk: 3D histograms: depths distribution [409]</td>
      <td>106</td>
    </tr>
    <tr>
      <td>3.3.3 Middle America Trench: trend regression models for the cross-section profiles</td>
      <td>107</td>
    </tr>
    <tr>
      <td>3.5.1 Kuril-Kamchatka Trench: Octave graph based on AWK-reshaped table, north [410]</td>
      <td>114</td>
    </tr>
    <tr>
      <td>3.5.2 Kuril-Kamchatka Trench: Octave graph based on AWK-reshaped table, south [410]</td>
      <td>115</td>
    </tr>
    <tr>
      <td>3.8.1 Mariana Trench: boxplots on data distribution by profiles. R code: A.6.2</td>
      <td>121</td>
    </tr>
    <tr>
      <td>3.8.2 Mariana Trench: violin plots on depth distribution by profiles. R code: A.6.25</td>
      <td>122</td>
    </tr>
    <tr>
      <td>3.8.3 Mariana Trench: notched plots on depth distribution by profiles. R code: A.6.3</td>
      <td>122</td>
    </tr>
    <tr>
      <td>3.8.4 Mariana Trench: KDE for the 25 bathymetric profiles, R code: A.6.19</td>
      <td>123</td>
    </tr>
    <tr>
      <td>3.8.5 Example of the regression analysis: Mariana Trench, selected profiles</td>
      <td>123</td>
    </tr>
    <tr>
      <td>3.8.6 Philippine Trench: Regression analysis of 25 profiles. Plotting: R code A.6.5</td>
      <td>124</td>
    </tr>
    <tr>
      <td>3.8.7 Four tectonic plates, trench slope angle in the deepest point</td>
      <td>126</td>
    </tr>
    <tr>
      <td>3.8.8 Multiple panel by groups: tectonic plates, depth by profiles</td>
      <td>127</td>
    </tr>
    <tr>
      <td>3.8.9 Mariana Trench: Ridgeline plots</td>
      <td>128</td>
    </tr>
    <tr>
      <td>3.8.10 Mariana Trench: ranking dot plots by data grouping, by R code: A.6.12</td>
      <td>128</td>
    </tr>
    <tr>
      <td>3.8.11 Mariana Trench: ranking dot plots by data grouping, by R code: A.6.14</td>
      <td>129</td>
    </tr>
    <tr>
      <td>3.8.12 Pairwise double-Y axis charts showing correlation between factors</td>
      <td>130</td>
    </tr>
    <tr>
      <td>3.8.13 Mariana Trench: ranking data for steepness variation, R code: A.6.22</td>
      <td>130</td>
    </tr>
    <tr>
      <td>3.8.14 Mariana Trench: compositional chart of the geometry of the profiles, R</td>
      <td>131</td>
    </tr>
    <tr>
      <td>3.8.15 Mosaic and silhouette plots for the Mariana Trench</td>
      <td>132</td>
    </tr>
    <tr>
      <td>3.8.16 Association plots for the Mariana Trench</td>
      <td>133</td>
    </tr>
    <tr>
      <td>3.8.17 Mariana Trench: classification of the k-means clustering by plates, R code A.6.9</td>
      <td>134</td>
    </tr>
    <tr>
      <td>3.8.18 Mariana Trench: hexagonal data distribution, by R {hexbin} library.</td>
      <td>135</td>
    </tr>
    <tr>
      <td>3.8.19 Mariana Trench: Pearson and Spearman correlation matrices, by R code A.6.41</td>
      <td>136</td>
    </tr>
    <tr>
      <td>3.8.20 Mariana Trench: normalized steepness of the slope angles by profiles, R</td>
      <td>138</td>
    </tr>
    <tr>
      <td>3.8.21 Mariana Trench: hierarchical dendrogram tree of the profiles, by R code: A.6.6</td>
      <td>139</td>
    </tr>
    <tr>
      <td>(a) Unsorted dendrogram</td>
      <td>139</td>
    </tr>
    <tr>
      <td>(b) Sorted dendrogram</td>
      <td>139</td>
    </tr>
    <tr>
      <td>3.8.22 Mariana Trench: hierarchical clustering, R code: A.6.6</td>
      <td>140</td>
    </tr>
    <tr>
      <td>(a) Bootstrap resampling</td>
      <td>140</td>
    </tr>
    <tr>
      <td>(b) Bootstrap probability</td>
      <td>140</td>
    </tr>
    <tr>
      <td>3.8.23 Mariana Trench: scatterplot matrix of the correlation between variables. R.</td>
      <td>141</td>
    </tr>
    <tr>
      <td>3.8.24 Mariana Trench: Factor Analysis, R code A.6.26</td>
      <td>143</td>
    </tr>
    <tr>
      <td>(a) EFA correlation matrix</td>
      <td>143</td>
    </tr>
    <tr>
      <td>(b) iCLUST analysis</td>
      <td>143</td>
    </tr>
    <tr>
      <td>3.8.25 Mariana Trench: fragment of factor analysis, by R code A.6.26</td>
      <td>144</td>
    </tr>
    <tr>
      <td>(a) General factor saturation (ωh)</td>
      <td>144</td>
    </tr>
    <tr>
      <td>(b) General factor saturation through omega coefficient: 3 factors ω</td>
      <td>144</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>3.8.26 Mariana Trench: ANOVA hypothesis testing, by R code: A.6.28</td>
      <td>146</td>
    </tr>
    <tr>
      <td>3.8.27 Mariana Trench: Euler-Venn diagram, R code A.6.23</td>
      <td>147</td>
    </tr>
    <tr>
      <td>(a) Variables: tectonic plates</td>
      <td>147</td>
    </tr>
    <tr>
      <td>(b) Variables: geomorphology</td>
      <td>147</td>
    </tr>
    <tr>
      <td>3.9.1 Mariana Trench: KDE of the bathymetric profiles, Python (Matplotlib and Seaborn) [428]</td>
      <td>148</td>
    </tr>
    <tr>
      <td>3.9.2 Python-based word cloud (libraries: Wordcloud, Matplotlib)</td>
      <td>149</td>
    </tr>
    <tr>
      <td>3.9.3 Mariana Trench: letter-value plot for data distribution by profiles, Python</td>
      <td>154</td>
    </tr>
    <tr>
      <td>3.9.4 Mariana Trench: violin plots on depth ranges, Python, by code A.5.1 [415]</td>
      <td>155</td>
    </tr>
    <tr>
      <td>3.9.5 Mariana Trench: heatmap for the bathymetric data distribution by profiles. Python</td>
      <td>156</td>
    </tr>
    <tr>
      <td>3.9.6 Mariana Trench: histograms of data distribution by tectonic plates. Python. Source: [421]</td>
      <td>157</td>
    </tr>
    <tr>
      <td>3.9.7 Mariana Trench: Euler-Venn diagrams, Python [415]</td>
      <td>158</td>
    </tr>
    <tr>
      <td>3.9.8 Mariana Trench: area chart and facet grid of the profiles, Python (Seaborn)</td>
      <td>159</td>
    </tr>
    <tr>
      <td>3.9.9 Mariana Trench: facet grid of the regression analysis of the profiles, Python</td>
      <td>159</td>
    </tr>
    <tr>
      <td>3.9.10 Mariana Trench: hexagonal and KDE plots by profiles. Python code A.5.9</td>
      <td>160</td>
    </tr>
    <tr>
      <td>3.9.11 Mariana Trench: correlation matrices for bivariate analysis, Python</td>
      <td>161</td>
    </tr>
    <tr>
      <td>(a) Pearson's correlation (parametric approach)</td>
      <td>161</td>
    </tr>
    <tr>
      <td>(b) Kendal rank correlation (non-parametric approach)</td>
      <td>161</td>
    </tr>
    <tr>
      <td>3.9.12 Mariana Trench: box-and-whisker plot for data distribution by 25 profiles. [419]</td>
      <td>162</td>
    </tr>
    <tr>
      <td>3.9.13 Philippine Trench: KDE of the bathymetric profiles, Python (Matplotlib and Seaborn)</td>
      <td>163</td>
    </tr>
    <tr>
      <td>3.9.14 Mariana Trench: stem plot of the slope gradient steepness by 25 profiles.</td>
      <td>163</td>
    </tr>
    <tr>
      <td>3.9.15 Mariana Trench: hierarchical cluster dendrogram of the profiles. Python</td>
      <td>164</td>
    </tr>
    <tr>
      <td>3.9.16 Mariana Trench: PCA of the sediment thickness by 25 bathymetric profiles, 3D view.</td>
      <td>165</td>
    </tr>
    <tr>
      <td>3.9.17 Mariana Trench: 3D scatterplot of the relationships between environmental factors</td>
      <td>166</td>
    </tr>
    <tr>
      <td>3.9.18 Mariana Trench: one-way ANOVA box plots</td>
      <td>167</td>
    </tr>
    <tr>
      <td>3.9.19 Methodological network, [428]</td>
      <td>168</td>
    </tr>
    <tr>
      <td>3.9.20 Mariana Trench: bathymetric patterns visualized by stacked area charts.</td>
      <td>169</td>
    </tr>
    <tr>
      <td>3.9.21 Mariana Trench: stacked bar plots on depths variations by profiles, Python.</td>
      <td>169</td>
    </tr>
    <tr>
      <td>3.9.22 Mariana Trench: radar charts of the variation in bathymetric data</td>
      <td>170</td>
    </tr>
    <tr>
      <td>3.9.23 Mariana Trench: stacked bar charts showing geological factors. Python.</td>
      <td>170</td>
    </tr>
    <tr>
      <td>3.9.24 Mariana Trench: pie charts of the bathymetric data distribution by tectonic plates. Python.</td>
      <td>171</td>
    </tr>
    <tr>
      <td>3.9.25 Plotted QQ statistics for the data distribution</td>
      <td>172</td>
    </tr>
    <tr>
      <td>3.9.26 Quantile regression plotted for sediment thickness (m) versus geologic parameters</td>
      <td>173</td>
    </tr>
    <tr>
      <td>3.9.27 Weighted Least Squares plotted for data distribution.</td>
      <td>174</td>
    </tr>
    <tr>
      <td>3.9.28 Mariana Trench: isotonic regression for variables, Python [415]</td>
      <td>175</td>
    </tr>
    <tr>
      <td>3.10.1 Mariana Trench: selected bathymetric profiles. Plotting: LATEX</td>
      <td>177</td>
    </tr>
    <tr>
      <td>3.10.2 Mariana Trench: 3D-view of the 25 bathymetric profiles. Plotting: LATEX</td>
      <td>177</td>
    </tr>
    <tr>
      <td>4.2.1 Kuril-Kamchatka Trench: median stacked profiles</td>
      <td>186</td>
    </tr>
    <tr>
      <td>4.3.1 Aleutian Trench: modeled trend curves of the relief of the mean cross-section profile. [370]</td>
      <td>192</td>
    </tr>
    <tr>
      <td>4.3.2 Aleutian Trench: histogram chart on bathymetric data distribution by profiles. [370]</td>
      <td>193</td>
    </tr>
    <tr>
      <td>4.4.1 Middle America Trench: histograms of the samples distribution by cross-section profiles, [413]</td>
      <td>195</td>
    </tr>
    <tr>
      <td>4.5.1 Peru-Chile Trench: profiles in Chilean (A) and Peruvian (B) parts [411]</td>
      <td>196</td>
    </tr>
    <tr>
      <td>4.5.2 Peru-Chile Trench: histograms for the Peruvian and Chilean segments [411]</td>
      <td>197</td>
    </tr>
    <tr>
      <td>4.6.1 Kermadec and Tonga trenches: geomorphological modelling. Source: [420]</td>
      <td>198</td>
    </tr>
    <tr>
      <td>4.7.1 Kermadec and Tonga trenches: models of slope gradient trends by profiles. Source: [420]</td>
      <td>199</td>
    </tr>
    <tr>
      <td>4.7.2 Kermadec and Tonga trenches: histograms of the bathymetric data distribution. [420]</td>
      <td>199</td>
    </tr>
  </tbody>
</table>
<table><tr><td>4.10.1</td><td>New Britain and San Cristobal trenches: graph model of the cross-section profiles</td><td>203</td></tr><tr><td>4.11.1</td><td>San Cristobal Trench: modeled trends of the slopes gradient by profiles</td><td>204</td></tr><tr><td>4.11.2</td><td>New Britain and San Cristobal trenches: histograms for bathymetric samples</td><td>206</td></tr><tr><td>4.12.1</td><td>Philippine Trench: faceted plot of the profiles. Plotting: QGIS</td><td>208</td></tr><tr><td>4.12.2</td><td>Philippine Trench: strip plots by tectonic plates and geology, Python code A.5.5 [414]</td><td>209</td></tr><tr><td>4.12.3</td><td>Philippine Trench: samples distribution by tectonic plates. Plotting: Python, and R [414]</td><td>210</td></tr><tr><td>4.12.4</td><td>Philippine Trench: correlation matrix of variables (Spearman method), Plotting: R [414]</td><td>211</td></tr><tr><td>4.12.5</td><td>Philippine Trench: ranking geomorphology of the cross-section profiles. Plotting: R [414]</td><td>212</td></tr><tr><td>4.13.1</td><td>Manila Trench: transect of the digitized cross-section profilesh</td><td>213</td></tr><tr><td>4.13.2</td><td>Manila Trench: histograms of the bathymetry</td><td>215</td></tr><tr><td>4.14.1</td><td>Ryukyu Trench: models of the slope gradient trends for the profile curves</td><td>217</td></tr><tr><td>4.14.2</td><td>Ryukyu Trench: histograms of frequency of the depth distribution</td><td>218</td></tr><tr><td>4.15.1</td><td>Yap and Palau trenches: geomorphological modelling of the profiles [412]</td><td>219</td></tr><tr><td>4.15.2</td><td>Yap and Palau trenches: statistical histograms on bathymetry [412]</td><td>220</td></tr><tr><td>4.16.1</td><td>Yap trench: trend model of the slope gradient direction [412]</td><td>221</td></tr><tr><td>4.17.1</td><td>Histograms on depths frequency: Mariana and Philippine trenches</td><td>225</td></tr><tr><td>4.17.2</td><td>Mariana Trench: graph of the 25 bathymetric profiles, QGIS [415]</td><td>226</td></tr><tr><td>4.17.3</td><td>Mariana Trench. Data distribution analysis: maximal depths by profiles, Python [415]</td><td>227</td></tr><tr><td>4.17.4</td><td>Mariana Trench. Correlation, k-means Clustering and Data Grouping, R [422]</td><td>228</td></tr><tr><td>4.17.5</td><td>Results of the k-means clustering of the Mariana Trench with different k-values, R [422]</td><td>229</td></tr><tr><td>4.19.1</td><td>Japan Trench: modeling geomorphic cross-section profiles</td><td>234</td></tr><tr><td>4.19.2</td><td>Japan Trench: statistical analysis of the data distribution (histograms)</td><td>236</td></tr><tr><td>4.20.1</td><td>Comparative analysis on data distribution by Hikurangi, Puysegur and Hjort trenches</td><td>238</td></tr><tr><td>4.21.1</td><td>Geomorphic cross-sections by Hikurangi, Puysegur and Hjort trenches</td><td>240</td></tr></table>

# List of Tools

## Software and utilities used in the current work:

### Main Tools:

* **Generic Mapping Tools**: used for cartographic mapping, modelling, automatic digitizing and spatial analysis.

* **R**: Programming language used for statistical analysis.

* **Python**: Programming language used for statistical analysis.

* **Quantum GIS**: GIS used for mapping, digitizing, spatial analysis and cartographic visualization.

* **AWK**: Programming language used for tables reformating and reshaping.

* **GNU Octave**: Programming language used for statistical analysis.

### Auxiliary Tools:

* **LATEX**: used for document typesetting and formatting

* **BIBLATEX**: used for management of bibliography and reference database

* **Gretl**: Gnu Regression, Econometrics and Time-series Library used for statistical analysis
* SPSS: statistical software used for statistical analysis

* GIMP: graphics software used for auxiliary drawing
List of Maps

<table><tr><td>1.0.1 Pacific Ocean: bathymetric map of the seafloor</td><td>2</td></tr><tr><td>1.1.1 Pacific Ocean: geologic map of main structures, GMT</td><td>4</td></tr><tr><td>1.1.2 Pacific Ocean: tectonic structures and main lineaments on the seafloor, GMT. [376]</td><td>6</td></tr><tr><td>2.1.1 Kuril-Kamchatka Trench and the Sea of Okhotsk: bathymetric map [418]</td><td>13</td></tr><tr><td>2.1.2 Kuril-Kamchatka Trench. Miscellaneous projections in GMT, [418]</td><td>14</td></tr><tr><td>(a) Mercator oblique projection</td><td>14</td></tr><tr><td>(b) Conic projection</td><td>14</td></tr><tr><td>2.1.3 Kuril-Kamchatka Trench: 3D-topographic mesh map. Source: [418]</td><td>15</td></tr><tr><td>2.1.4 Kuril-Kamchatka Trench [418]</td><td>16</td></tr><tr><td>(a) Geoid model with clipped land area</td><td>16</td></tr><tr><td>(b) Marine free-air gravity anomaly</td><td>16</td></tr><tr><td>2.1.5 Kuril-Kamchatka Trench [418]</td><td>16</td></tr><tr><td>(a) Topographic map, GMT</td><td>16</td></tr><tr><td>(b) Gravity surface model, GMT</td><td>16</td></tr><tr><td>2.2.1 Aleutian Trench: bathymetric map based on ETOPO1 grid, [370]</td><td>18</td></tr><tr><td>2.2.2 Aleutian Trench: contour bathymetric map, [370]</td><td>19</td></tr><tr><td>2.2.3 Aleutian Trench and Aleutian Islands: geoid model map, [370]</td><td>19</td></tr><tr><td>2.2.4 Aleutian Trench and Aleutian Islands: 3D mesh grid model, [370]</td><td>20</td></tr><tr><td>2.3.1 Middle America Trench: bathymetric map, ETOPO1 grid, [413]</td><td>21</td></tr><tr><td>2.3.2 Middle America Trench: topographic contour map, [413]</td><td>21</td></tr><tr><td>2.3.3 Middle America Trench: map of geological settings</td><td>22</td></tr><tr><td>2.3.4 Middle America Trench: vertical gravity model map, [413]</td><td>23</td></tr><tr><td>2.3.5 Middle America Trench: topographic surface map modeled by ASCII data, [413]</td><td>24</td></tr><tr><td>2.4.1 Peru-Chile Trench: bathymetric map, ETOPO1 [411]</td><td>26</td></tr><tr><td>2.4.2 Peru-Chile Trench: geological map [411]</td><td>26</td></tr><tr><td>2.4.3 Peru-Chile Trench: geoid regional model [411]</td><td>27</td></tr><tr><td>2.4.4 Peru-Chile Trench: map of gravity surface model [411]</td><td>28</td></tr><tr><td>2.5.1 Tonga and Kermadec trenches: bathymetric map, ETOPO1</td><td>30</td></tr><tr><td>2.6.1 Kermadec and Tonga trenches: transect segments; geologic and tectonic settings.</td><td>31</td></tr><tr><td>2.7.1 Vanuatu and Vityaz trenches: topographic map</td><td>32</td></tr><tr><td>2.8.1 Vanuatu and Vityaz trenches: geologic map</td><td>33</td></tr><tr><td>2.9.1 New Britain and San Cristobal trenches: topographic map</td><td>35</td></tr><tr><td>2.10.1 New Britain and San Cristobal trenches: geoid model map</td><td>37</td></tr><tr><td>2.10.2 New Britain and San Cristobal trenches: geological and tectonic map</td><td>38</td></tr><tr><td>2.10.3 New Britain and San Cristobal trenches: gravimetry model map</td><td>38</td></tr><tr><td>2.11.1 Philippine Sea Basin: GEBCO based bathymetric map</td><td>39</td></tr><tr><td>2.11.2 Philippine Sea Basin: Geologic map.</td><td>40</td></tr><tr><td>2.11.3 Geoid model of the Philippine Sea Basin (PSB)</td><td>41</td></tr></table>
<table><tbody><tr><td>2.11.4 Gravity model of the PSB</td><td>42</td></tr><tr><td>2.11.5 3D relief model: Philippine Trench (south segment)</td><td>43</td></tr><tr><td>2.12.1 Manila Trench: bathymetric map, SRTM 15 sec grid</td><td>45</td></tr><tr><td>2.12.2 Manila Trench [418]</td><td>46</td></tr><tr><td>(a) Geologic map</td><td>46</td></tr><tr><td>(b) Geoid model</td><td>46</td></tr><tr><td>2.12.3 Manila Trench: marine free-air gravity map</td><td>46</td></tr><tr><td>2.12.4 Manila Trench: 3D mesh map of the geomorphology</td><td>47</td></tr><tr><td>2.13.1 Ryukyu Trench and Arc, East China Sea and Philippine Sea: bathymetric map</td><td>48</td></tr><tr><td>2.13.2 Ryukyu Trench: geologic map, ETOPO1</td><td>49</td></tr><tr><td>2.13.3 Ryukyu Trench: geoid map</td><td>50</td></tr><tr><td>2.13.4 Ryukyu Trench: gravimetry map</td><td>51</td></tr><tr><td>2.13.5 Ryukyu Trench: 3D mesh geomorphological modelling</td><td>52</td></tr><tr><td>2.14.1 Yap and Palau trenches: geological map [412]</td><td>53</td></tr><tr><td>2.14.2 Yap and Palau trenches: geophysical gravity map [412]</td><td>54</td></tr><tr><td>2.15.1 Yap and Palau trenches: topographic map [412]</td><td>55</td></tr><tr><td>2.15.2 Yap and Palau trenches: geoid model [412]</td><td>56</td></tr><tr><td>2.16.1 Mariana Trench: bathymetric map</td><td>57</td></tr><tr><td>2.16.2 3D relief model: Mariana Trench, south segment</td><td>59</td></tr><tr><td>2.16.3 Earthquakes event map at PSB area: prime hypocentres and magnitude values</td><td>62</td></tr><tr><td>2.17.1 Izu-Bonin Trench: bathymetric map</td><td>64</td></tr><tr><td>2.17.2 Izu-Bonin Trench: geologic map</td><td>65</td></tr><tr><td>2.17.3 Izu-Bonin Trench</td><td>66</td></tr><tr><td>(a) Geoid model</td><td>66</td></tr><tr><td>(b) Free-air marine gravity model</td><td>66</td></tr><tr><td>2.18.1 Japan Trench: topographic map</td><td>67</td></tr><tr><td>2.18.2 Japan Trench: geological map</td><td>68</td></tr><tr><td>2.18.3 Japan Trench: geoid model map</td><td>69</td></tr><tr><td>2.18.4 Japan Trench: gravity model map</td><td>70</td></tr><tr><td>2.18.5 Japan Trench: 3D mesh model map of geomorphology</td><td>71</td></tr><tr><td>2.19.1 Hikurangi, Puysegur and Hjort trenches, New Zealand: GEBCO based topographic map</td><td>72</td></tr><tr><td>2.19.2 Hikurangi, Puysegur and Hjort trenches, New Zealand: geoid model</td><td>73</td></tr><tr><td>2.20.1 Hikurangi Trench, New Zealand: tectonic and geological settings</td><td>75</td></tr><tr><td>2.20.2 Hikurangi, Puysegur and Hjort trenches, New Zealand: Marine free-air gravity anomaly</td><td>76</td></tr><tr><td>3.1.1 Comparison of the resolution in datasets: GEBCO, SRTM, ETOPO1 and ETOPO5</td><td>79</td></tr><tr><td>3.3.1 North Pacific Ocean: contour map in the Orthographic azimuthal projection, GMT</td><td>84</td></tr><tr><td>3.3.2 Landmask of the shorelines (land vs water areas), GMT</td><td>85</td></tr><tr><td>3.3.3 Grid contour modelling by Nearest neighbor algorithm, KKT GMT</td><td>87</td></tr><tr><td>3.3.4 Grid contour modelling by XYZ2grd algorithm, GMT</td><td>87</td></tr><tr><td>3.3.5 Seismic map of the focal mechanisms: PSB margins</td><td>88</td></tr><tr><td>3.3.6 Velocity ellipses at PSB margins</td><td>92</td></tr><tr><td>3.3.7 Histogram equalization on the topography grids, ETOPO1</td><td>94</td></tr><tr><td>3.3.8 Histogram equalization on the topography grids, GEBCO</td><td>96</td></tr><tr><td>3.3.9 Kuril-Kamchatka Trench: map of geologic settings, [409]</td><td>97</td></tr><tr><td>3.3.10 Middle America Trench: geoid model</td><td>98</td></tr><tr><td>3.3.11 Mariana Trench: color geoid image with grey-shaded topography of the clipped areas, GMT</td><td>99</td></tr></tbody></table>
<table><tbody><tr><td>3.3.12</td><td>Middle America Trench: 3D topographic mesh model map</td><td>100</td></tr><tr><td>3.3.13</td><td>Middle America Trench: gravity model map</td><td>102</td></tr><tr><td>3.3.14</td><td>Izu-Bonin Trench: Digitizing cross-section profiles</td><td>103</td></tr><tr><td>3.3.15</td><td>Middle America Trench: gravity surface map modeled by ASCII data</td><td>108</td></tr><tr><td>3.3.16</td><td>Aleutian Trench: surface modelling from the ASCII xyz data. [370]</td><td>109</td></tr><tr><td>3.3.17</td><td>Aleutian Trench and Aleutian Islands: gravimetry map. [370]</td><td>109</td></tr><tr><td>3.6.1</td><td>Mariana Trench: map of the profiles and fragment of the coordinates</td><td>117</td></tr><tr><td>3.6.2</td><td>Philippine Trench: map and digitized cross-section profiles. QGIS</td><td>118</td></tr><tr><td>4.2.1</td><td>Kuril-Kamchatka Trench: enlarged view of the profiles</td><td>187</td></tr><tr><td>4.3.1</td><td>Aleutian Trench: digitized cross-section profiles. [370]</td><td>191</td></tr><tr><td>4.3.2</td><td>Aleutian Trench: topographic surface modelling from the ASCII xyz data. [370]</td><td>193</td></tr><tr><td>4.4.1</td><td>Middle America Trench: cross-section profiles on the selected segment, [413]</td><td>194</td></tr><tr><td>4.5.1</td><td>Peru-Chile Trench: digitized profiles of Peruvian and Chilean segments [411]</td><td>197</td></tr><tr><td>4.8.1</td><td>Vanuatu and Vityaz trenches: cross-section profiles map</td><td>200</td></tr><tr><td>4.8.2</td><td>Vanuatu and Vityaz trenches: histograms of the cross-section profiles</td><td>200</td></tr><tr><td>4.9.1</td><td>Vanuatu and Vityaz trenches: digitizing cross-section profiles</td><td>201</td></tr><tr><td>4.9.2</td><td>Vanuatu and Vityaz trenches: 3D models</td><td>202</td></tr><tr><td>4.11.1</td><td>New Britain and San Cristobal trenches: segments of the digitized profiles</td><td>205</td></tr><tr><td>4.12.1</td><td>Cross-section transect profiles: Philippine Trench</td><td>207</td></tr><tr><td>4.13.1</td><td>Manila Trench: two segments of the digitized cross-section profiles</td><td>214</td></tr><tr><td>4.14.1</td><td>Ryukyu trench: cross-section profiles of the southern and northern segments</td><td>216</td></tr><tr><td>4.16.1</td><td>Yap and Palau trenches: digitized cross-section profiles [412]</td><td>222</td></tr><tr><td>4.16.2</td><td>Yap and Palau trenches: 3D mesh modelling [412]</td><td>223</td></tr><tr><td>4.17.1</td><td>Cross-section transect profiles of the Mariana Trench</td><td>224</td></tr><tr><td>4.18.1</td><td>Izu-Bonin Trench: modeled graph of the cross-section profiles in two segments</td><td>231</td></tr><tr><td>4.18.2</td><td>Izu-Bonin Trench: 3D mesh model map</td><td>232</td></tr><tr><td>4.18.3</td><td>Izu-Bonin Trench: statistical histograms of the depth frequency</td><td>233</td></tr><tr><td>4.19.1</td><td>Japan Trench: map of the cross-sectional profiles along two segments</td><td>235</td></tr><tr><td>4.20.1</td><td>Puysegur and Hjort trenches, Macquarie Island Arc: Digitizing cross-section profiles</td><td>237</td></tr><tr><td>4.21.1</td><td>Hikurangi Trench, North Island, New Zealand: Digitizing cross-section profiles</td><td>239</td></tr></tbody></table>
List of Tables

<table><tr><td>1.1.1 Summary of the statistics on the 20 deep-sea trenches of the Pacific Ocean</td><td>5</td></tr><tr><td>3.1.1 Data sources, types and precision</td><td>80</td></tr><tr><td>3.2.1 Software and tools used in dissertation</td><td>81</td></tr><tr><td>3.3.1 Earthquake events for 1976/1977</td><td>90</td></tr><tr><td>3.3.2 Magnitude and Moment Tensor</td><td>91</td></tr><tr><td>3.4.1 GMT-derived table for the Cross profile #1</td><td>110</td></tr><tr><td>3.4.2 AWK-based reshaped table (fragment): location and depths (m) for the first 10 profiles</td><td>112</td></tr><tr><td>3.7.1 Types of the statistical analysis used by Python and R libraries</td><td>119</td></tr><tr><td>3.8.1 Algorithms of the descriptive statistical analysis</td><td>120</td></tr><tr><td>3.8.2 Statistical algorithm of clustering, R code: A.6.9</td><td>133</td></tr><tr><td>3.9.1 Advantages of Python language for geo data analysis and processing</td><td>149</td></tr><tr><td>4.1.1 Mariana Trench: variations in gradient steepness of the right flank</td><td>181</td></tr><tr><td>4.1.2 Mariana Trench: variations in gradient steepness of the left flank</td><td>181</td></tr></table>
# Document Outline

The content of dissertation: 104 maps, 101 Figures, 13 Tables, 40 listed equations, 937 cited references, in total 436 pages. The structure consists of 10 main chapters:

* Chapter 1: Introduction summarizing the scope, goal and aim of the research. Background section presents research focus and object (20 hadal trenches). Existing works are reviewed and discussed, research gap identified, problem formulated. Research objective gives the direction of the study divided by general objective (geospatial analysis) and specific objective (technical approaches). Research questions and hypothesis underline the introductory chapter. Abbreviations are introduced in the List of Acronyms.

* Chapter 2: Study Area (trenches of the Pacific Ocean). It gives a review of each of the 20 trenches of the Pacific Ocean presenting study area, geographic and geological settings, depicts geomorphic features of the trenches based on the existing research.

* Chapter 3: Methodology and Data. It describes used data, workflow of the GMT, R, Python, AWK and Matlab scripting algorithms. The functionality of the applied tools is discussed, graphical output presented as graphs, diagrams and charts. Mathematical algorithms used in statistical data modelling and Geographic Information System (GIS) are presented and listed in the List of Equations. Tables summarizing statistical approaches and methods are presented in the List of Tables. Graphs and Maps are listed in the List of Figures and List of Maps. Full programming codes are provided in the Appendix A. Advanced solutions of GMT toolset enabling high quality mapping by various cartographic techniques are discussed and described.

* Chapter 4: Results of the work: classification and comparison of the geomorphology of the 20 hadal trenches used by GMT, R and Python statistical methods. The results are presented as a series of the thematic maps of geological and geophysical visualization of the 20 trenches of the Pacific Ocean and their geomorphic modelling.

* Chapter 5: Discussion obtained in this research and discusses findings in geostatistical analysis by R and Python. Besides, this chapter provides a detailed interpretation of the results suggesting impact factors affecting trench formation.

* Chapter 6: Conclusion on the undertaken research. It concerns the effects of the impact factors that influence geomorphic structure of the ocean trenches. In a global summary, it reflects the perspectives of the geostatistical analysis in marine geology, using GMT and programming languages in marine geology. The dissertation ends by providing recommendations for further studies and discussing perspectives and further outlook for similar research.

<figure><img src="image_2.png" /></figure>
# Chapter 1

# Introduction

THE focus of this dissertation is oceanic trenches, the deepest areas of the Earth. Specifically, study object are 20 deep-sea trenches located along the margins of the Pacific Ocean (Fig. 1.0.1), Tab. 1.1.1. Worldwide, deep-sea ocean trenches are mostly formed in the locations of the subduction zones of plate, along which lithospheric tectonic plates subduct one under another. Geomorphological shape of the deep-sea trenches is highly variable (V-shaped, U-shaped, elongated to upside-down-ridge-like, circular-like, arcuate, symmetric of irregular: left- or right-sided), which is often related to the prevailing tectonic plate movements, geologic regime and substrate.

<figure><img src="image_3.png" /><figcaption>**Map 1.0.1:** Pacific Ocean: bathymetric map of the seafloor</figcaption></figure>

Due to the specific geological conditions, oceanic trenches have unique structure and features formed as a result of the complex process. Morphologically presented as long, narrow, steep-sided depressions in the ocean bottom, trenches typically occur in the maximum oceanic depths, ca 7,300 up to > 11,000
m. There is a range of the factors influencing trench structure and formation: bathymetry, geography, geology and tectonics with complex interconnections among these determinants. The focus is upon comparative analysis of variability in geomorphological structure, classification of the cross-section profiles and formation of the 20 deep-sea trenches. The work continues investigations of the geology of the Pacific Ocean and highlights interplay between geomorphic, geological, tectonic and volcanic factors affecting submarine landform formation.

Remote location and complex bathymetry necessarily arises a need for the ML GIS approaches to visualize, model and map seafloor at such depths, to visualize and compare the geomorphology of the trenches and to assist in the development of the effective cartographic methods, technical tools and accurate datasets for submarine mapping contributing to our better understanding of the seafloor. This dissertation focuses on the comparative analysis of the submarine geomorphology of the trenches located in the Pacific Ocean by means of data processing, visualization mapping and modeling through GMT shell scripting, R and Python languages.

According to the International Hydrographic Organization (IHO) classification, trenches are 'long narrow, characteristically very deep and asymmetrical depressions of the sea floor, with relatively steep sides' [272]. The innovativeness of the work consists in the multi-disciplinary approach combining GIS analysis and statistical methods of R which contributes towards studies of ocean trenches, aimed at geospatial analysis of big data. Modelling patterns of the correlation between the geomorphic and geological features of the seafloor of the trenches is critical for proper ocean modelling.

Mapping deep-sea trenches has always been constrained both by their remote location and by a lack of high-quality bathymetric datasets on the seafloor. However, converging Earth observation advances (e.g. GEBCO, 1 Arc-Minute Global Relief Model of Earth's topography (ETOPO1) datasets) and Information Technology (IT) technologies enable accurate mapping and modelling of the least reachable objects of the seabed over wide areas of the seafloor in hadal trenches. Utilization of the statistical approaches for comparative analysis of the trenches geomorphology enables to compare variations between their shape form and highlight different submarine landforms.

Rapidly developing IT methods has revolutionized seafloor mapping, enabling to collect contextual information for visualization of the bathymetry and thereby established the knowledge base for marine geo-science. Current dissertation contributes towards advancing of the marine geo-science through report detailing of the mapping of oceanic trenches.

In this framework, following key questions were addressed: which depths of the seafloor are the most repetitive for each of the trenches and how can knowledge on geological and tectonic settings be used to explain variations in their geomorphology. Large study area and limited datasets available to perform the study determined that these questions had to be answered through mapping and characterization of the cross-section modelling performed via GMT, QGIS, Python and R.

The geomorphology of the oceanic trenches has been mapped and visualized insufficiently detailed comparing to the terrestrial areas the Earth, due to the complex submarine relief and the technical difficulties of seafloor observations. However, rapid development of new cartographic techniques, advanced methods of data modelling and visualization contributed to the progress in mapping and interpretation of the submarine geomorphology. Deep-sea trenches are mostly present a V- or U-shape in their cross-section profiles, yet can be more complex in form: trough-form, asymmetric, oblique, displaying riverine-like geometric system in a plain 2D (Fig. 4.12.1 and Fig. 4.17.1), oblique or 3D view (Fig. 2.11.5 and Fig. 2.16.2). Generally, trench geomorphology vary in slope steepness, curvature and depths.
# 1.1 Background

### 1.1.1 Submarine geomorphology

Oceanic trenches present a complex system with highly interconnected factors: geology (sediment thickness and tectonics including tectonic plates), bathymetry (coordinates, slope angle, depth values in the observation points) [400]. There are various geodynamic processes that influence tectonic rift dynamics and structure as well as and rifted margin geomorphology. Currently, the interest towards the geodynamics, the drivers and consequences of these processes was implemented as a key target goals of the oceanographic research in China [117]; [118]. Knowing and proper understanding of the driving factors affecting the ocean ecosystems gives an understanding of the possible dynamics, accumulation and location of the target ocean resources that are crucial for the economic development.

Geomorphological evolution of the oceanic trenches in the Pacific Ocean is largely controlled by a variety of factors including geological settings, tectonic plate movements [180], [202], [432], geophysical processes [580], [221], [666], submarine volcanism [589], [433], inducing intensity of sedimentation [400] and marine biological factors contributing to the sediment supply. Earlier studies on oceanic trenches highlighted relationship between the geomorphic patterns of the deep-sea channels, submarine fans and their topography related to the mid-ocean ridge tectonics, volcanism and dynamics, for instance of the Philippine back arc [542]; [90]; [181]; [221]; [220], as well as earthquakes and gravitation [498].

<figure><img src="image_4.png" /><figcaption>**Map 1.1.1:** Pacific Ocean: geologic map of main structures, GMT</figcaption></figure>

The seafloor of the trench presents a background for all geological, oceanological and geochemical processes that are necessarily reflected in its shape. Understanding bathymetry of the ocean is crucial for the marine geological research. As noted by [132], the distribution of elevations on the Earth or hypsography is highly uneven. The majority of depths is occupied by deep basins (4–6.5 km) covered with abyssal plains and hills, while seafloor with ranges 2–4 km depth mostly consists of oceanic ridges and in total cover about 30% of the total ocean seafloor. Finally, the shallow areas and continental margins with 2 km depth and shallower cover only the least amount of area: 15% of the seafloor [508].
Finally, valley, seamounts, and submarine canyons are only the minor features of the seafloor. Given the importance of the deep-sea areas, study of trenches geomorphology with regards to the bathymetry is clear.

<table><caption>**Table 1.1.1:** Summary of the statistics on the 20 deep-sea trenches of the Pacific Ocean</caption><thead><tr><th>No</th><th>Name</th><th>Max. depth (m)</th><th>Length (km)</th><th>Width (km)</th></tr></thead><tbody><tr><td>1</td><td>Aleutian</td><td>8,109</td><td>3,400</td><td>59</td></tr><tr><td>2</td><td>Mariana</td><td>11,022</td><td>2,550</td><td>59</td></tr><tr><td>3</td><td>Philippine</td><td>10,540</td><td>1,320</td><td>65</td></tr><tr><td>4</td><td>Kuril-Kamchatka</td><td>10,542</td><td>2,900</td><td>59</td></tr><tr><td>5</td><td>Middle America</td><td>6,669</td><td>2,750</td><td>34</td></tr><tr><td>6</td><td>Peru-Chile</td><td>8,065</td><td>5,900</td><td>64</td></tr><tr><td>7</td><td>Palau</td><td>8,080</td><td>700</td><td>47</td></tr><tr><td>8</td><td>Japan</td><td>8,513</td><td>800</td><td>59</td></tr><tr><td>9</td><td>Kermadec</td><td>10,047</td><td>1,200</td><td>88</td></tr><tr><td>10</td><td>Tonga</td><td>10,882</td><td>1,375</td><td>78</td></tr><tr><td>11</td><td>Izu-Bonin</td><td>9,810</td><td>2,800</td><td>82</td></tr><tr><td>12</td><td>New Britain</td><td>9,140</td><td>335</td><td>70</td></tr><tr><td>13</td><td>San Cristobal</td><td>9,020</td><td>742</td><td>64</td></tr><tr><td>14</td><td>Manila</td><td>5,400</td><td>648</td><td>76</td></tr><tr><td>15</td><td>Yap</td><td>8,946</td><td>500</td><td>45</td></tr><tr><td>16</td><td>New Hebrides</td><td>7,600</td><td>1,200</td><td>34</td></tr><tr><td>17</td><td>Puysegur</td><td>6,300</td><td>835</td><td>65</td></tr><tr><td>18</td><td>Hikurangi</td><td>3,750</td><td>342</td><td>95</td></tr><tr><td>19</td><td>Vityaz</td><td>6,000</td><td>413</td><td>47</td></tr><tr><td>20</td><td>Ryukyu</td><td>7,790</td><td>2,250</td><td>38</td></tr></tbody></table>

This dissertation aims at geomorphological modelling, systematic mapping, spatial analysis and classification of the 20 oceanic trenches of the Pacific Ocean (Tab. 1.1.1). There are many attempts undertaken to understand, to what extent and how do the geophysical movements in the subduction zones affect the trench geomorphology, deformation and migration, [136]; [169]; [203]; [257]; [490]. General concepts and understanding of the functioning and current problems in research directions of the deep-sea trenches were implied in the current thesis.

The actuality of the studies of the ocean seafloor cannot be underestimated. Recent progress in modelling and predicting earthquakes and tsunamis in Pacific ocean are based on the data analysis applied for submarine regions [335]. For example, using global datasets of broadband and long-period seismograms, recorded as a time series ranging 2006-2014 from Incorporated Research Institutions for Seismology (IRIS), a clear descendance in the morphology of the Pacific Plate was detected, which becomes flatten at the base of the upper mantle [137]. Moreover, application of the geodynamics data related to the tsunami, its possible reasons and consequences, are presented recently reporting that the shallowest reaches of plate boundary subductions host substantial slips that generate large and destructive tsunamis [273]. Attempts towards studies of the ocean geomorphology, dynamics and intercorrelation between
various factors affecting its functioning are given by various research, [531]; [535]; [521]; [516].

### 1.1.2 Seafloor spreading

Seafloor spreading creates an axial rift and corrugated hills. Spreading ridges are formed by nearby faults where subduction of the cooled tectonic plate into the mantle causes creation of the deep ocean trenches usually accompanied by earthquakes. Subduction of the cooled plate into the mantle causes creation of the deep ocean trenches and here major earthquakes and tsunamis originate.

<figure><img src="image_5.png" /><figcaption>**Map 1.1.2:** Pacific Ocean: tectonic structures and main lineaments on the seafloor, GMT. [376]</figcaption></figure>

There are 20 deep-sea trenches of the Pacific Ocean (Tab. 1.1.1), including major and minor ones. The classification of the tectonic plate boundaries according to their cross-correlation with the kinematical and geometrical properties of the plate boundaries should reflect their geodynamics. Previous works discussed a problem of the slab movement around the trench [180]; [183]; [239]. Bathymetric patterns of the seafloor of the Pacific Ocean (1.0.1) show more complex and deep structure in its western part. The geologic structure of the Pacific Ocean has a complex character (Fig. 1.1.1), [376]. Global seafloor fabric, magnetic lineation picks, slab contours and tectonic plates boundaries are presented on Fig. 1.1.2.

### 1.1.3 Historical milestones

The history of the studies of ocean geomorphology is being constantly developing throughout the 20th century until now. The chronology of the landmark research include, for instance: [121]; [233]; [673]; [235]; [3]; [314]; [205]; [42]. Understanding a variety of landforms in submarine geomorphology is a very complex task that ideally include a combination of the multi-disciplinary approaches: spatial data visualization, geostatistical analysis, geological modelling, etc.

The question of the formation and structure of the ocean trenches has long attracted science [543]; [551]. A variety of research has been done in various aspects of the trenches: bathymetric measurements,
to assess the volumes of mineral resources in the abyss, analysis of the pelagic and biotic communities, predicting earthquakes location and frequencies. Nowadays, sound statistical methods are elaborated as important additions for traditional GIS methods of geospatial analysis of the deep ocean trenches.

20th century is notable for systematic ocean exploration using Remote Sensing (RS) methods, Quantum GIS (QGIS) tools for data processing and a variety of the ML tools for data post-processing and statistical analysis. Many attempt of seafloor mapping and monitoring exist so far [84]; [271]; [354]; [574] and the advances in marine seafloor mapping are constantly developing. Mapping submarine geomorphic features and understanding factors of their variability centers several important components in the development of marine geology. 1st, support spatial marine observations and management; 2nd, supporting environmental monitoring of the oceans; 3rd, generating knowledge of the Earths shape; 4th, economic monitoring and evaluation of the natural resources in the deep ocean that has a strategic aim of planning.

Rapid recent development of the IT technologies in 21st century enabled to analyze geological and oceanological stream data in large quantities which facilitates the studies of the marine geomorphology. In 21st century, the advantages of the application of modern IT technologies for marine geology include, above all, the possibility to apply programming languages for data analysis. A multi-disciplinary approach includes GIS geospatial analysis, algorithms of the data analysis provided by the scripting libraries of high-level programming languages, such as Python and R.

In the last 2 decades along with recent progress of the programming and ML, many research presented applications of Python and R. By testing functionality of packages, algorithms and methods, these works challenged data science and brought about a new field of study in geosciences. However, examples of the geological studies on ocean trenches mostly focus on slab subduction and tectonic plate movements [219]; [58]; [304]; [221]; [61]; [256]; [163], yet insufficient use of the programming applications, and mostly apply GIS methods [164]; [509]. Therefore, in marine geology domain, despite obvious importance of the linkage between GIS and programming languages, enough case studies are not yet available. Current thesis contributes to fill in this gap.

1.1.4 Importance

Advanced data computing and processing by a combined approach of the advanced scripting tools for data analysis (GMT, Python, R, Matlab, AWK) in marine geology is a promising dynamically developing extension of the existing traditional GIS. Shell scripting and programming languages enable to deal with massive amounts of the geological data. Batch processing of big geological datasets is possible through applying ML methods from the IT domains. Number of use cases in processing big data sets in the marine geology is increasing since they are produced in a machine-generated stream at cruise as output of sampling series massive as data arrays and captured streams of video and imagery.

The advantage of the numerical computing by programming languages lies in its effectiveness for the marine geology, specifically, its effectiveness and functionality when processing with big datasets. The ML tools are highly effective for processing arrays and tables containing data of thousands of samples. Correlation between various parts of the submarine geomorphology is very complex. For example, there are proven correlations between geographic locations of the trenches, shapes, geophysical settings and submarine geomorphology [539]; [650]; [297]; [304]; [305].

The value of the big data in marine geology is indisputable nowadays since larger datasets increase reliability and precision. However, the question is not about the need for the massive data processing and their storage but in *how* to manipulate effectively with big structured datasets, which ML algorithms and methods to apply for stream data processing in marine geology. Effective answer to these questions is given by programming algorithms that enable to process data arrays by complex approaches. As demonstrated in this dissertation, graph-based analysis of the geological data models by GMT, Python, R and Matlab with table reshaping by AWK enable to process data as a stream through shell scripting.
### 1.2 Research problem

The problem of proper understanding of the deep-sea areas in the ocean seafloor lies in its unreachable location. As noted by [290], there is a need to evaluate and proper understanding seafloor geomorphology for marine resource management. However, current knowledge on ocean functioning is relatively scarce. At the same time, modeling ocean seafloor is critical for the precise bathymetric mapping. The majority of the methods of ocean modelling have overlooked the Python programming approach for statistical analysis where large data sets are being processed by a set of the embedded mathematical algorithms.

Various papers reported problems of deep ocean slab dynamics, tectonics, [577]; [240], deep ecosystems functionings and development [634]; [586]; [111]; [103]; [635]. Many attempts have been made so far to study correlation between factors involved in trench formation. However, there are certain limitations in these studies caused by the study object: submarine trenches are the least reachable land form on the Earth that require application of advanced numerical modelling.

Besides, submarine erosion occurring within the main channel of the trench and adjusting slope areas, plays important role in sculpting geomorphology of the trenches. Due to their physical inaccessibility, oceanic trenches are distinct from other objects on the Earth. Various studies are reported on the geometric variations of the trench involving uneven distribution of various geomorphic phenomena across the seafloor [544]; [208]. Amongst these, the question of how the trench shape varies and what are the factors affecting its geomorphology is the most challenging in view of the importance of the deep segments for the whole ocean environment. The distribution of elevations on the Earth or hypsography is highly uneven. The majority of the depths is occupied by deep basins (4-6.5 km) while relatively few areas are covered by shallow zones. At the same time, a considerable pool of resources is hidden by the ocean depths which explains the actuality of the ocean research for the national economies.

Limitations in marine geological studies are imposed by the high cost of the marine expeditions. Using available open source geodata sets removed this problem by the use of low-cost geospatial data and their processing in GIS and open source programming language R and Python. Similarly, Python based statistical set of statistical libraries, such as Numerical Python (NumPy), Scientific Python (SciPy), Statistical Models (StatsModels), and Matplotlib present effective low-cost and easily available method for the marine oceanological data processing and modelling. Multiple approaches and GIS methods have been used so far to model ocean seafloor, the most unreachable part of the Earth. These include: RS-echo sounding [688], Conductivity-Temperature-Depth profiler (CTD) technique [704], acoustic methods, continual profiling with single-beam systems and bottom coverage capability, multi-beam swath-mapping systems, computer based modeling.

Regional studies of the marine geology of the trenches across the Pacific Ocean, [46]; [67], modelling and predictions made upon analysis of the geophysical settings of various trench, produced by these investigators were instrumental in understanding current issues of the marine geological studies. The concepts of these reports on seafloor spreading, tectonic slab subduction, continental drift, and plate tectonics in the Pacific Ocean were analyzed in the current research.

Understanding deep connections between various factors of the ocean trenches gives a key to proper modelling of the location of the mineral resources and prognosis of the dynamic processes, e.g. earthquakes. Studies of such complex objects require numerical approach. Solutions proposed by the advanced data processing proved to be effective tool for the analysis of the data sets in marine geology. For instance, linking raster datasets and geologic data on tectonic spreading lineaments with seafloor processes eventually shaping present bathymetric forms, it is possible to interpolate geomorphology patterns to the large scale to provide a framework in which a detailed seafloor mapping can be performed. Therefore, the importance of the precision in numerical computing for marine geology cannot be underestimated.
### 1.3 Research gap identification

This dissertation aimed to fill the gap in classification and systematic mapping of the deep-sea trenches of the Pacific Ocean. Technically, the gap consists in lack of multi-disciplinary research combining programming and Earth sciences research by demonstrating the advantages of the programming languages (e.g. Python, R, AWK) in marine geology. This work introduces application of such advanced data analysis applied to studies of the submarine geomorphology. Novelty of this approach consists in highlighting functionality of the high-level languages, Python and R, and applicability of the ML algorithms in marine geology, doing a step apart from the use of traditional GIS.

Methodologically, this dissertation contributed to fill in the gap in the fields by connecting ML analysis in marine geological data analysis with geomorphological analysis, gradient modelling and classification. Technical application of GMT, Python and R was performed to identify key algorithms assisting in geomorphological analysis. It advanced methodological testing of combination of several programming languages with GMT in marine geology with a case study of 20 trenches of the Pacific Ocean. Algorithms applied for the marine geological data set enabled to gain insights into the seafloor geomorphology. Statistical methods and advanced ML graph-based data analysis has a broad applicability to similar research aimed at modelling landform variations.

Practical purpose of this dissertation is to test statistical libraries of Python and R for geospatial data analysis, modeling and visualization and GMT shell scripting for geomorphological modelling. Effective and precise plotting demonstrates indisputable advantages of the ML in geology. Application of the programming significantly increases precision, speed and effectiveness of the data analysis. Accurate cartographic visualization of the datasets is crucial for proper classification, highlighting correlations and detecting dependancies such as geology, bathymetry, oceanography, tectonics. Precise mapping by means of GMT is strongly recommended for further application in marine geological studies, especially for analysis of geomorphology of the oceanic trenches.

Rapid development of methods in geospatial data processing recently grown in marine geology. However, use of high-level programming languages still require special focus and attention as lacking comprehensive introduction of the ML algorithms. Current state of research in geology includes methods supported mainly by GIS. While some work fragmentary include Python scripts as plugins embedded in GIS, relatively few works use programming in full [680]. On the other hand, supporting GIS methods by computer modelling contributes significantly towards understanding of the ocean trenches.

Hence, this dissertation has both scientific and practical implications for understanding spatiotemporal rates of the interplay between various factors affecting submarine structure of the trenches of the Pacific Ocean. Geomorphic modelling and comparative assessment of the bathymetry between major deep-sea trenches of the Pacific Ocean based on the automatic cross-section transecting and analysis of their geological, geophysical and tectonic settings, enables to highlight differences between their local structure and to visualize how their morphology vary in different segments.

The relationship between submarine geomorphology, bathymetry and tectonic settings of the trenches can be derived by comparing geomorphic features (e.g. slope steepness) and geological conditions (sedimentation) [400]. Variations in structure of the submarine geomorphology have origin in the palaeologic geological time caused by the variety of impact factors [671]. To evaluate relationships between factors affecting morphology of the trench, methods of the advanced modelling and statistical analysis can be applied [302]; [702]. Examples of the studies on correlation between various factors influencing ocean functioning are presented [547]; [666]; [205]. They aim to analyze the interconnections between geology, sedimentation and ocean landforms using available methods.
# 1.4 Research objective

### 1.4.1 General objective

* To analyze variations in geomorphological shape of the submarine geomorphology in 20 deep-sea trenches of the Pacific Ocean.

* To perform classification of the geomorphological types of the 20 deep-sea trenches based on their geometric properties, distinctness and similarities

The thesis aims at the analysis of the variation of the geomorphology of the 20 trenches of the Pacific Ocean in their various segments based on the applied statistical analysis supported by GMT, Octave, Python and R libraries.

### 1.4.2 Specific objectives

* To visualize and map local geological properties of the 20 deep-sea trenches based on the available datasets

* To visualize and map geophysical properties (marine free-air gravity and geoid model) of the 20 deep-sea trenches based on the available datasets

* To use methods of the advanced programming algorithms (Python and R) in order to receive models of the correlation between geological factors and trench geomorphology

* To apply GMT modules to systematically model cross-section identical profiles for 20 trenches for geomorphological comparative monitoring of their geometry

* To perform statistical analysis of the bathymetry for the 20 respective trenches, along the margins of the Pacific Ocean.

Combined application of the advanced tools of data analysis (Python, R, GMT, Octave, AWK) for the data analysis and modelling reveals a significant unevenness in the geomorphology in trenches along the margins of the Pacific Ocean, and a correlation between the geological factors affecting its structure.

# 1.5 Research questions

This study answers three questions aimed to describe and characterize variations and perform classification in the geomorphic types of the 20 trenches located along the margins of the Pacific Ocean, using advanced methods of data analysis (GMT, R, Python, AWK). To systematically record the cross-section profiles of the trenches and assess their geological, tectonic, geographic, oceanological and bathymetric settings, a combination of the statistical methods, GIS and programming were applied [417]. Hence, the research questions of this dissertation are as follows:

* Are deep-sea trenches distinct in geomorphology with varying in-situ geological and geographic conditions along the margins of the Pacific Ocean?

* What are the factors affecting the geomorphology of the trenches and which of them are the most influencing ones?

* What classification of the geomorphic shape forms in plan (orthogonal) view can be suggested for the trenches of the Pacific Ocean?
**1.6 Hypothesis**

$\mathcal{F}$or research question 1 the Hypothesis Ho claims: geomorphic types of the deep-sea trenches are not distinct from each other and independent with varying geographic location along the margins of the Pacific Ocean, which means Ho1: $\mu1 = \mu2 = \mu3 = ... = \mu n$. The alternative Hypothesis Ha claims the opposite statement: geomorphic types of the deep-sea trenches are distinct from each other and vary according to their geographic location (NSWE) along the margins of the Pacific Ocean, Ha1: $\mu1 \neq \mu2 \neq \mu3 \neq ... \neq \mu n$.

For research question 2 the Hypothesis Ho claims: geophysical, geological, bathymetric and geodetic information extracted from the attribute features of the GIS layers may explain interplay between the geomorphic variations of the selected hadal trenches located in the Pacific Ocean, which contains all aspects of interconnections of mentioned above geospatial factors, which means Ho2: $\mu1 = \mu2 = \mu3 = ... = \mu n$. The alternative Hypothesis Ha claims the opposite statement: information on geophysical and geological settings of the trenches cannot be used for the comparative mapping and analysis of the geomorphology of the trenches, because they do not provide enough thematic information and correlation to discriminate types of the submarine landforms, Ha2: $\mu1 \neq \mu2 \neq \mu3 \neq ... \neq \mu n$.

For research question 3 the classification of the trenches has been performed based on the comparative analysis of the similarities and distinctiveness between the geometric shape forms and steepness by profiles. Central hypothesis of this research is: factor variables of geological origin or geospatial factors (i.e., geodetic location of the segments of the trench and tectonic properties of the underlying plates) explain variations of the geomorphology of the deep-sea trench. Hence, the hypothesis considered in the research objective where a set of the geological settings was explored by a GIS dataset.

A statistical testing by R and Python has been used to compare between various factors and geomorphic responses of the trenches, whether they are geomorphologically distinct and at least one pair of factors (slope steepness, geographic location, depths, tectonic plates, closeness of the submarine volcanoes, slope aspect) is statistically different and affecting trench geomorphology. One sample data set covers environmental factors of the trenches (i.e., geology, tectonics, geodetic features (descriptive statistical data on depths across the profiles), geospatial locations, sediment thickness [400] and active volcanism in the nearby area. Second sample data set is aimed to prove the hypothesis and perform comparative analysis on geomorphic and geological domains.

Variations of the factors affecting trench geomorphology is assumed to be normal as well as the equality of the statistical variances. The hypothesis testing was carried out using the ANOVA statistical test. The ANOVA test was performed by R library {car}. Thus, the key hypotheses of the research will be tested to prove whether the results of the research are accurate, reasonable and correct. The results have been interpreted on homogeneity of variance by means of one - way ANOVA tests. Since the p-value is < the significance level 0.05, there are significant differences between the groups of tectonic factors affecting trench formation. Tukey multiple pairwise comparisons test enabled to perform multiple pairwisecomparison between the means of groups and shown 95% familywise confidence level of the results.

In contrast to the traditional existing approaches, the hypothesis of this dissertation presents improvements on the marine geological data processing and interpretation methods by applying advanced methods of data analysis and visualization: a combination of GMT cartographic scripting toolset and programming languages Python, R, Octave and AWK. Developed methodology enabled to model submarine geomorphology, allowed assessment of variations between their shape, and estimation of factors affecting trench formation by complex interplay of the geospatial factors.
**1.7 Technical approach**

ain technical tools of this research is open source GIS toolsets and programming languages: R, Python, Octave, AWK. Choosing right statistical methods for the seafloor modelling is a crucial step in planning research. Available data may vary depending on current research scope, be it structure of the sedimentary cover of the ocean trench, geometric features of the deep horizons (angle steepness), waves velocity characteristics as reflected by rock density, submarine earthquakes, deep ocean currents. Methodological approach require careful reflection of the data set and goal of the statistical analysis.

Marine geology data processed by the R and Python libraries present data models on the topography of the ocean floor and environmental variables. Such data available online as free GIS layers in vector and raster formats were used in current research. Ocean research methodology is based on application of various GIS with specific tools for geospatial rater of vector data processing. Both commercial ArcGIS and open-source Geographic Resources Analysis Support System (GRASS GIS) or QGIS are suited for the seafloor study, processing attribute data of the biological and geological resources of the hadal trenches, geophysical fields and tectonic plates [393]. The diversity of the scientific tasks and need to study various aspects of the trench structure recorded as objects in the machine system, led to the creation of the specialized geological and geophysical methods, GIS and different equipment and tools.

Although use of traditional GIS is generally recommended, programming presents more powerful tool for geospatial data processing, sometimes overlooked. Open source tools of Python, R, AWK and Octave were used extensively to set up the processing scripts enabling rapid and reproducible analysis. Python and R, are highly suitable for the statistical analysis in geosciences, as having powerful statistical and math libraries, highly effective for scientific computing and used in current research.

The proposed approach of the combined data analysis by GMT, QGIS, R and Python for geospatial and statistical analysis enabled accurate and efficient computation and modelling of the large datasets in marine geology. Challenge in the assessment of the geological big datasets (several thousand of sampels) concerns the difficulty in manual identifying a correct algorithms in the computations and analysis of data distribution. The necessity to apply a precise ML algorithms recently increased in geographic sciences with respect to the importance of choosing effective method for data visualization and computation. The solutions to these problems are provided by Python's Pandas data frames: optimizing data structure, selecting correct parts from the data frame (columns, rows in data arrays), plotting.

In its geospatial part, this dissertation presents a GMT based automated workflow enabling large-scale profile cross-sectioning aimed at transect mapping in 20 trenches. It starts with cartographic mapping using high-resolution datasets (GEBCO, SRTM_15) and ends with automated cross-sectioning aimed at comparative analysis and classification of the geomorphic shape forms of the trenches. In total 50 modules of GMT scripting toolset were trained on extensive datasets collected from 20 trenches across the whole region of the Pacific Ocean.

This dissertation presents a computer-based approach by advanced data modelling. Classic approaches of the GIS mapping and other tools of geoinformatics [179], [133], RS, navigation charts and data mod-elling by cross-sections, [657]), statistical modelling by R and packages, e.g. dplyr, ggplot2, PMCMR, car [616]. Of these, GMT shell scripting and statistical modelling of the oceanological data sets by R and Python languages are the most cost-effective tools for studies on submarine geomorphology.

<figure><img src="image_6.png" /></figure>
# Chapter 2

# Trenches of the Pacific Ocean

## 2.1 Kuril-Kamchatka Trench

Kuril-Kamchatka Trench (KKT) is an oceanic trench with depths more than 5000 m, located on the border of the Sea of Okhotsk located in the Far East, north-west Pacific Ocean (Fig. 2.1.1).

<figure><img src="image_7.png" /><figcaption>Map 2.1.1: Kuril-Kamchatka Trench and the Sea of Okhotsk: bathymetric map [418]</figcaption></figure>

Geographically, the KKT continues south-eastwards from the coast of the Kamchatka Peninsula, in parallel to the Kuril Island chain, and meets Hokkaido Island. Geologically, the KKT presents a boundary of the large subduction zone of the Pacific tectonic plate under the Okhotsk tectonic plate [58], which causes irregularity in its bathymetric shapes and geomorphic structure. The submarine geomorphology of the seafloor underlying the KKT is formed as a result of the complex interplay between the geological substrate, water column and a range of the geophysical processes and phenomena [398]. As a result,
the variations in the distribution of the geomorphic landforms in the trench were caused by a continuous effect of various factors through the whole history of the trench formation.

The Sea of Okhotsk is a semi-enclosed sea, connected in terms of bathymetry with the Pacific Ocean through the Bussol and Krusenstern straits [527]. The Sea of Okhotsk is not isolated, has maximal depths of 3374 m. It has deep-sea straits to the NW Pacific Ocean, the deepest of which are the Bussol with depths reaching 2300m [71], (Map 2.1.1). The Sea of Okhotsk is connected with the Pacific Ocean hydrologically through the Bussol Strait. The upper water masses of the KKT region are strongly influenced by the currents in opposite directions: the Oyashio coming from the Arctic southwards into the Pacific Ocean and the Kuroshio coming from the South China and flowing in northeast direction [45].

KKT is characterized by a geometrically long narrow, elongate, deep and asymmetrical depression of the seafloor with steep slopes. Together with Aleutian Trench, it presents contiguous complex covering area of 254,740km² [225]. The KKT is distributed along Kuril island arcs and Eurasian continent masses, developed in the association with an island-arc system where Pacific tectonic plate subducts below the North American and Okhotsk plates. The Okhotsk plate represents a region of active grabens lying southwards of a chain of small sedimentary basins in the Cherskii Mountains [656]. The geomorphic and topographic patterns of the land area of Kamchatka Peninsula are influenced by the climate and glaciological effects (glacier cirques) [33]. The KKT is divided by the Bussol Strait into southern and northern parts. Because geologic factors differ in northern and southern parts of the trench, it affects its geomorphology.

Southern part of the Greater Kuril Chain includes the large Kunashir, Iturup, and Urup islands constituting the islands-arc-trench system. This part is confined to the northwestern Pacific subduction zone [315]. The topographic and geomorphic features of the trench in its northern part are different from those located further southwards near the group of the seamounts.

<figure><img src="image_8.png" /><figcaption>**Map 2.1.2:** Kuril-Kamchatka Trench. Miscellaneous projections in GMT, [418]</figcaption></figure>

Specific distinction of the KKT is its division into two parts: northern and southern segments separated by the Bussol Strait [398]. The geological settings of the study area is characterized by high seismicity, repeated earthquakes, and tectonic instability caused by plate subduction. Geological factors differ in northern and southern parts of the trench, which naturally affects its geomorphology. The comparisons on the northern and southern parts of the KKT seafloor are not assessed within the published literature. Therefore, the novelty of this study is a contribution towards the regional analysis of the geospatial variation of the trench geomorphology in its southern and northern parts. The formation
of the hadal trench is affected by the impacts of local geological and geophysical settings varying along the trench.

The tectonic properties of the **KKT** mutually affect both land and submarine geomorphology. Thus, as noted by [597], the location of the **KKT** at the meeting place of active Kuril-Kamchatka and Aleutian arcs causes the coastline of the Kamchatka Peninsula to be affected by strong tectonic activities. Fracture zones have variable influence on the uplift of the Kamchatka Peninsula. Geomorphic consequence include raise of the multi-level, highly uplifted marine terraces displaced along the active tectonic faults. Furthermore, the tectonic movements cause the uplift of the coastal sediments [597].

**KKT** belongs to the Chilean tectonic subduction type, which has a gentle subduction dip advancing at a speed of ca. 2 cm yr-1 and inter-seismic elastic shortening of 1-2 mm yr-1 [128]. Strong lateral velocity in the shallower mantle wedge accounts for both the compressional subduction tectonics and back arc compression in the **KKT**. This results in the seismic-velocity anomalies in the mantle under the **KKT**, as shown on the tomographic images by [761]. Despite the age of the Pacific Plate, there is no sharp variation of the age on the seafloor along the **KKT** [617], and the old Pacific Plate is rejuvenated at the north of the **KKT** around the Meiji Guyot seamount.

<figure><img src="image_9.png" /><figcaption>**Map 2.1.3:** Kuril-Kamchatka Trench: 3D-topographic mesh map. Source: [418]</figcaption></figure>

The Kuril-Kamchatka subduction zone, a part of the circum-Pacific 'Ring of Fire', forms northern part of the Kuril-Kamchatka volcanic arc in the North-West Pacific Ocean [602]; [206], [398]. The subduction rate of the Pacific plate beneath the Eurasian Plate is ca. 79 mm yr-1 [129]; [57] or 8-9 m per century along the **KKT** [568]. This causes high seismic and volcanic activities, making this region one of the most tectonically active, seismic regions of the world. More specifically, one of the most active volcanic arcs in the world is located on the eastern coast of Kamchatka [203]; [600]. A number of studies analyzed earthquakes across the region of the southern Sea of Okhotsk in the adjusting Kamchatka
Peninsula. Nowadays, there are 29 active, and ca. 300 extinct volcanoes located on the Kamchatka Peninsula near the KKT [689].

<figure><img src="image_10.png" /><figcaption>Color geoid image of the Kuril-Kamchatka Trench with gray-shaded topography of the clipped land areas</figcaption></figure>

(a) Geoid model with clipped land area

<figure><img src="image_11.png" /><figcaption>Marine free-air gravity anomaly for the Kuril-Kamchatka Trench area</figcaption></figure>

(b) Marine free-air gravity anomaly

<caption>**Map 2.1.4:** Kuril-Kamchatka Trench [418]</caption>

Southern part of the KKT differ, as proved by recent findings [315] where it is demonstrated on the example of South Kuril that intense fluid thermal fluxes in the continental lithosphere cause of the formation of magma chambers and the development of volcanism and multi-metal ores. Over 700 earthquakes with hypo-centers >50 km are recorded in the KKT focal zone [41] Individual earthquakes show large variations in seismic slip and distribution of foreshocks and aftershocks that shows spatial heterogeneity in the stress level and geological settings along the tectonic plate boundary [642].

<figure><img src="image_12.png" /><figcaption>Surface modelling of the topography along the Kuril-Kamchatka Trench
Modelling: GMT surface module, tension factor of the continuous curvature splines 0.25
Input raw table data: global 1-min grid resolution in ASCII XYZ-format, applied blockmean filter.
Output spatial model: 50-m grid spacing in netCDF format</figcaption></figure>

(a) Topographic map, GMT

<figure><img src="image_13.png" /><figcaption>Surface modelling of the gravity along the Kuril-Kamchatka Trench
Modelling: GMT surface module, tension factor of the continuous curvature splines 0.25
Input raw table data: global 1-min grid resolution in ASCII XYZ-format, applied blockmean filter.
Output spatial model: 30-m grid spacing in netCDF format</figcaption></figure>

(b) Gravity surface model, GMT

<caption>**Map 2.1.5:** Kuril-Kamchatka Trench [418]</caption>
Northern part of the **KKT** located northwards from the Bussol Strait presents a geographically complicated region with smaller islands than in other parts of the Kuril volcanic arc [525], Map 3.3.9. This area is notable for the abnormally high heat flow in the ocean floor. Reduced thermal thickness of the Pacific Plate upon its subduction into the northern **KKT** indicates underlying mantle plume and point out that thermal thickness of the underlying plate is lesser than expected comparing to the normal oceanic plate of such age [683]; [667]; [682]; [681].

Historically, the **KKT** subduction zone experienced a variety of large tsunamis along the trench [773]. Reports on the seismisity of the **KKT** are provided by numerous studies [307]; [204]; [643]. Maximal depth of the seismicity along the **KKT** changes gradually from ca. 600 km to ca. 300 km with latitude increasing in a central part from ca. 50° N to ca. 54° N [203], and then decreasing sharply to a depth of 100 km northwards. The rate occurrence of the tsunami on the Shikotan Island, the southern end of the Kuril Island Chain, is one per 250 yr.

The most active section of the **KKT** subduction zone is the southern Kuril Islands and Hokkaido sections with nine Mw 7.5-8.5 earthquakes [525]. Larger earthquakes estimated as Mw 9.0 occur only every ca. 500 yr on Iturup Island [275]. Such seismic situation in the **KKT** makes this area in the north-west Pacific coast a tsunami-prone region [216]. The focal mechanism of the strongest earthquakes (M>5) in the southern part of the **KKT** affects the orientation of the trench in this part through the main fracture zones with respect to the direction and type of the geomorphic displacements. The formation of the **KKT** is affected by the impacts of local geological, geophysical and oceanological settings that resulted from the complex process of the tectonic plates movements and subduction. At the same time, there are local variations in northern and southern parts of the seafloor that influence the shape of the slopes. In the subduction zone of the Pacific Plate, these cross-sections of the tilted trench illustrate crustal processes of the subduction and deposition of deep-sea sediments into the trench in its both segments. The sediment dispersal system of the trench forms two distinct types: the first one is a trench-parallel and a second one is a trench-crossing system. The geological parameters visualized on the Map 3.3.9 show fracture lines that predominantly have south-eastern direction showing perpendicular stretching towards the trench general direction.

The geomorphology of the **KKT** includes main submarine landforms related to the sedimentation processes, volcanic activities, tectonics, geologic formation history and regional oceanographic processes. The most important of them are as follows:

* system of connected submarine canyons and channels;

* gullies, and deep-sea fans located on the slopes;

* turbidity currents;

* drifts, moats and abyssal channels driven by the ocean circulations;

* rotational deep-seated landslides triggered by the instability of the trench slopes and mass-transportation connected to the geologic underlying rocks structure;

* craters associated with deep-sea fluid-flow processes.

The formation of a variety of landforms including terraces and slopes of the trench reflects the complex action of a range of factors. These include tectonic, sedimentary, oceanographic, biochemical processes and hydrodynamic phenomena. As a result, morphological character of the seabed in both southern and northern part is depending on the local and regional factors. The Greater Kuril Chain forms a natural barrier dividing the Sea of Okhotsk basin from the Pacific Ocean by the chain of islands and adjacent **KKT**, and separated into 2 parts by the Bussol Strait. The lateral slopes of the **KKT** are formed by the cumulative effects of the geologic, seismic and sedimentary factors at the subducting tectonic plate boundaries.
### 2.2 Aleutian Trench

The Aleutian Trench (AT) is located in the North Pacific Ocean (Map 2.2.2) along the and Aleutian Islands (Fig. 2.2.1) in the area of tectonic plate subduction [144].

<figure><img src="image_14.png" /><figcaption>Map 2.2.1: Aleutian Trench: bathymetric map based on ETOPO1 grid, [370]</figcaption></figure>

Geologically, the Aleutian Islands arc is classified as an accreting margin with most of the arc having a well-developed frontal prism formed in the Plio-Pleistocene. Central Aleutian volcanic front migrated northwards 30-60 km since the formation of the arc massif completed in the Late Eocene. AT has weak coupling between the surface tectonic plate and subducting slabs [761]. The seismicity of the Aleutian regions causes tsunami that affect far located regions [644] of which the 1946 was by far the most severe [23]. Further studies on the seismicity, earthquake sources and effects distribution in the region of the Aleutian Islands is developed in various studies [153], [156]. The tectonics of the Aleutian-Komandorsky chain that is proved [184] to be colliding with Kamchatka at the Kamchatka Peninsula.

Structural evolution of the Kamchatka-Aleutian triple junction area in the Mesozoic and Tertiary was affected by the subduction in Kronotskiy subduction zone and collision of the Kronotskiy arc against North-East Eurasia margin [8]. The Aleutian tectonics is deeply connected with Kuril-Kamchatka since the Aleutian transform fault separated the Kronotskiy terrane block from the Pacific plate [174]. Relevant studies [173], [249] develop further investigations on the crustal structure transition form oceanic to continental arc in the Aleutian Islands region. The geological composition of the andesite substrate in the AT results as melts from subducted Pacific ocean crust [313]. More details on the geological and biological uniqueness of the Aleutian islands area are provided in relevant research [614]. An interesting example of the existing cartographic work as contour bathymetric map dated 1945 can be found by [561].

Processes of the deep-sea terrigenous sedimentation are formed by the transfer of the erosion materials from the adjacent land. The main processes include transportation, deposition and re-deposition of the sedimentation materials. The origin of the deep ocean sedimentation is diverse. In general, apart from the sediments from the rivers, the terrigenous material goes to the oceans through iceberg melting and glacial deposits transferred to the seafloor, as well as dusty material moved by the aeolian processes.
<figure><img src="image_15.png" /><figcaption>**Map 2.2.2:** Aleutian Trench: contour bathymetric map, [370]</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>**Map 2.2.3:** Aleutian Trench and Aleutian Islands: geoid model map, [370]</figcaption></figure>

Normally, the sediments transferred by rivers are deposited within the shelf sublittoral areas, and rarely reach deeper regions of the continental slope and, even less the abyssal basins. However, sediments deposited on the shelf may move to the deeper parts of the ocean including hadal trenches, due to further move from the shelf edge downwards, avalanche sedimentation, i.e. gravitational flows, which are caused
by the gravity. Deep currents flow through the trench ventilating its bottom water flowing through the West Pacific trenches originates from the Southern Ocean and Antarctic and flows northward [734].

<figure><img src="image_17.png" /><figcaption>**Map 2.2.4:** Aleutian Trench and Aleutian Islands: 3D mesh grid model, [370]</figcaption></figure>

Sediments cover is less than 10 m thick and is composed of soft, bedded clay with sand-size ash fragments. This clayey sediment covers a very gentle terrace 500 m wide [119]. The turbidity currents causing deposition of turbidites are being generated on the outer shelf and upper continental slope, due to the precipitation and transport via the submarine canyons to the axial part of the trench. The speed of such sedimentation in the bottoms of trenches closely depends on the volumes of the incoming sediments ranging from 300 to 3000 mm/thousand years [281]. As a result of such sedimentation, over one kilometer of sediments have accumulated in the southern AT and Peru-Chile Trench over the last several hundred thousand years [370].
### 2.3 Middle America Trench

Middle America Trench (MAT) is located along the eastern margin of the Pacific Ocean, Map 2.3.1. Tested segment is focused on the Guatemala Trench, a southern part of MAT, located on the Cocos Plate between the Tehuantepec Ridge and stretching until Cocos Ridge, between the coordinates: 96°W-82° W, 7°N-17° N (Map 2.3.1), off Middle America (Map 2.3.3), [413].

<figure><img src="image_18.png" /><figcaption>Map 2.3.1: Middle America Trench: bathymetric map, ETOPO1 grid, [413]</figcaption></figure>

The MAT is located in the place of the subduction of Cocos Plate, a young oceanic tectonic plate, beneath the Pacific Ocean off the coast of the Central America continent.

<figure><img src="image_19.png" /><figcaption>Map 2.3.2: Middle America Trench: topographic contour map, [413]</figcaption></figure>

The Cocos Plate was created as a results of the break of Farallon Plate into two pieces that initiated
two tectonic plates: Nazca Plate and Cocos Plate. In turn, the Cocos Plate is bounded by the North American Plate to the northeast, the Caribbean Plate on the south, the Pacific Plate on the west and by the Nazca Plate to the south. The MAT is unique among other Pacific trenches, since its nature is complex and varies in its different parts.

The neotectonics of the MAT consists of two distinct parts with varying features of the oceanic plate and geology in northern and southern parts. The division between the both parts goes by the Tehuantepec Ridge, a linear undersea ridge located off the west coast of Mexico in the Pacific Ocean, a remnant of an old fracture zone formed as a fracture zone and transform fault along the East Pacific Rise that reoriented the fracture zone as a result of a change in the motion of the Pacific Plate [719].

Northern part of the MAT, located off Mexico, is sometimes referred to as Acapulco Trench. It is a minor hadal trench, a part of the East Pacific Rise that stretches from Jalisco to Tehuantepec Ridge. The Acapulco region, was initiated by a large slip motion giving to the Honduras Platform an offset of ca. 800 km and cutting the Mexican structure off. In its northern part, the MAT goes along the continent of North America along Mexico up to the Middle America Transverse Zone. Because MAT runs very obliquely, it is not related to the neotectonic features of Mexico, [413].

<figure><img src="image_20.png" /><figcaption>**Map 2.3.3:** Middle America Trench: map of geological settings</figcaption></figure>

The MAT borders the end of the North American continent up to the Middle America Transverse Zone. Tectonically, the southern end of the North American continent is a special place due to the changed direction of the plates compression: during the Late Cretaceous to the Paleocene, north-south compression between the North and South Americas was predominant, therefore the Caribbean thrust system was dominating, [413]. However, the situation changed since the Pliocene and the dominant movement now became a west-east strike slip [20]. The MAT is a hadal trench trending northwest, which marks the Caribbean-Cocos plate boundary in the east Pacific Ocean [218].

Southern part is MAT, located off Middle America. The minor study area where the transects of the cross-sectional profiles are located is bordered by the square with coordinates: 94°W-87° W, 11,5°N-15° N (Map 2.3.2). Southern part of the MAT is resulted from a subduction zone related to the Franciscan subduction zone of North America where the Isthmus of Panama supports a huge Tertiary volcanic
cover. Here **MAT** originated from an ancient subduction zone involving Mesozoic oceanic sediments and crust [20]. The Cocos Ridge serves as a border for the start of the wide continental margin characteristic of the **MAT** [558], [413]. The Upper Jurassic-Cretaceous-Tertiary oceanic sediments occur in the slope of the Guatemala Trench, a southern part of the **MAT**, as well as in the Nicoya Complex of Costa Rica. Deformation across a convergent tectonic plate boundary of **MAT** correlate with gravity-driven and subduction-driven tectonic processes.

Geochemistry of the sediments of the **MAT** is presented by the olivine-plagioclasephyric basalts, the only igneous rocks is the study area, and plagioclasephyric high-alumina basalts. Basalts with thickness layer of 7 m covered by 150 m of sedimentary layers were detected on the Cocos Plate section of **MAT**.

The petrographic features of **MAT** basalts are typical for the basalts of the ocean floor: subaphyric, with plagioclase phenocrysts dominant over olivine phenocrysts, composed of abundant plagioclase microlites, olivine, clinopyroxene and rare spinel microcrysts, partly devitrified brown glass. [300]. However, white some of the basalts are similar to the oceanic tholeiites, others have a composition transitional to island-arc tholeiitic basalts. As described by [135], this may indicate presence of magmatism in the zones of convergence of the oceanic and continental or island-arc crust. Petrochemical setting of the **MAT** basalts show compositions typical for the oceanic tholeiites and transitional to island-arc tholeiites with advanced differentiation of high-alumina varieties in both types of rocks. Namely, such transitional-type basalts differ from rocks of the oceanic and island-arc basalts by values of TiO₂ concentration and the K₂O/K₂O/Na₂O ratio, [413].

<figure><img src="image_21.png" /><figcaption>**Map 2.3.4:** Middle America Trench: vertical gravity model map, [413]</figcaption></figure>

Among other deep-sea trenches, chemically similar to **MAT** tholeiitic basalts are detected in the transects of Philippine and Yap trenches. Young age of some basalts from the oceanward slopes of the **MAT** may indicate evidences of the local magmatism caused by the zone of convergence of oceanic and continental crust located nearby. This explain the eruption of the transitional basalts as oceanic and island-arc tholeiites in the**MAT**. Secondary minerals of the basalts of **MAT** in studied transects include smectite and chlorite, as well as talc, calcite, phillipsite, mica, and mixed-layer chlorite-montmorillonite also fill veins in the basalts of the trench [346].

The **MAT** runs parallel to the ancient structures that can be explained by the processes of paleosubduction. On the contrary, the Acapulco Trench, which is a northern part of the **MAT**, intersects geologic structures of the southern Mexico. The differences in the south and northern parts of the **MAT** are notable
on the land- and oceanward sides. Thus, the impact of the Tehuantepec and Cocos ridges differ on the seaward side, but very weak on the landward side of the MAT. Thus, on the oceanward side, both ridges restrict the MAT off the Middle America continental shelf, but on the continental side, the Tehuantepec Ridge produces only a slight offset in the geological structures, whereas the Cocos Ridge raises them as a whole [20]. A system of major, active transform faults 'Motagua-Polochic system' consists of the Motagua Fault and Polochic Fault (Map 2.3.3, north), [413].

The Motagua Fault runs across the Middle America, continuing offshore until it merges with the MAT near the Acapulco forming part of the tectonic boundary between the North American Plate and the Caribbean Plate. The Polochic fault lies north and continues Motagua Fault in parallel, sharing motion between the North American and Caribbean Plates [523]. The Motagua Fault caused several major earthquakes, including the 7.5 Mw Middle America 1976 earthquake. The geologic structure of the MAT includes Paleozoic crystalline basement rocks, Mesozoic intrusive igneous rocks and sedimentary rocks spanning the Cretaceous to the Pleistocene. The subduction of the Cocos Plate in the Paleogene caused eruption of the volcanoes, which resulted in active melting and formation of magma. The volcanoes are mainly located along the Pacific Volcanic Chain (Map 1.1.1) that runs in parallel to the MAT.

<figure><img src="image_22.png" /><figcaption>**Map 2.3.5:** Middle America Trench: topographic surface map modeled by ASCII data, [413]</figcaption></figure>

Northern part of the study area is distanced by the location of Chortis Block, a group unit of the rocks originated as continental crust in Nicaragua. The Nicoya Peninsula (south-east on Map 2.3.3) is formed by a volcanic-sedimentary series determined by radiolarian assemblages as Upper Jurassic and Cretaceous [663]. The distribution of the ophiolites and other geologic features is presented on Map 2.3.3, [185]. The schematic map of the geological setting of the study area is visualized on Map 2.3.3 by available data from the GMT and United States Geological Survey (USGS) sources: bathymetry, tectonic plates movement directions, distribution of volcanoes, ophiolites, ridges and tectonic slabs in the MAT area.

Marine biological analysis of the samples taken from the MAT [677] shows calcareous benthic, planktonic foraminifers, and calcareous nannoplankton recovered within sediment cores along the trench axis that show relatively high faunal diversity. Their presence at great depth indicates rapid sediment transport and burial. The abundance of the biogenic silica throughout the sediment section indicated that continental slope of MAT had high biological productivity environment. The conglomerates of the trench indicate
rapid sedimentation of the deposits accumulated in a trench slope that proved a relatively quiet environment of sediment deposition [112]. The samples from the MAT revealed areas composed of hemipelagic muds containing substantial biogenic components that proves a high degree of bioturbation: the samples consist of diatoms, radiolarians, sponge spicules, calcareous nannoplankton, and foraminifers of lower to middle bathyal benthic species [678].

The 'Glomar Challenger' expedition sampling shown that selected spots on MAT predominantly consist of dark olive gray biogenic muds. The terrigenous component of the sediments is dominant and consists of clay minerals and minor contributions of volcanic glass. Biogenous contribution is secondary and consists of ca. 50% diatoms and nannofossils and biogenic debris (foraminifers and sponge spicules), [413].

Brief sedimentation history of the MAT area was summarized by [345]: the sedimentation on the continental slope was supplied by a smectite- and plagioclase-rich terrigenous land source area during the Late Cretaceous. In middle Miocene a volcanic source existed in MAT area, as proved by the volcanic glasses followed by the persisted volcaniclastic constituents. Major sediment types of MAT include turbidites, hemipelagic sediments, chalk, basalt and slope sediments [638], [413]. Sedimentation processes on the MAT began in the early Miocene with an increased accumulation of the metalliferous foraminifer-nannofossil ooze above the basalt basement. The basal chalk, a source of Fe and Mn, presents a typical mid-oceanic metalliferous sediment type, located near the spreading center of MAT. Sedimentation time gap at MAT in the middle Miocene to Early Pliocene refers to a radiolarian-bearing pelagic clay and weak supply of terrigenous sediment at this period, which in turn may indicate remote location of the site from the land masses. The Cocos Plate moved away from its spreading center toward the subduction zone in the MAT during this period.

During Pleistocene, the hemipelagic sedimentation was substituted for the turbidite accumulation that reached present location on the trench seafloor. Sites located at the juncture of the Middle America slope and the MAT seafloor contain biogenic debris, including wood fragments. The sequence of the Quaternary trench-filling turbidites is either faulted or deposited against a fault scarp in lower Miocene chalk on the detected sites of the MAT [674], [114].

Lithologic variation and differing compaction states at the MAT show [162] that sediments on the continental slope have identical compaction as sediments in the trench, yet their compaction curve is intermediate between pelagic clays and diatom ooze. The sediments in the MAT have intermediate compaction level between the pelagic clays and terrigenous sediments, which is caused by the mix of pelagic sediments with terrestrially derived turbidites. Another particular feature of the MAT consists in partly consolidated lower Miocene to Quaternary hemipelagic muds that contain vertical-subvertical veins, <2 mm thick, spaced from one to several millimeters apart. Apart from the MAT, such morphologically analogous structures were obtained from the inner slope of the Japan Trench [396], [393].

Ocean crust of the Cocos Plate subducting beneath the landward slope of the MAT has a linear horst and graben topography of hundreds m relief. This passive assimilation of oceanic material occurs without disturbing topography shape. The subduction of the horst and graben in the MAT area is largely passive and locally active. Mid-slope area has rugged topography covered by thick slope deposits, while lower slope is smooth except local brakes by benches. Upper and middle slope areas are correlated with strong magnetic anomalies. Rough midslope topography may reflect erosion following the Paleocene uplift and local subsidence in the early Miocene. Slope deposits covered the MAT landward slope, correlate with the increased arc volcanism indicated by ash layers [260]. A thick sequence of the terrigenous sediments was discovered seaward of the MAT [678]. More detailed insight to the origins of the geomorphology of the MAT and factors affecting formation of its landforms, have been studied in relevant papers [676], [10], [675], [22], [9].
### 2.4 Peru-Chile Trench

The Peru-Chile Trench (PCT), alternatively called Atacama Trench [187], is the longest hadal trench of the Pacific Ocean (Map 2.4.1). It stretches on 5,900 km from Ecuador to Chile 6°00′N, 81°50′W to 39°00′S, 75°00′W [172], Map 2.4.1.

Antarctic plates beneath the South American Plate. As followed by its name and location, PCT can be logically divided into two parts: Peruvian and Chilean one. The Chilean part is longer than Peruvian, stretching in a NS direction along 4,300 km, from -18°S to -56°S.

<figure><img src="image_23.png" /><figcaption>**Map 2.4.1:** Peru-Chile Trench: bathymetric map, ETOPO1 [411]</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>**Map 2.4.2:** Peru-Chile Trench: geological map [411]</figcaption></figure>

In its total length, the trench covers an area of ca. 590,000 km2 [14]. The PCT is shallower comparing to other trenches of the Pacific Ocean (64 km width). Reaching maximal depth of 8,065 m, it is deepest trench of the South Pacific Ocean. The PCT is formed by the subduction of the Nazca and

Due to the specific geologic, geotectonic, geomorphic and climatic characteristics, the region of the PCT is exposed to a wide variety of hazardous processes such as earthquakes, local tsunamis, generated between the PCT and Chilean coasts, volcan-
ism and continent-specific geological hazards, e.g. floods, landslides and mass movements, as briefly discussed below.

<figure><img src="image_25.png" /><figcaption>**Map 2.4.3:** Peru-Chile Trench: geoid regional model [411]</figcaption></figure>

Volcanism intensity in the study area can be illustrated by recent documented tsunamigenic giant earthquakes: Valdivia earthquake, the largest ever recorded (Mw = 9.5), 1960 and the Maule earthquake (Mw = 8.8), 2010 [104].

Bathymetry of the PCT has following distinguished features. The continental margin of Southern Chile between 36° S and 39° S has intermediate shelf of width 20-30 km [728]. Here the gradient of the continental slope is inclined at 2.5°-4.0° with frequent irregularities: plateaus, slope basins and escarpments as steep as 10°. South Chilean slope is dissected by four major and some minor meandering submarine canyons (Biobío, Imperial, Tolten and Calle-Calle) connected to the large rivers flowing off the continent.

The Biobío Canyon ends in submarine fans where it enter the trench [711]. Slightly sinuous axial channel is observed in the Chilean Trench, with a width of a 7-10 km and depth ca. 150 m [728]. It originates from the Chacao Canyon at 41°S and connected to the exits of the largest submarine canyon systems of the continental margin (Calle-Calle, Tolten, Biobío and San Antonio Canyons).

Nazca Plate forms oceanward boundary of the PCT, elevating ca. 100-400 m comparing to the trench and showing parallel geological lineaments. Nazca Plate has chains of 2-km-high seamounts aligned almost parallel to the complex subduction of the Valdivia and Mocha Fracture Zones [109], Map 2.4.2. More details on the complexity of the processes in the oceanic fracture zones, affected by high degree of hydration in the crust and upper mantle is discussed by [105]. Geophysical setting of the study area in the Peru-Chile area are shown on Map 2.4.4.

The PCT is formed by the subduction of the Oligocene oceanic crust of the Nazca Plate and Antarctic tectonic plates beneath the South American Plate at the PCT. The convergence rate of the Nazca Plate is 6.76.9 cm/a at an angle of ac. 80° [192]. The tectonic setting of the trench is complex, formed by a set of the processes of tectonic plates subduction, sediments accretion and their implications on the PCT. The Mocha crustal block, configured by the PCT, Mocha and Valdivia fracture zones, lies on the Nazca Plate between ca. 37.5° and 40° S. It separates two segments Maule segment to the north and Chiloé segment to the south [104].

Subduction processes in a historical context of the evolution of the Nazca and Antarctic tectonic
plates and their formation along the western margin of South America has been thoroughly described (e.g., [653]; [530]; [664]; [81]; [662]; [72]; [659]. Central Andes is a typical example of the convergent continental margin where the forearc, an important element directly reflects the interaction between the subducting and overriding plates [536]. The dynamic interaction between the active Chile Ridge and the continental margin determines the Taitao Triple Junction (46°-47° S).

North of the triple junction, Nazca Plate is subducted beneath the South American Plate in an ENE direction at a rate of 8.4 cm/year. To the south, the Antarctic Plate subducts along the E direction at only 2.4 cm/year [88]. The Nazca Plate adjacent the PCT is placed at the eastern boundary of the East Pacific Rise, which is the fastest-spreading ocean ridge on Earth. The Nazca Plate is bordered by two microplates: Easter microplate and the Juan Fernandez microplate. In turn, these include three major seamount chains: the Easter Seamount Chain, the Nazca Ridge, and the Carnegie Ridge [46]. The huge earthquake of 1960 resulted because of the subduction of the Mocha fracture zone which caused stress concentration and rupture of the Nazca Plate [100]; [727].

Main geomorphological structural units in Chile from west to east are Coastal Cordillera, Central Depression or Central valleys, and Andean Cordillera. The distinguished submarine geomorphological features of the PCT area include submarine ridges: Juan Fernández Ridge, Copiapó Ridge, Taltal Ridge, Nazca Ridge, and Iquique Ridge. The Iquique Ridge is composed of several seamounts (b1.5 km high) surrounded by a broad region of shallow seafloor, swelling up to 250 km wide in some areas and >500 m in elevation. The submarine Taltal Ridge intersects the PCT at 25° S offshore [108]. Other geomorphic features are O'Higgins Guyot and Eastern seamount chain (Map 2.4.2). The place of bending of the PCT on two parts (Peru and Chile, respectively) is the Arica bend (Map 2.4.2).

The geomorphology of the subducted slab in the PCT region changes from flat to the steep one along its path beneath the Andes. The region where the Juan Fernandez Ridge is subducting beneath the continental margin (27°-33° S) correlates with

a flat-slab subduction zone [88]. On the contrary, in north- and southwards direction from this area, the Wadati-Benioff zone, i.e. a zone of seismicity corresponding to the down-going tectonic slab in a subduction zone [605], is steeper.

<figure><img src="image_26.png" /><figcaption>**Map 2.4.4:** Peru-Chile Trench: map of gravity surface model [411]</figcaption></figure>

The flat-slab subduction zone is located northwards of the Taitao Triple Junction, or Chile Tripe Junction [43] where geomorphological feature of the Chilean Andes is characterized by the
transverse river valleys that separates the Coastal Cordillera from the Andean Cordillera (27°-33° S). Southwards 33° S, the geologic and geomorphic features in the Peru-Chile Trench region change due to the dip change of the subduction zone [88]. [536] report existence of >60 individual large-scale landslides located within the hyper-arid coastal Cordillera of the Atacama Desert, Northern Chile adjacent to the PCT. The system of such giant landslides, developed as a combination of rock avalanches and multiple rotational failures, is located along the coastline within a distance of ca. 650 km and contributes to the sedimentation of the PCT. Other sources of sediments can include earthquakes, e.g. sediment thickness in the PCT area in the region of 1960 earthquake ranged from ca. 300 to 700 m, thus having significantly increased sedimentation [604].

Over 50% of the world's deep-sea trenches are classified as sediment starved or partly sediment filled [193]. Northern part of the PCT, that is Peruvian segment, is classified as 'sediment starved', while southern part (Chilean segment) is 'partly sediment filled', as illustrated by [192]. As a response of sediment loading at the trench, the downward deflection of the trench plays a crucial role in determining its inlet sediments capacity (the amount of the subducted sediments). Weak and hot oceanic plates show more downward deflection at the trench due to sediment loading comparing to the old oceanic plates [107]. Thus, weak oceanic plate involves a larger space for sediment subduction.

From the environmental aspect of view, the Peru-Chile is considered as eutrophic, that is having high biological productivity and organic matter on the seafloor of the trench [348]. [187] illustrates the uniqueness of the biodiversity within the PCT system as follows: high concentrations of

the nutritionally-rich organic matter also at 7800-m depth, high nematode densities (>6000 ind. 10cm-2). The geomorphology of the trench is crucial for the deep-sea biology. Geographic location and geomorphic structure of the trench have affects on its biomass. Trenches located closer to the continents receive more inputs of the organic matter from the adjacent shelves. This creates more food supplies for the continental slope with steep angles and less food for the slopes with low gradients.

The availability of the nutrients largely relies on the flux of the depositing organic-rich matter [720]. The content of the geological CO₂ burial rates varies in the seafloor, with the increase in the Pacific basin [85]. Two distinctly different domains of the seafloor exist, on which carbon burial takes place: surfaces located by the fringes of the continents and the constantly submerged surfaces of continental slopes and the deep sea. The mixture of the geochemical components located at various depths of the continental slope and trench implies that sediments from upper to lower slope incorporate into a single bed [728]. Examples of the geochemical complex circulation, system of deposition and transport include [655]; [757]; [364].

Specifically for the PCT, larger downward deflection of the plate is found along the Chiloé segment than along the Maule segment [109]. Hence, according to the sedimentation conditions, the PCT can be divided into two main provinces: the main sediment-free province extending 8°-32° S and the sedimentary province extending 33°-57° S [231]. The margin off Isla de Chiloé is characterized by a largely sedimented segment of the PCT. This is caused by the extension of turbidite deposits seaward reaching 250 km from the trench axis [106]. The region close to the Taitao Triple Junction is nearly devoid of sediments and has rapid narrowing of the forearc region [43].
**2.5 Kermadec Trench**

Kermadec Trench (KT) is located in southwest Pacific Ocean, eastwards from Australia. It is located 120 km off the New Zealand with axis continuing from ca. 26°-36° S. It is the 5th deepest trench in the world with a maximum depth of 10,177 m [285] m and a length of 1500 km [288], Map 2.5.1.

<figure><img src="image_27.png" /><figcaption>Map 2.5.1: Tonga and Kermadec trenches: bathymetric map, ETOPO1</figcaption></figure>

Southward continuation of the **KT** is Hikurangi Trough located eastwards off the North Island of New Zealand lying between the southern end of the Cook Strait and the Chatham Rise. A convergence at maximal rate ca. 249 mm/yr along the Tonga-Kermadec island arc system makes it one of the most seismically active subduction zones in the world [54].

Detailed description of the environmental, geological and tectonic settings of the **KT** is in [685]; [12]; [615]; [684]. The majority of research on **KT** focus on the biota communities of the deep-sea ecosystems [358], [360], [576] or tectonic plates in the Pacific Ocean [146], [706]. Example of the morphological heterogeneity affected by the geographic isolation of the **KT** gives a case study of New Hebrides and **KT**, SW Pacific Ocean [347]. The effects of the geographical proximity and geological ages of the **KT**, bottom current patterns and bathymetry on the patterns of the biogenetic structure are explained by geographical isolation and demographic independency of the marine species which preserve species endemism [626].
**2.6 Tonga Trench**

Tonga Trench (TT) is located in southwest Pacific Ocean, eastwards from Australia northwards from
the KT. The study area is focused on two hadal trenches located in south-west Pacific Ocean northwards
from Australia and New Zealand: KT and Tonga. Their brief geographic description can be characterized
as follows.

<figure><img src="image_28.png" /><figcaption>Map 2.6.1: Kermadec and Tonga trenches: transect segments; geologic and tectonic settings.</figcaption></figure>

Its closeness to the Antarctic makes it to be one of the coldest trenches in the world [47]. KT runs parallel to the Kermadec Ridge (southern part of the Tonga-Kermadec Ridge) with geomorphology of V-shape formed by tectonic subduction of the Pacific Plate under the Indo-Australian Plate extending from approximately 26°-36° S near the northeastern tip of New Zealand’s North Island [359]. The specific conditions of the tectonic setting in the study area is the subduction of the Pacific Plate at a rapid rate beneath the Indo-Australian Plate along the Tonga-Kermadec Trench [86], Map 2.6.1, right.

Similar to KT, TT belongs to the South Pacific Subtropical Gyre (SPSG) biogeochemical province and has the same primary productivity rate of 87 g C m-2 y-1 [751]. TT is adjacent, continuing KT northwards with axis stretching from ca. 15°-26° S. Located in close proximity to the KT, it is separated by a sill located on the Tonga Platform [747] and by the Louisville Seamount Chain [289] (Map 2.6.1, right). With maximum depths of 10,882 m, TT is the 2nd deepest trench in the world [60]. Both trench axes have roughly 30° slight from the longitude line. Due to the complex interaction of various factors (ocean currents, closeness to the Antarctica) the deep Pacific Ocean seafloor underlying the trenches is notable for variable surface productivity [505]. Further studies on TT, its environment and communities can be found in relevant literature [706]; [161]; [237].
## 2.7 Vanuatu (New Hebrides) Trench

The Vanuatu Trench (VT) (alternative former spelling: New Hebrides Trench) is located in Fiji Basin, East Australia [388].

<figure><img src="image_29.png" /><figcaption>Map 2.7.1: Vanuatu and Vityaz trenches: topographic map</figcaption></figure>

The region is notable for complex geophysical settings which resulted in the formation of the trenches, high seismicity, geodynamic complexity and seismic instability. Vanuatu (New Hebrides) trench-arc system divides the New Hebrides back-arc troughs and the active North Fiji marginal basin. The Vanuatu arc stretches ca. 1200 km in the SW Pacific Ocean located in the area with coordinates 10°-23° S, 160°-180° E (Fig. 2.7.1). Narrow VT is bordered by the Vanuatu volcanic archipelago (New Hebrides), which is a submersed part of an oceanic island arc that extends between 12° S and 25° S, between the Torres to Hunter Islands [294], 1000 km to the northwest of the KT. Geomorphology of the VT is reported [234] to have masses of rubble, talus slopes and fragmented outcrops [407].

VT is a shallower trench of ca. 7156 m. Relative to other neighbour trenches (KT, Mariana Trench (MT)), it is warmest, and underlies intermediate bio-productivity [505]. VT has developed in a situation of complex tectonic settings. More specifically, it is located in a double convergent boundary between the Australian and Pacific tectonic plates [36]. The tectonic development of the VT has two distinct subduction stages [294]: 1) subduction of the Pacific Plate under the Indian-Australian plate westward direction; 2) subduction of the Indo-Australian plate beneath the North Fiji back-arc basin at the margin of the Pacific Plate, north-eastwards direction. Therefore, the convergence is marked by two opposite subduction zones, defined by the VT to the west and the TT to the east, resulting in an asymmetric opening of the North Fiji back-arc basin [21].
**2.8 Vityaz Trench**

Vityaz Trench (VtT) is located in the North Fiji Basin, East Australia [388]. It belongs to the New Hebrides trench-arc system, caused by the Indo-Australian subducting tectonic plate and a subduction of the d'Entrecasteaux ridge [91]. This area is notable for the double convergent complex boundary where Pacific Plate subducts under the Indian-Australian plate westwards, and Indo-Australian plate beneath the North Fiji back-arc basin northeastwards [388].

<figure><img src="image_30.png" /><figcaption>Map 2.8.1: Vanuatu and Vityaz trenches: geologic map</figcaption></figure>

Previous surveys on bathymetry, magnetism, seismicity and focal mechanism in the region between the southern New Hebrides Arc and North Fiji Basin enlighten the geodynamic complexity and instability of the area [526], [388]. An active volcano Mt. Yasur is located in Tanna Island of the Vanuatu arc [692]. Discussions on the geologic setting of the VT exist in literature [555]; [691]; [721]; [519]; [68]; [598], where particular attention is paid to geochemistry, mineralogy, magmatism, volcanology, seismicity and tectonics of the region [407]. The New Hebrides island arc consists of numerous volcanoes that frequently form islands situated in a tectonically complex arc-backarc system [44], [79], Fig. 2.8.1.

Three flat, mainly covered by forests, islands of Maré, Lifou and Ouvéa may illustrate tectonic deformation at various distances from the VT. Loyalty Islands serve as tension cracks resulting from the elastic bulging of the Australian plate before its subduction at the VT [62]. The New Hebrides archipelago is a complex reversed-arc system that divided into four major volcanic provinces [83]:

1. Western Belt is an Early Miocene extinct volcanic arc in the frontal arc;

2. Eastern Belt formed in the Middle Miocene, a calc-alkaline arc volcanism;

3. Marginal Province submerged in the Early Pleistocene is shown by volcanic activity;
4. Central Chain is a present volcanic line continues Marginal Province volcanism;

The anomalous morphology of the central New Hebrides arc is caused by the subduction of the DEntrecasteaux aseismic ridge which enters the central part of the VT. Besides, there is a deformation distributed over numerous spreading ridges in Lau and North Fiji back-arc basins. Geometry of Lau and North Fiji back-arc basins and segmentation of the New Hebrides arc are significantly influenced by the subduction of aseismic ridges: Louisville, D'Entrecasteaux and Loyalty [594], [388] (Fig. 2.8.1). The basaltic rocks from the central and southern islands of the New Hebrides (Aneityum, Tanna, Erromango, Efate, Emae, Tongoa and Epi), have geochemical features typical of island arc volcanics. The basalts from Futuna Island display characteristics typical of calc-alkaline rocks [147].

Conversion of the two tectonic plates at the ocean margins causes sinks or subduction of the oceanic plates. The mechanism of faulting in the subducting plates at the trench is well described [777]; [776]. The terrestrial crust appearing at the top of this new layer is formed either by new masses of the terrestrial crust caused by the arc volcanism, or because of the piled up sediment deposits coming from the subducting lower tectonic plate [262]. Therefore, ocean margins with high seismicity, repeated earthquakes and volcanism indicate areas of the collision and movements of the ocean plates and active sedimentation, where trenches are located. Such and other geodynamic processes of the deep subduction take place of the Pacific Plate underneath cause extensional events and volcanism [779]. Specific changes in the geochemical content of the underlying seafloor has also been detected.

Factors affecting plate subduction include hydrothermal factors [763], buoyancy forces, rheology of mantle minerals, stabilities of hydrous minerals, partial melting, mechanisms and rates of melt migration, kinetics of phase transformations, mechanisms of deep earthquakes, geochemical recycling, and the processes of continental collision that result from the subduction [545]. One fundamental problem concerns the depth to which subducted lithosphere penetrates into the mantle because this is related to the scale of mantle convection and the Earths evolution.

Finally, active sedimentation on the bottom of the seafloor leads to the accumulated amount at rifted margins which contributes to the formation of the VtT. Sediments outflowing further to the ocean provide important geological bodies and resources. Natural hazards taking place in the ocean, strongly correlate with submarine earthquakes and volcanic eruptions during active rifting [74]. Moreover, there is a certain correlation between the high oceanic features and thickness of the subduction channels and earthquake rupture segments [108]. VtT result from the complex geodynamic processes that continuously shape the surface of the trench' seafloor [64], [407]. Nowadays, the its seafloor demonstrates footprints of the many continuous steps of the seafloor evolution.

Tectonic plates act as giant radiators of the heat cooling, thickening, and gradually subsiding by progressing from ridge to trench. For example, it explains the appearance of the double seismic zone beneath the Mariana island arc [647]. In such a way, tectonic plates form the large-scale pattern of the system of ridges and deep ocean basins. The deformation of the upper plates together with surface migration of the ocean trenches of the Pacific Ocean are the prevailing observables of the dynamics of the inter parts of the plates [546].
**2.9 New Britain (Bougainville) Trench**

The New Britain Trench (NBT) (a.k.a. Bougainville Trench) is located eastwards off Papua New Guinea as northern borders of the Solomon Sea, Pacific Ocean (Map 2.9.1) [383]. The region is one of the world's most active subduction zones at the triple junction of the Pacific Plate, Indo-Australian Plate and Solomon Sea Plate. The Woodlark Basin subducts beneath the Solomon Islands arc region, forming a double-sided subduction zone [760]. As the region is situated in the complex zone of the convergence at the tectonic plates boundary, it is trapped between the converging Ontong Java Plateau and Australia. Geologically, the area belongs to one of the most prospective for intrusion-related mineral deposits [250], [383].

<figure><img src="image_31.png" /><figcaption>Map 2.9.1: New Britain and San Cristobal trenches: topographic map</figcaption></figure>

NBT is defined by the 6,000 m isoline. It is a narrow, 50-75 km wide trench extending northeasterly from the eastern end of the Huon Gulf along the south coast of New Britain [529]. Its name is derived from the homonymous New Britain Island from the New Britain arc (Map 2.9.1) [383]. Its western extension are the north-dipping Ramu-Markham Fault zone and the West Bismarck arc [1]. Northern part is characterized by the Bismark Sea back-arc basin (Fig. 2.10.2) where active rifting and seafloor spreading take place. The Bismark Sea back-arc basin comprises the South and North Bismarck microplates separated by the Bismarck Sea fault [708]. The islands of eastern Papua New Guinea and the Solomon islands are island arc terranes, surrounding NBT, formed adjacent to the boundary between the Australia and Pacific plates [25].

The subduction along the NBT is crucial for the tectonic development in Papua New Guinea because comparing to other Pacific trenches, it differs in its seismic structure [502]. Thus, it indicates a steeply dipping (>80°) structure to upper mantle depths (630 km), unlike other Pacific trenches where dips range between 45°-60°. Active volcanic arc along the north coast of New Britain Island (Fig. 2.10.2, red triangles) correlates with the subduction curve along the NBT [383]. More discussion on NBT tectonics
and structure exist in existing papers [737]; [110].
### 2.10 San Cristobal Trench

San Cristobal Trench (SCT) stretches in SE direction from the junction from the New Britain along the group of Solomon Islands: Bougainville, Choiseul, Santa Isabel, Malaita until it reaches San Cristobal Island from which it derived the name. The Woodlark Basin is adjacent to the SCT to the southwest and to the south of the Solomon Islands arc region (Fig. 2.10.2) [383]. It is a young (ca. 5 Ma) oceanic basin that subducts beneath the NBT [760]. The eastern part of the Solomon Plate bordering the SCT, enters the front of the Pacific Plate. As a consequence, it results of significant earthquakes along the Solomon Plate [756].

<figure><img src="image_32.png" /><figcaption>**Map 2.10.1:** New Britain and San Cristobal trenches: geoid model map</figcaption></figure>

The environment of the hadal trench presents a complex system with highly interconnected factors affecting geomorphological structure, formation and development of the trench: slabs and tectonics plates, bathymetry, geographic location, geologic structure of the underlying basement and sediment thickness [433]. Further detailed explanation on structure and these factors can be found in existing literature [124]; [171]; [716]; [298]; [503]; [625]; [252] describing bathymetry, geomorphology, sedimentation, submarine canyons and terraces of the NBT. However, these work are mainly restricted to the western part of the Solomon Sea and are based on the existing data at that time. Current study proposes a comparative analysis of the NBT with its contiguous SCT. Moreover, this study is supported by modern datasets (SRTM 15 sec Digital Elevation Model (DEM), gravity and geoid grids) using fine cartographic toolset GMT.
<figure><img src="image_33.png" /><figcaption>Map 2.10.2: New Britain and San Cristobal trenches: geological and tectonic map</figcaption></figure>

<figure><img src="image_34.png" /><figcaption>Map 2.10.3: New Britain and San Cristobal trenches: gravimetry model map</figcaption></figure>
**2.11 Philippine Trench**

Philippine Trench (PT) is located in the axe of the collision of the Philippine Sea Plate (PSP) and Sunda Plate, west Pacific Ocean.

<figure><img src="image_35.png" /><figcaption>Map 2.11.1: Philippine Sea Basin: GEBCO based bathymetric map</figcaption></figure>

PSB is marked by the complex interaction of three lithospheric plates: Eurasian, Australian and the PSP, which includes collision, subduction and accretion. Old, heavy and large (103,300,000 km²) Pacific plate plays the major role comparing to the Australian Plate (47,000,000 km²) and the PSP 5,500,000 km²[7]. The morphology of the Pacific plate has a low dip angle at shallow depths. The PSP, a large and tectonically complex region of the western Pacific located between the Pacific, Eurasian and Australian plates, is the world's largest marginal basin plate [664]. The PSP has two back-arc basins formed in Oligocene to Miocene period: Parece Vela and Shikoku Basins (Fig. 2.11.2).

Slab dynamics is one of the important driving forces for the trench formation affecting the mech-
anisms of its migration (retreat or advance). It is therefore crucial to characterize the origin of the
subducting slab morphology in the deep mantle identifying the features of subduction zones, which are
among the fundamental issues of solid Earth [761]. Effects of slab mineralogy and phase chemistry on
the subduction dynamics (buoyancy, stress field) kinematics (rate of subduction and plate motion), elas-
ticity (deformation and seismic wave speed), thermometry (effects of latent heat, isobaric superheating)
and seismicity (adiabatic shear instabilities) are discussed previously [56].
<figure><img src="image_36.png" /><figcaption>**Map 2.11.2:** Philippine Sea Basin: Geologic map.</figcaption></figure>

The geologic map shown on Fig. 2.11.2 indicates boundaries between the lithospheric plates (solid red lines). Yellow fronts denote trench axes. Tectonic slab contours are depicted by the red dashed lines. Green arrows and relevant numbers indicate the convergence rates (mm/year) along the trenches. Magenta triangles denote hydrothermal areas mostly located along the trench. The morphology and development of the subducted slabs are complex [97], on either mantle flow or convective layering at 660 km depth. Slabs can deflect horizontally in the upper mantle transition zone beneath some convergent margins whereas penetration to lower mantle depths occur beneath other island arc segments. As a result, different styles of the subduction across the upper mantle transition zone affect trench formation and are reflected in its geomorphology.

Double seismic zone beneath the Mariana Island arc is explained [647] by the following conceptual scheme of the processes of the global plate tectonics. Subduction of the cooled plate into the mantle causes formation of the deep ocean trenches where, as a consequence, earthquakes and tsunamis originate. Active volcanism and seismic situation around Mariana arc show recorded events at the cross-sections of the earthquake transects. Subducting lithospheric plates act as giant radiators of the heat cooling, thickening, and progressively subsiding from ridge to trench. As a result, spreading seafloor, affected by
moving plates, creates an axial rift, corrugated hills, and ridges, formed by the nearby faults.

Plate subduction along the Izu-Bonin Mariana arc (IBM) trench caused trench roll-back, arc rupture and back-arc rifting of two interconnected back-arc basins: Parece Vela and Shikoku Basins. The Kyushu-Palau Ridge (KPR), a remnant arc of the active IBM system results from the spreading of the Shikoku Basin (Fig. 2.11.2). It plays a key role in the subduction process of the west Philippine Sea Plate: the subduction zone here is characterized by the KPR subducting beneath the Kyushu Arc (Fig. 2.11.2) [82]. A set of various geo dynamic processes including subduction-interface rheology, phase-transition buoyancy, slab stagnation, rollback of mantle and ridge-push effects, cause significant trench motion of the IBM detected as advance by global plate-motion observations [101].

<figure><img src="image_37.png" /><figcaption>Map 2.11.3: Geoid model of the PSB</figcaption></figure>

Seismic phenomena is the PSB (Fig. 3.3.5) can be explained as the result of tectonic processes at the edges of the lithospheric plates that spread apart at the ocean ridges along the large strike-slip faults and converge at the hot and weak volcanic island arcs of IBM and the Philippines. Seismicity map (Fig. 3.3.5) in the PSB area shows that most earthquakes are confined to the marginal areas presenting a narrow, continuous belts around large stable areas of the PSP. The activity differs by divergence and convergence zones being moderate in the zones of plate divergence and including deep shocks at shallow depths in the zones of plate convergence. Seismic data on focal mechanisms are presented at Centroid Moment Tensor (CMT) catalogue which gives a relative direction of tectonic plates motion throughout active
belts. Focal mechanisms point at relative motions of the lithosphere plates determined from magnetic and topographic data associated with the zones of plate divergence.

<figure><img src="image_38.png" /><figcaption>Map 2.11.4: Gravity model of the PSB</figcaption></figure>

A phenomena of the deep-sea trench includes its migration that depends primarily on the age at the trench [165], [217]: located on the place of the subduction plate boundary at a given time, the trench may change its location over time as a result of the complex processes of the global plate tectonics. As proved by [350], trench migration and hence, geomorphic fluctuations, depends on the lower plate parameters and controlled by the subducting plate velocity $V_{sub}$. From the more recent publications on PT dynamics it has been noted [761] that fluctuations in the trench dynamics affect slab morphology and character. Another particular feature of the PT regions is tectonic plate tearing in the northwestern corner of the subducting PSP discussed in detailed by [497].

[766] keep track of the lateral fluctuations in the upper mantle structure of the PSB, which is later more detailed studied by [767] with a more close focus on the geophysics across the Philippine Sea. In some previous case studies [542]; [524]; [570], using available methods of the oceanographic mapping existing at that time, the geomorphic patterns of the deep-sea channels, submarine fans and their topography were analyzed with regards to the deep-water sedimentation [134], mid-ocean ridge tectonics and volcanism, dynamics of the Philippine back arc [90]; [181]; [220], earthquakes and gravitation [499].
There are proven relationships between the geomorphic structure of the submarine landforms and sedimentation in the selected parts of the Ocean [138]. Other factors include oceanology as underwater currents [197], geology [225], tectonics [226], earthquakes in the Western PSP [496], accumulation of the sedimentation that goes into the hadal trenches through the canyon-channel systems [113].

<figure><img src="image_39.png" /><figcaption>**Map 2.11.5:** 3D relief model: Philippine Trench (south segment)</figcaption></figure>

PT is located along the eastern edge of the Philippine archipelago, which is a deformed orogenic belt resulting from the collision of oceanic and continental blocks [310]. Here, the PSP subducts under the Philippine archipelago (4°-15° N), which is a part of a convergence zone between the Eurasian plate (Sunda block) and the PSP [596]. Subducting PSP cause a high-velocity anomaly in the KPR area. The PSP tears and forms a slab window corresponding to the KPR subduction [82]. Tearing of the tectonic plate in the northwestern corner of the subducting PSP is further discussed by [497]. The PSB is a complicated region, notable for complex bathymetry and tectonic plate shape [753], consisting of many different patchworks of the seafloor ages [101].

Because of the location in the Philippine and Sunda tectonic slabs collision, the Philippine Fault is proven to have motion in its dynamics [245]; [765]; [758]; [646]; [311]; [282]. The northwestward mo-
tion of the Philippine Sea Plate is partially absorbed by the subduction of first, the PT moving westwards, second, the East Luzon Trough located in the north-east, and third, by the Philippine Fault. Along with geologic factors, tectonic instability of the Philippine Sea Trench necessarily causes variations in the structure of its geomorphology, which is studied in this paper.

PT has been studied with respect to the geophysical processes and geological settings at selected sample points margins. This interest is in part due to correlation that exist between tectonic plate movements and historical evolution of the geological processes [242]; [671]; [611]; [130]; [89]. There is also evidence of deep-sea species existing on the seafloor of the PT which provides a habitat to a certain examples of the hadal crustaceans shrimps such as amphipods [238]. PT supports geomorphic evidence of the tectonic deformation resulting from the westward PSP subduction along the trench, as reconstructed by [611] who detected a raised marine platforms and terraces along the 80-km-long coastline of the Philippine coasts.

Heterogeneous nature of its submarine geomorphology resulting in depths and steepness variations at the local level (ca 1200 km) is caused by crustal deformation along the Philippine Fault, Luzon originated from Eocene [771], [701]. Specifically, the fault reveals left-lateral slip of the Philippine archipelago from the northwest corner of Luzon to the southeast end of Mindanao. Therefore, it is often challenging to distinguish the multiple spatial variables of the tectonic and geological factors with those of depth distribution and gradients in deep-sea trenches.

Simulated tectonic evolution of the southern Japan [228] shown that it was caused by the subduction of the Pacific and PSP and the collision of the IBM arc: geomorphological and geological data on plate movements suggest that subsidence affected the entire PSB after which the area of subsidence narrowed until the basin's uplift. [167] identified the geodynamic processes in the crust and mantle of the Philippines archipelago which induce geographic differences in azimuthal tomographic anisotropy between the central and southern Philippines. Recorded earthquake events at the 11 cross-sections of the earthquake transects visualized at three groups of depths: 0-60, 60-350 and > 350 km, demonstrating active volcanism and seismicity around the Philippine archipelago and trench.

Further reading on tectonics and geologic settings of the PT can be found in relevant papers, e.g. with more detailed discussions on spatial variation in seismic earthquake data [52]; shape form of the subducting slabs [35]; tectonics of the Parece Vela Basin [583]; subduction system of the PSP [251]; questions of fault topography patterns and quaternary faulting along the Philippine Fault zone, Central Luzon [245]; highlighted accreted terranes in the northern part of the Philippine archipelago [310]. Recent surveys revealed the presence of peridotites, gabbros, and massive and pillowed flows of the Mesozoic ophiolite belt. Major, trace and rare earth elements of the volcanic rocks show subduction-related influences on formation of the island arc [214]. Mixed sedimentary consolidated rocks were formed by the submarine erosion from the abyssal plain area located near the PT and transported to the trench, as well as submarine canyons followed by water currents [425].

Some more studies of the PSP have investigated its geophysical settings with a special focus on magnetism and reconstruction models highlighting its complex tectonic history, origin and motion development, seismicity and earthquakes [221]; [220]; [765], [408]. Other efforts have been focussed on interpreting the geophysical and tectonic settings of the PSP. Thus, [766] discussed lateral fluctuations in the upper mantle structure of the PSB, studied more detailed by [767] with a close focus on the geophysics across the PSB.
**2.12 Manila Trench**

Manila Trench (MnT) is located in South China Sea, west Pacific Ocean, west of the islands of Luzon and Mindoro in the Philippines (Map 2.12.1), [404].

<figure><img src="image_40.png" /><figcaption>Map 2.12.1: Manila Trench: bathymetric map, SRTM 15 sec grid</figcaption></figure>

The area of the MnT is located in the Manila subduction zone at the Philippine Sea plate boundary where it moves in the northwest direction toward the Eurasian plate with a high convergence rate [514], [406]. The MnT with its maximal depth of the trench about 5,400 m [511] and stretching in almost vertical North-South direction is created by the subduction of the Eurasian Plate (through its part Sunda Plate) under the Philippine Sea Plate. Specific geological structure of the MnT causes potential repetitive earthquakes and seismicity [749].

Another frequent hazard caused by the subducting slab of the Eurasian plate beneath the MnT (Map 2.12.2, left) is destructive tsunamis causing catastrophic damages in the source area and along the coastlines of the Luzon Island. The hypocenters of the tsunami of the MnT are located at the depths <100 km [641]. The tsunami hazard from the MnT source has been assessed in more details in several research papers [571], [35], [690]. The zone of the Eurasian Plate subduction explains the belt of volcanoes in the MnT area (Map 2.12.2, left), on the west side of the Philippine island of Luzon. The area between the northernmost Manila subduction zone and southern Taiwan is considered a transition from subduction to initial collision and a weakly coupled nature for the northern part of the MnT [96], [406].

The geological complexity of the MnT is also expressed by the connection with the PT where a horizontal mantle flow exists between the MnT and the PT. It is caused by the collision between the Palawan block and the Philippine Mobile Belt, and movement off the South China Sea slab [167], [406],
[404]. Various factors cause the formation of the MnT. Being a hadal trench, it presents a special area of the ocean seafloor with distinct geomorphological structures characterized by the notable depths and steep gradient angles, located in the zones of the continental margin tectonic plates bending.

<figure><img src="image_41.png" /><figcaption>(a) Geologic map</figcaption></figure>

<figure><img src="image_42.png" /><figcaption>(b) Geoid model</figcaption></figure>

Map 2.12.2: Manila Trench [418]

<figure><img src="image_43.png" /><figcaption>Map 2.12.3: Manila Trench: marine free-air gravity map</figcaption></figure>

The convergence between the two plates forming the MnT is roughly northwestward [406]. There
is a high-velocity zone present in the crust and upper mantle beneath the Luzon Arc where the MnT is stretching [140]. All these factors briefly mentioned to illustrate the tectonic settings of the study area indicate high seismicity and special tectonic zone where the MnT has been formed. The seafloor of the hadal trench presents a complex structure combined of various landforms: mid-ocean ridges, transform faults, ocean plains complicated by chains of seamounts, minor ridges, trenches and plateaus.

<figure><img src="image_44.png" /><figcaption>**Map 2.12.4:** Manila Trench: 3D mesh map of the geomorphology</figcaption></figure>

Factors influencing geomorphic development, structure and bathymetric patterns of the trench are diverse. To mention some of them: geological, hydro-chemical, geothermal, climatic, tectonic and bathymetric determinants. The steepness of the Manila geomorphology varies: the dip angles along the MnT increasing gradually southwards from ca. 25° at the latitudes between 18° and 21° N, 32° at the latitude 17° N, and nearly vertical at the latitude 14° N [93]. The tectonic front of the MnT continuing northward into the frontal thrust faults in the western Taiwan shows active plate boundary between the Eurasian and Philippine Sea plates in the Luzon-Taiwan region [768].

The structure of the northern MnT has been studied in various papers focused on the problems of crustal structure and deformation in the north of the MnT [493], [232], [340]. Among other findings, the increasing dip angle of the MnT from north to south has been discovered. The MnT loses its sharp bathymetric pattern in the early collision zone and gradually becomes a less well-defined deformation front [491]. However, the comparative analysis of its southern and northern parts is still missing. Therefore, current paper contributes to better understanding of the MnT by comparative analysis of its geomorphology in the northern and southern segments.
**2.13 Ryukyu Trench**

The Ryukyu Trench (RT) is a 1400 km long hadal oceanic trench, stretching along the southeastern edge of the Ryukyu island arc, between the Philippine Sea, Taiwan Island and Kyushu Island, southern Japan, Pacific Ocean [13] (Map 2.13.1) [392]. The trench was formed as a result of the subduction of the oceanic crust of the Philippine Sea Plate beneath the continental crust of the Eurasian Plate [142]. The aim of the research is to analyze differences in the geomorphology and bathymetry of the northern and southern parts of the trench using GMT [743].

<figure><img src="image_45.png" /><figcaption>Map 2.13.1: Ryukyu Trench and Arc, East China Sea and Philippine Sea: bathymetric map</figcaption></figure>

The bathymetric pattern of the RT varies in its northern and southern parts which reflects the correla-
tion between the geomorphic shape of the hadal trench, steepness of its gradient slope angles and tectonic
settings in the area. Thus, in the northern part of the trench, the subduction of the Philippine Sea Plate
is notable for shallow depths and steepness, reaching only ca. 11°. On the contrary, the angle becomes
steeper, reaching 70° below ca. 70 km. The region located to north-west of the Ryukyu volcanic island
arc is a Back-Arc Basin (BAB), a depression of the Okinawa Trough (Map 2.13.2) [392].

The subduction of the Philippine Sea tectonic plate causes eruption of the approximate 34 volcanoes (Map 2.13.2) and earthquakes producing tsunami. In contrast, the slab dip in the central and southern parts of the RT is more gentle, reaching 40-50° at 70 km depth [333], [517], [392]. There are three groups
of islands on the forearc basement stretching over the Ryukyu arc [322]. The Kerama Fault, about 250 km wide (Map 2.13.2), separates the southern from the central group of islands.

<figure><img src="image_46.png" /><figcaption>**Map 2.13.2:** Ryukyu Trench: geologic map, ETOPO1</figcaption></figure>

The RT reaches depths of 7000 m below sea level (BSL) and the forearc slope between the forearc island chain and the trench is between 130 to 160 km wide. The northern group has Yaku-shima and Tanega-shima and a 200 km long Tokara Channel gap between the island groups [582]. The tectonics of the hadal trench reflects evolutional processes of the building seamounts and the structure of the Earths crust. Therefore, it causes variations in the lithological patters, geological conditions of the rocks formation, stratigraphy and sedimentary settings of the ocean seafloor formation during Jurassic, Mesozoic and Cretaceous. Double subduction system of the RT reflects the complex trench motions as an expression of the interaction between the subducting plates and the underlying mantle of the RT. The Pacific plates subducts beneath the Philippine Sea Plate, which is in turn subducting beneath the Eurasian Plate along the RT [166], [392].

Geomorphology of the ocean seafloor results from the complex geodynamic processes. These demon-
strate traces of the continuous evolution and shape seabed surface as a system of the mid-ocean ridges, transform faults, vast ocean plains complicated by chains groups, ridges and plateaus as well as minor forms such as terraces, seamounts and hills, 3D Map 2.13.5. Various factors affect the trench geomorphology and in turn, are reflected in its complex structure and functionality of the submarine environment. To mention some of them: effects of the magmatism [317], seismicity [217], volcanism and hydrothermal system [549], [550].

<figure><img src="image_47.png" /><figcaption>Map 2.13.3: Ryukyu Trench: geoid map</figcaption></figure>

Another example is the effect of the thickness of the subduction channels and earthquake rupture segments on the geomorphology of the oceanic landforms. Thus, the spatial differences of the RT geomorphology is reflected in its sediment composition that change systematically from lighter off northern Ryukyu arc to heavier along the central part of the Ryukyu arc. This is explained by the dominance of the terrigenous and pelagic sedimentation off of the northern and central Ryukyu arc, respectively [679], [392].

RT presents a structural trap in the continental margins of the Pacific Ocean since the sediments are carried by the ocean currents passing through the system of trenches on the west of the Pacific [392]. The sediment types of the RT include among others mud diapirs, hemipelagic sediments, mud-supported breccia, fossil planktonic foraminifera, calcareous nannofossils [723]. The content of the planktonic foraminifera varies according to the seasonal warming of surface waters and vertical mix as a result of the NW monsoon [750].
<figure><img src="image_48.png" /><figcaption>Map 2.13.4: Ryukyu Trench: gravimetry map</figcaption></figure>

Seismic investigations of the RT area show that the sedimentary thickness is ca. 2-3 km on the oceanic plate, oceanic crust thickness is 5-6 km. The upper mantle material underlying the oceanic plate has low seismic velocities, explained by the geochemical processes of the mantle peridotites [330]. Submarine margins of the continents can be divided into two types: the passive and active ones, depending on the features of the geomorphology and geology. The active margin in the Ryukyu area is located off northern Taiwan in the southern Ryukyu forearc (Map 2.13.2), [392]. A mega-splay fault system associated with great earthquakes in the south of the Ryukyu region is described by [254]. Indeed, the southwestern Ryukyu system is characterized by the edge regime that is heavily influenced by the juxtaposing continental lithosphere [344].

Although the seafloor of the RT is separated vertically from the euphotic zone, it serves as a depositional center for the organic carbon accumulation. This is caused by the geomorphological factors: steep slopes enable lateral transport from the adjacent continental margins [284]. Studies on the spatial biodiversity of the RT show [320] low values at shallow depths, but increased with water depth reaching maximum between the trench floors and abyssal plains. This proves that bathymetric patterns of the deep-sea biomass of the RT spatially differ in various regions of the trench.
Composite overlay of the 3D topographic mesh model
on top of the 2D geoid contour plot
Region: the Ryukyu Islands and Ryukyu Trench, West Pacific Ocean

<figure><img src="image_49.png" /><figcaption>**Map 2.13.5:** Ryukyu Trench: 3D mesh geomorphological modelling</figcaption></figure>

Such discrepancies reflect differences in the environmental conditions, e.g. primary productivity level. This is also supported by the discoveries of the migration patterns in the RT area where reefs and geomorphic landforms serves as a land bridge for the migration of terrestrial organisms from Okinawa-jima to Miyako-jima [16].
**2.14 Palau Trench**

The Palau Trench (PaT) is located in the Philippine Sea, northwards from the Papua-New Guinea and eastwards from the Philippines, west Pacific Ocean (Map. 2.15.1). The geological settings of the PaT area is shown on Map 2.14.1. PaT with the greatest depth at 8,080 m (situated in its southern part) has a straight shape of the geometric form, stretching from south-west to north-east (Map map: YPT1 and Map map: YPT6), located within a square 133°-136° E and 6°-8° N, east from the Yap Islands.

<figure><img src="image_50.png" /><figcaption>Map 2.14.1: Yap and Palau trenches: geological map [412]</figcaption></figure>

One of the most unique characteristics of the PaT is its short distance (35 km) between the trench axis and the coast of the Palau Islands westwards [332]. The geology of the PaT includes sequences of serpentinized peridotite, basalt and limestone on its landward slope and a huge block of coral-reef limestones at depths 4,900 m to 6,500 m [332]. PaT is connected with the Kyushu-Palau Ridge in the north and Ayu Trough in the south. Yap and PaT are distinguished from the flat-bottomed Ayu Trough by their more similar to V shape in the cross section.

The Islands of Palau located westwards to the PaT are composed of volcanic rocks capped by coral reef limestone. This indicates that the Palau islands were located along the volcanic front earlier [332]. The tectonic situation of Yap and PaT is on the PSP. The PSP is the tectonic plate completely surrounded by convergent boundaries of the tectonic plates [758]. The tectonic settings of Yap and PaT presents a complex region of convergence among three tectonic plates: the PSP, the Pacific and the Caroline plates.
Hence, Yap and PaT belong to the subduction zones surrounding PSP along its eastern boundary.

<figure><img src="image_51.png" /><figcaption>Map 2.14.2: Yap and Palau trenches: geophysical gravity map [412]</figcaption></figure>

Increasing developments of the ocean resource exploration caused concerns on ocean areas and PaT geomorphology, formed as a result of complex set of processes [401]. The data analysis revealed deep interconnections between its submarine geomorphology and impact factors: geology and bathymetry. The structure of the submarine geomorphology of the PaT is a complex interplay of these processes. Various factors affect its geomorphic structure, geometry of shape, formation and development. The most important include the following: sediments, tectonics and stratigraphy, bathymetry, geographic location, slope aspect degree; geologic structure of the underlying basement, ocean streams determining local geomorphological conditions that depend on the sediment deposition processes.

Main morphostructure elements of the seafloor of the PaT are the outskirts of the continents, tectonic plates and ocean bedrock. The structure of the trench and the nature of its geomorphic shape are therefore greatly complicated by the multiple secondary tectonic disturbances: system of faults, displacements of grabens, horsts and lateral geologic shifts. The interconnection of these complex processes is also illustrated by recent findings [240] that demonstrates correlation between the back-arc deformation and upper continental tectonic plate velocity.
**2.15 Yap Trench**

Yap Trench (YT) is located in the Philippine Sea, northwards from the Papua New Guinea, eastwards from the Philippines, west Pacific Ocean (Map. 2.15.1). YT with the greatest depth at 8,292 m is ca. 700 km long and 50 km wide [654]. It stretches from the trench axis to Yap islands arc with a characteristic 'J' form [362]. It is morphologically divided into the northern and southern segments with the boundary between the two segments at 8°26′N [182].

<figure><img src="image_52.png" /><figcaption>Map 2.15.1: Yap and Palau trenches: topographic map [412]</figcaption></figure>

The Yap-Palau subduction zone lies southwards off the Mariana arc-trench system. The Caroline Plate subducts here northwestwards beneath the Philippine Sea Plate. The most prominent geological structures in the Caroline Plate are Caroline Ridge (Map 2.14.1), a buoyant oceanic plateau, and Sorol Trough, spreading in the middle of the ridge [139].

YT and PaT are formed in the place of the PSP subduction and oceanic lithosphere sinking to the mantle. As sinking PSP moves deeper into the mantle, the fluids are released from the mantle rock. This cause melting of the overlying mantle. New magma (molten rock) rises to the surface and erupts violently forming volcanoes and arcs of islands along the convergent tectonic plate boundary. Therefore, earthquakes are common in the subduction zone of the PSP (Map 2.15.1, red triangles).
<figure><img src="image_53.png" /><figcaption>Map 2.15.2: Yap and Palau trenches: geoid model [412]</figcaption></figure>

YT and PaT form a typical trench-arc-basin system which includes deep trench, island arcs, and back-arc basin, a Parece Vela Basin, located in the eastern side of the Philippine Sea Plate (Map. 2.14.1). Such complex tectonic structure of the region is reflected in notable heterogeneity of the crustal and upper mantle velocity structures beneath the Philippine Sea Plate [578]. Necessarily, it affects the sub-marine seafloor topography and its geomorphic shape. Environment of the YT and PaT (biochemical and ecological setting) are described in relevant papers [757]; [751]; [512].

Syntheses of the tectonic and magmatic evolution of the region of the Philippine Sea, YT and PaT can be found in further works [39], [599], [672], [581], [717]. Water mass properties and deep currents in the northern YT were observed and discussed recently [512].
**2.16 Mariana Trench**

**Geographic Location** MT is the deepest part of the World Ocean with a maximal depth of 10,984±25 m (95%) at 11.329903° N / 142.199305° E at the Challenger Deep [188]. As the deepest place on the Earth, it is constantly deserving attention of the scientific world [89]; [119]; [166]; [180]; [709]; [712]. The arc of the MT can be fitted into a square with coordinates 140°E-148°E, 11°N-25°N (Fig. 2.16.1). Geographic location of the edge of the MT along the coasts of the continents or island arcs is explained by the subduction of tectonic plates at the boundaries of their collision: Mariana, Pacific, Philippine, Caroline tectonic plates [278].

<figure><img src="image_54.png" /><figcaption>Map 2.16.1: Mariana Trench: bathymetric map</figcaption></figure>

MT is one of the 37 known deep-water trenches of the World Ocean, 28 of which located in the margin areas of the tectonic plates of the Pacific Ocean [607], and the rest is as follows: 5 are located in the Atlantic Ocean [63] and 5 in Indian Ocean [215]. With a total length of about 2,550 km, MT creates a complex of the deeply interrelated factors and processes of tectonic plates. MT is a strongly elongated, arched in plan and lesser rectilinear, narrow topographic depression of the seafloor, crescent-like in its geometric plain shape. It is located in the west Pacific Ocean, 200 km to the east of the Mariana Islands, eastwards of the Philippine Islands and China, Fig. 2.16.1. Its transverse profile is asymmetric: the slope is higher on the side of the Mariana island arc, dissected by the deep underwater canyons.

Seafloor bathymetry varies seaward of the trench axis: the Challenger Deep area, southwest of Guam, is associated with the deepest relief sounding of 10,984 ± 25 m (95%) at 11.329903°N / 142.199305°E,
and axial vertical loading [188]. The Challenger Deep is up to 2 km deeper than the average depth along the axis of the MT [178]. MT has complex geomorphic structure and unevenness in profiles stretching SW where its geomorphological structure varies. The geomorphology is subjected to various phenomena that affect its shape. These include bathymetry (depths), tectonics plates, geology (sedimentation) and geometry (slope steepness). Stretching for hundreds kilometers, MT is formed as a result of the subduction of the Pacific Plate beneath the PSP. The subduction boundaries have a classified 'Mariana type' where the coupling between the two plates is either weak or non existent [724]. Westward slope of the MT presents a barren trench wall with no uplift, while eastern part is the border along which western edge of the Pacific Plate is subducting beneath the smaller Mariana Plate. As a result, the geomorphic feature of the MT is its very steep slopes and depths of 3-5 km width of the bottoms rarely exceeding 5 km in breadth [567].

MT has complicated steps of various shapes and sizes, caused by active tectonic and processes of sedimentation. The steepness of its geomorphic depth averages in 4-5°, but its individual parts can be limited to steeper slopes as subjects to the gravitational flow system of the submarine canyons and valleys[312] [574]; [226]. Complex distribution of the various geomorphic material on the adjacent abyssal plains of the ocean contributes to the formation of the geomorphic features of the particular region of the MT. The main area of the seafloor of the trench has prevailing depths of >3000 m, a seabed being the most ancient parts of the ocean floor formed in the late Jurassic. To date, research on MT and IBM arc has focused on aspects of the tectonic processes of plate subduction [166], faulting and deformation [777], [778], geometry of the lithospheric gradient dips [61], plate motion, geologic structure and depths of slabs [243]. [39], petrogenetic studies on geochemic traces of volcanic rocks from the MT pointed at its tectono-magmatic evolution.

**Bathymetry** Recent bathymetric and topographic analyses [544] indicate that thin lithosphere in the IBM region (20 km) might have been intensely deformed during tectonic evolution of the region. It may explain notable variation in microstructure of samples taken from the ground on the MT, which range from coarse granular and intensely elongated texture to fine porphyroclastic and fine-grained equigranular textures. Submarine relief of the MT is marked by complicated steps of various shapes and sizes, caused by active tectonic and processes of sedimentation. NW Pacific Ocean is specially characteristic for the vast areas of the bottom of the basins occupied by depressions >6000m. The structure of the MT has following general pattern: a major total area is being occupied by the abyssal depths (3-6 km), while the extreme depths >6 km, are smaller in comparison to the previous areas [724]. The abyssal make up relatively small part of the total area of the MT, while the 2^nd^ ones cover about 2/3 of the whole.

Depths >6000 m are confined mainly to the deepest part of the trench located in its SW part, although individual depressions to depths of 6-7 km, rarely up to 7.5 km, occur in the central part of the basins along the trench [557]. Typical depths of the Mariana seafloor are 4000-5000 m where mid-ocean ridges and deep-sea basins are formed by numerous seamounts and deep-sea depressions are located. There are differences in the velocity of the longitudinal seismic waves in the geological layers of the MT structure: water and sedimentary layer have speed of 3.5-6.2 km/s, basalt layer has speed of 6.5-7.0 km/s. The layer of gabbro and banded gabbro are located beneath. The boundary of the crust-mantle is made by a sharp increase in the velocities from 7 to 8 km/s (the boundary of the Mohorovičić (Moho)). Upper mantle is the region with velocities of 8.0-8.2 km/s [189]. Analogues of the oceanic crust on the ground are ophiolites. The average thickness of the crust of this type under the platforms is ca. 40 km [215]. The regions with bark continental type in the ocean are fragments of continental mass formed as a result of the formation of the modern ocean floor causing migration and slow movement of the trench [265]. MT has a distinctive morphological feature of the convergent plate boundaries, along which lithospheric plates move towards each other, located about 200 km parallel to a volcanic island arc [308].
Composite overlay: 3D topographic mesh model on top of 2D geoid contour plot
Region: Mariana Trench
Data: ETOPO 5 arc min grid, Geoid Image 9.2 5 min grid

Perspective view azimuth: 250/45

<figure><img src="image_55.png" /><figcaption>**Map 2.16.2:** 3D relief model: Mariana Trench, south segment</figcaption></figure>

The formation of the trench is caused by the complex interconnection of various factors [188]. A trench marks the position at which the subducting slab descends beneath another lithospheric slab [130]. General form of the bathymetry of the MT is inclined toward the margin plate being connected with the ocean basic zone. It has high and steep island or continental slopes and more low and gentle slopes from the south-eastern side. The trench has a distinctive morphological feature of the convergent Pacific plate boundaries, along which lithospheric plates move towards each other. Various narrow steps are often found on the slopes of the trench. On the longitudinal profiles of the bottom of the MT there are many deepwater detected transverse steps and thresholds connected by movements of the Earth's crust along transverse faults [777]. These thresholds can play important role in the evolution of the deep water trough. For example, they control the intensity of its filling by sediment precipitation. In this process very important are the streams of the precipitation moving along the bottom from the part of the trough which is closer to the continent [703]. At the bottom of the trough there is a transverse threshold, which can present kind of a dam, in front of which there is accumulation of a thick layer of sediment precipitation.