Here, the εᵢ are unobserved stochastic error terms with zero mean and finite variance, which may not be the same for all i. The schematic process of factor analysis includes grid generation, model setup, execution and analysis of the results with visualized correlation matrix showing the most important impacts factors according to their values. The results of the correlation matrix are shown on Fig. 4, [432]. The standardized loadings based upon correlation matrix of factor analysis of the trench (Fig. 5 [432]) show impact values for each factor.

<figure><img src="image_121.png" /><figcaption>**Figure 3.8.24:** Mariana Trench: Factor Analysis, R code A.6.26</figcaption></figure>

A {FactoMiner} library was used for cluster analysis iCLUST (Fig. 3.8.24). The goal is the same as for the factor or components analysis, but methodologically, it reduces the complexity of the data and attempts to identify homogeneous sub-groups. Here, EFA aims to extract more regular impact factors of the geologic morphological development, whereas cluster analysis and hierarchical dendrogram extract the groups and classes in a total cloud of the bathymetric observation points. Factor analysis results in illustrating cross-influences of the most notable factors: geological, geomorphological, tectonic, geographic and environmental parameters. This enabled to quantitatively analyze correlation between the actual shape of the trench and its impact factors (Fig. 6 [432]). Fm: one of several factoring methods, such as pa (principal axis) or ml (maximum likelihood), as shown on the code: A.6.26.

The ω testing was done to find out two alternative estimates of the reliability that take into account hierarchical structure of the inventory data ω (Fig. 3.8.25). It shows general factor saturation in a simple way (ωh) (left) and through 3 factors ω coefficient (right). These were called using omega function for factor analysis, R code A.6.26. The computed coefficient omega (hierarchical) (ωh) is an estimate of the general factor saturation of the performed test. Various factoring methods were tested to better describe data factors. Nevertheless, fa() function provided the best results used for common factoring (Fig. 7 [432]). The oblique rotation (rotate = oblimin) was used, which recognizes that there is some correlation between geomorphological factors affecting trench formation.
<figure><img src="image_122.png" /><figcaption>**Figure 3.8.25:** Mariana Trench: fragment of factor analysis, by R code A.6.26</figcaption></figure>

The principal axis factoring (fm = "pa") was used, as the identifying the underlying constructs in the
data was not necessary in this case. The EFA matrix is shown on Fig. 3.8.24, left. Once all cross-section
profiles are inspected, the assessment of the bathymetric sample points and other parameters (depth,
sediment thickness) were met by factor analysis correlation matrix: Fig. 3.8.24. Factor loadings (Fig. 5
in [432]) enable to assess the results of the factor solutions. Thus, one can see that sediment thickness
(factor solution = 0.65), slope angle steepness (factor solution = 0.61), and Mariana Plate location (factor
solution = 0.74) have high factor loadings >0.5 around 0.7 on the first factor (PA1).

Therefore, these factors are the most influencing and representative for the trench morphology. The
sediment thickness is mostly impacted by the slope steepness. 2 geophysical indicators were particularly
tested in the comparative analysis. It was found that slope degree and amplitude has important impact
on the sediment thickness, while aspect degree has lesser effect. The second column (PA2) reveals (Fig.
5 [432]) that calculated tg° slope angle has a factor solution = 0.98 (extremely high), following by depth
distribution (factor solution = 0.76). General factor saturation (ωh) and 3 factors ω coefficient are shown
on Fig. 3.8.25.

The location of the Caroline and Philippine Plates do not affect that much trench with negative factor
solutions (-0.79 and -0.70, respectively), by lower loading on PA2 (factor solutions = 0.25 and 0.12) and
a slight loading on factor PA1. This suggests that statistics is less related to the concept of Caroline
and Philippine Plates than Mariana Plate and its geomorphic settings, e.g. tg° slope angle steepness,
sediment thickness. On the resulting table (Fig. 5, [432]) one can see that each factor accounted for
around 20% (0.26% and 0.18%) of the variance in responses leads to a factor solution accounted for
100% of the total variance (Cumulative proportion PA2: 1.00) in the trench morphology. Finally, these
factors are correlated in-between at 0.15 and recall that this choice of the oblique rotation allowed for the
recognition of this relationship. The hypothesis testing was sufficient, with following factors having the
highest score: tg° slope angle, sediment thickness, Mariana Plate location.
### 3.8.20 ANOVA to assess hypothesis testing

The results have been interpreted on the homogeneity of variance by means of the one-way ANOVA series of tests (Fig. 3.8.26). Several ANOVA test methods were executed. The brief description of their advantages and particularities is given below. The Tukey Honest Significant Differences (HSD) multiple pairwise-comparisons was applied to the ANOVA results. The Formula 3.8.20: for Tukey's test is as follows:

$$q_s = \frac{Y_A - Y_B}{SE} \qquad (3.21)$$

where $Y_A$ is the larger of the two means being compared, $Y_B$ is the smaller of the two means being compared, and SE is the standard error of the sum of the means. Since ANOVA test is significant, the Tukey HSD test was computed by R code: A.6.28. TukeyHSD() takes the fitted ANOVA as an argument. This test enabled to perform multiple pairwise-comparison between the means of groups. The library {multcomp} was used to perform a Pairewise t-test and to calculate pairwise comparisons between the group levels with the corrections for the multiple testing using R function pairwise.t.test().

The Levene's test was used to analyse the homogeneity of variances at the next step of ANOVA testing by calling function leveneTest() in the car package: A.6.28. This is an inferential statistic used to assess the equality of variances for a variable calculated for two or more groups by Formula 3.8.20:

$$W = \frac{N - k}{k - 1} * \frac{\sum_{i=1}^{k} N_i (Z_i - Z_{..})^2}{\sum_{i=1}^{k} \sum_{j=1}^{N_i} (Z_{ij} - Z_i)^2} \qquad (3.22)$$

Where k is the number of different groups to which the sampled cases belong; $N_i$ is the number of cases in the i group; N is the total number of cases in all groups; $Z_{ij}$ is received from the value of the measured variable for the j case from the i group.

Comparing to the Pairewise t-test, the advantages of the Levene's test is its lesser sensitiveness towards the departures from the normal distribution. Afterwards, the Welch t-test was executed in parallel to the oneway.test() in experimental mode defined by the following Formula 3.8.20:

$$t = \frac{\hat{X}_1 - \hat{X}_2}{\sqrt{\frac{s_1^2}{N_1} + \frac{s_2^2}{N_2}}} \qquad (3.23)$$

The Welch-test does not require that assumption have been implemented in the function oneway.test() which make is more suitable comparing to the last one.
<figure><img src="image_123.png" /><figcaption>Figure 3.8.26: Mariana Trench: ANOVA hypothesis testing, by R code: A.6.28</figcaption></figure>

The Shapiro test was alternatively used to analyze variance of the residuals to assess a test of normality (Fig. 3.8.26) by Formula 3.8.20:

$$W = \frac{\sum_{i=1}^{n} a_i x_i}{\sum_{i=1}^{n} (x_i - \bar{x})^2} \qquad (3.24)$$

Finally, a non-parametric Kruskal-Wallis rank sum test was applied to finish with a series of ANOVA testing. The test statistic is given by Formula 3.8.20:

$$H = (N - 1) \frac{\sum_{i=1}^{g} n_i (\bar{r}_i - \bar{\bar{r}})^2}{\sum_{i=1}^{g} \sum_{j=1}^{n_i} (r_{ij} - \bar{\bar{r}})^2} \qquad (3.25)$$

where: $n_i$ is the number of observations in group i; $r_{ij}$ is the rank (among all observations) of observation j from group i; N is the total number of observations across all groups; $\bar{r}_i$ is the average rank of all observations in group; $\bar{\bar{r}}$ is the average of all the $r_{ij}$. The result have been interpreted on homogeneity of variance by means of one-way ANOVA tests. Since the p-value (Fig. 3.8.26) is less than the significance level 0.05, one can conclude that there are significant differences between the groups highlighted with "*" in the model summary, that is four tectonic plates. The Tukey multiple pairwise-comparisons test enabled to perform multiple pairwise-comparison between the means of groups and demonstrated 95% family-wise confidence level of the results. The plotted homogeneity of variances of the results are shown in Fig. 3.8.26.
**3.8.21 Euler-Venn Diagram**

Logical visualization of the results as Euler-Venn diagram (Fig. 3.8.27) was plotted as illustration of correlations between the impact factors affecting trench geomorphology. The Euler-Venn diagram shows 4 and 6 factors (Fig. 3.8.27), plotted to visualize possible crossings between the variables and the most notable parameters using `{venn}` R library by script: A.6.23

<figure><img src="image_124.png" /><figcaption>**Figure 3.8.27:** Mariana Trench: Euler-Venn diagram, R code A.6.23</figcaption></figure>

The Euler-Venn diagrams enable to analyze logical factors crossing and categories affecting mor-
phology. It shows possible logical relationships between entries of a datasets: tectonic plates, sediment
thickness; geometry of the trench (slope°, aspect class, hill shade group class derived as quantitative val-
ues from GIS); morphology; volcanism and depths. The area of the shape is proportional to the number
of elements it contains, which is useful for explaining complex hierarchies and overlapping definitions as
the case of factors affecting trench morphology (Fig. 3.8.27). Mariana plate crosses all other plates, yet
the most correlation has with Pacific Plate. Comparing crossings between categories: sediment thickness
and slope angle, igneous zones and trench slope°. Upon analysis of possible combinations, intersections
and similarities between categories, one can see the relationship between the factors (Fig. 3.8.27).

**3.8.22 Word cloud**

The integrated use of GIS, statistical methods by R scripting applied for geological data processing aimed at a multi-disciplinary approach to study spatial distribution of the impact factors affecting trench geomorphology schematically presented on Fig. 22 [422] by R code A.6.34. The interpretation and classification of the spatial data were used to estimate factors of the trench geomorphology. Cumulated flowchart of methods, approaches and data was done by `{wordcloud}` library by R code A.6.36.
## 3.9 Python language for marine geology

### 3.9.1 Advantages of Python

Python, an object-oriented, high-level programming language of general purpose applied for marine geological data analysis and data processing still remains a relatively novel approach. Usually applied in IT domain applications, such as computer data analysis or data science, Python initially released in 1991 by [640] can effectively be used in the marine geology as demonstrated in this section. Among other languages, Python has become increasingly popular [495], [533] both in industry and academia. There are various significant advantages of the Python that make it a stand-alone language among the others. The advantages of Python can be summarized in a concise way in a Tab. 3.9.1 as following answer to the main question of this subsection:

<figure><img src="image_125.png" /><figcaption>**Figure 3.9.1:** Mariana Trench: KDE of the bathymetric profiles, Python (Matplotlib and Seaborn) [428]</figcaption></figure>

The polymorphism embedded to the Python syntax enables to seamless overload standard operators to adjust their behavior in the context of the current task. Since Python is a very-high-level and constantly evolving language, it has flexible arrays. Another feature of Python is its of the inheritance that scripts lets create subclasses containing qualities of their upper-level classes. GIS plugins do not able to perform a Python-level data analysis with the same effectiveness and visualization, e.g. such Python libraries as NumPy, SciPy, Matplotlib [399].

The syntax of the Python codes include operating with both immutable objects (such as strings and tuples) and mutable sequences of mixed types of items (such as lists). Besides, the division of the code into a visible interface and run makes the coding intuitive and fast. Moreover, the important feature of the Python consists in its object-oriented approach: encapsulating the long lists of the arguments and passing them into the objects makes the implementation of the code effective and not overloading the computer memory which is freed shortly after the last reference to it has been removed [399]. Python's ability to read-in, manipulate, and write data to and from .csv files is a key feature enabling effective data analysis. Finally, syntax coding in Python can be formatted in readable and very structured style due to the long history of the language development.

In view of these advantages, Python was selected as a refined tool due to its powerful functionality and effective coding solutions. Hence, this dissertation introduces a broad application of the Python for data modelling based on the application of its embedded mathematical algorithms and statistical libraries:
<table><caption>**Table 3.9.1:** Advantages of Python language for geo data analysis and processing</caption><thead><tr><td>No</td><td>What ?</td><td>How ?</td><td>Where ? (Libraries)</td></tr></thead><tbody><tr><td>1</td><td>Beauty of graphs</td><td>Color palettes, lines and design solution</td><td>Seaborn, Matplotlib, Bokeh</td></tr><tr><td>2</td><td>Precision of data analysis</td><td>processing arrays, data frames</td><td>Pandas, NumPy, SciPy</td></tr><tr><td>3</td><td>Elegance of coding</td><td>IDLE, Jupyter Notebook</td><td>All libraries</td></tr></tbody></table>

Matplotlib, NumPy, SciPy, StatsModels [399]. The effectiveness of the data computing and visualization by Python is the key factor for applying its functionality in this dissertation. Some approaches of the scientific visualization and methods of the data analytics discussed previously by [115] were considered in this research.

Python provides an appropriate base for the geospatial analysis of the factors that affect the geomorphology of the trenches in their distinct parts. Python-based part of the research implemented the proposed statistical approach using SciPy, NumPy and Pandas libraries of Python and embedded algorithms. Applied methods of Python defines the variability in local geomorphological structure of the oceanic trenches. Spatial correlation between variables (sediment thickness, gradient degree of the cross-section profiles, geospatial location of the segments of the trench) were detected. Processing large data sets with variable numeric values: geomorphic, geologic and tectonic and geometric values (degree of slope steepness of profiles) were processed by Python and its libraries for statistical analysis and graphical plotting. The perspectives of Python application lies in its repeatability by existing codes. Proposed methodology can guide similar research on the analysis of the geologic variables in the trenches, in context of marine geologic analysis.

Python codes demonstrated in this dissertation are provided in full for repeatability of the methods in similar case studies (as summarized on Fig. 3.9.2). There are many approaches to analyze big data sets in marine science, and many of them are based on the use of GIS in various contexts: e.g. marine biology, bathymetric mapping, navigation, marine geology for exploration, etc. The novelty of using Python lies in the attempt to perform a multidisciplinary approach that combine programming with marine geological analysis. Most of the current GIS tools do not include the power functionality of Python for the statistical analysis of the large data sets in full and this dissertation contributes to the methodological development of the technical tools in marine geology.

<figure><img src="image_126.png" /><figcaption>**Figure 3.9.2:** Python-based word cloud (libraries: Wordcloud, Matplotlib)</figcaption></figure>

In view of the multidisciplinarity of the Python algorithms, methods and codes demonstrated with a case study of the given data frame can effectively be applied in other aspects of the geological data, including data taken from the R/V observations.
## 3.9.2 Installing Python libraries

The statistical analysis and programming algorithms were widely applied from the existing descriptions of the mathematical methods and computing approaches, [633]; [725]; [222]; [224]; [341]. Data processing was performed in Integrated Development and Learning Environment (IDLE), a Python editor and interpreter. Processing tables was done using multi-module NumPy fundamental library for fast computing and effective operating with arrays and matrices [575].

Current research uses the most recent version of Python: 3.7.2. installed via pip. The installation of the Python libraries and dependancies has been implemented via command line in bash terminal (bash is a Portable Operating System Interface (POSIX)-compliant shell). Because this process is not trivial, a brief explanation of the crucial installation steps is provided below. Upon installing the core packages of Python, the settings were adjusted. The package installer for Python pip version 19.0.3 was installed to ensure further installation of necessary libraries:

```
$ curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
```

Afterwards, the pip was installed using following command:

```
$ python get-pip.py
```

The next step included the installation of the Apples xcode tools:

```
$ xcode-select install. Then, two crucial python packages have been installed via the console utility pip. A NumPy, a Python based fundamental package for scientific computing providing convenient and fast N-dimensional array manipulation. A SciPy package, depending on the NumPy, has been installed as well. SciPy is used for the math computations, science, and engineering. Pandas, the most popular data manipulation package in Python has been installed for processing data in tabular format.
```

The installation of these packages was done using pip in command line bash terminal for SciPy version 1.1.0, NumPy version 1.14.5, and Pandas version 1.11.0, stepwise:

```
$ pip install sciPY $ pip install numpy $ python3 -m pip install pandas.
```

Additionally, plotting packages assisting in visualizing the data: Matplotlib and Seaborn version 0.10.0, have been installed using commands:

```
$ sudo python3 -m pip install matplotlib
```

```
$ python3 -m pip install seaborn.
```

The advantages of the Matplotlib consists in its versatility and capability of creating a significant variety of scientific plots. Seaborn package is build atop of the Matplotlib. It combines the power of the Matplotlib with more intuitive approach for the scientific data visualization. After all necessary libraries have been successfully installed, the statistical analysis has been performed using available geo dataset on the sample trenches.

## 3.9.3 Data preprocessing

The QGIS part of the research produced an optimized representative sampling dataset consisting of 25 cross-section profiles having in total 12.590 bathymetric observation points. The data were read-in as a table in a .csv format and processed by algorithms in Python libraries: Pandas for reading and processing tables, NumPy for array analysis, Matplotlib for large dataset modelling, analysis and visualization. Importing data from table with variables in Comma Separated Value (.csv) file was done using Python Pandas library, installed in the previous step.

Data were converted from the attribute QGIS tables, imported and stored into .csv tables. These included vector layers, the coordinates and thematic attribute data in numerical or string format. Further data processing was implemented by Python. These include cross-section profiles having data from various vector GIS layers used to visualize several thematic maps from the project: bathymetry, geographic coordinates and depths, information on geological provinces, tectonics, geometric properties of
the trench: steepness angles of the profiles and bathymetry, ophiolite areas, large igneous provinces and lithology, seamounts, igneous volcanic zones, sediment thickness, tectonic plates boundaries in the area.

The data.frames were created by Pandas for storing tabular 2D data. Using read_csv() function the .csv files were read into Pandas DataFrames data type. Current working folder for the Python scripting was defined as follows:

>>> import os

>>> os.getcwd()

>>> os chdir(‘/Users/pauline/Documents/Python‘).

Defining working folder is crucial, since it indicates the path to the data storage, manipulating and reading by Python libraries.

3.9.4 Pandas: a Python library for creating DataFrames

Geologic and tectonic data attributes were imported into the Python using Pandas library a csv table.
Pandas, a data management library of Python enabled perform data management and manipulation, e.g.
logical queries for spatial analysis. Pandas was used for various manipulations with geological data:
loading from csv table, viewing, editing content, analyzing data range and structure, selecting target rows
and columns, automation of the data processing, manipulation and inquiries, examining the descriptive
basic statistic on data, etc.

Therefore, the start of this project initialized with the loading an existing of the GIS data set into
a Pandas environment. The csv file was imported into the Pandas DataFrame using command: df =
pd.read_csv("Tab-Bathy.csv"). The initial table for the bathymetry (a case of Mariana Trench) had 517
rows and 18 columns. At the second step, the data were analyzed by logical queries in Pandas environ-
ment for tables. Selecting necessary columns or rows and their attributes in Pandas is relatively straight-
forward. Thus, using iloc and loc as operations for retrieving data from Pandas data frames, necessary
columns were selected for modelling by logical selections.

For example, X = df iloc[ : , 1:13] was done for the case of selecting 12 first columns in the bathymetry table. Alternative syntax would be (for the case of 4 columns) X = df iloc[ : , [1, 2, 3, 4]] for smaller data sets where the number of columns could be defined manually. Similarly, selecting necessary row during the data analysis was done using loc[] function, e.g. B = df loc[12] returned the bathymetric observation nr. 13 (starting from 0). For instance, selecting the slope classes fir geomorphic analysis across the Mariana Trench was done using following Python snippet code:

1 very Steep =data['slope_class'] ==10,

2 extreme =data['slope_class'] ==9,

3 strong =data['slope_class'] ==8,

which returns a boolean expression of True/False for all 25 profiles. Selecting a particular profile
and corresponding geologic columns for this profile was done using following code snippet (example of
profile nr. 15):

1 profile_15 =data. loc [data [profile] ==15, [sedim_thick, slope_angle, igneous_volc]]

Analyzing the geological data set is possible through the logical queries by Pandas, for example, to
find the maximal bathymetric depth, or to find a profile that has the thickest layer of sediments. This is
done by the codes:

1 # What is the maximal depth? (bathymetric analysis)

2 data['Min'].min()
certifi-2018.11.29. To validate Secure Sockets Layer (SSL) connections the following command has been implemented in the bash terminal:

```
$ sudo /Applications/Python 3.7/Install Certificates.command
```

Jupyter Notebook, an open-source web application for Python, was installed by command:

```
$ sudo python3 -m pip install jupyter
```

It is used to store and share Python codes, equations and plot visualizations with legends. Running Jupyter Notebook was done by call:

```
$ jupyter notebook. All Python scripts were processed, saved and exported via Jupyter Notebook.
```

Libraries and their dependencies were installed by pip: NumPy and SciPy:

```
$ python3 -m pip install numpy
```

```
$ python3 -m pip install scipy
```

NumPy is effective tool for processing arrays, sorting and selecting of read-in data from the tables.
NumPy creates a multidimensional array object from a given table.csv. Using Python's syntax and semantics, it operates with matrices using logical, bit-wise, functional operations with elements, and performs a series of the routines for fast operations on arrays [575]. NumPy is based on the object-oriented approach, mathematical and logical manipulations with table using 'ndarray'.

Scripts, modules and codes were written using reference semantics and built-in functions of NumPy and Python. Saved scripts included written codes that parse command lines and perform graphs plotting by executing functions and modules. The namespaces of NumPy were imported from numpy.core and numpy.lib by calling:

```
>>> import numpy as np
```

The depending libraries have been installed as well. These include Jinja2, NumPy, Pillow, PyYAML, Six, Tornado. Manipulating with tabular data stored in csv files was implemented in Pandas optimized for the high-level processing of tabular data. The Matplotlib library was installed and imported to customize plots.

Matplotlib library was selected as a main tool for graphical plotting by Python, as it enables operating statistical analysis and plot figures using pyplot functions: plotting and adjusting the areas, visualizing statistical outputs, selecting colors for the plots, adding labels etc. Seaborn library enables to perform refine plotting, modelling and analysis of a large set of marine geological data aimed at understanding poorly explained submarine geomorphic structure of the oceanic trenches.

StatsModels library was used for linear regression applied for data interpretation. The aim is to investigate correlation between various factors: bathymetry, geomorphology, tectonic plates and their correlation. The methodological approach include data analysis, modelling, plotting and visualizing. Statistical modelling by StatsModels include several tested algorithms:

1. weighted least square linear regression between geological variables;

2. autocorrelation;

3. design matrix;

4. Ordinary Least Square (OLS) regression;

5. quantile regression

Spatial and statistical analysis of the correlation between factors aimed at detecting of geological and geodetic factors affecting steepness and shape of the trench. Following factors were analyzed: geology (sediment thickness), geographic location of the trench, tectonic plates and depths records along the profiles: max, mean, min values, and statistical calculations of the 1st and 3rd quantiles. The study revealed correlations between sediment thickness, distinct variations in submarine geomorphology and sampling across the trench segments.
### 3.9.6 Data Analysis

The profiles were analyzed by modelling in Python. A statistical modelling aimed at the analysis of the relationships between the attribute factors use main existing statistical methods. During statistical testing and experiments, numerous approaches [70]; [714]; [585]; [584] provided by Python StatsModels and Matplotlib libraries were used as the core algorithms. The statistical analysis was done using table containing attribute values of the marine geospatial parameters. Topographic values of the profiles were derived from the table with depths of samples. Min/max and quartile values were read into table. Despite variations in density of the sample data caused by geomorphic shapes (ranging from extremely steep slope to gentle slope), it was possible to merge and integrate the results of the vector overlay.

Statistical analysis was based on Python libraries that include mathematical algorithms and common approaches for data processing and analysis [123]; [65]; [99]. The KDE analysis was carried out in Seaborn library using mathematical algorithms described by [98]. Visualized results of combining five KDE plots for the bathymetric cross-section profiles are shown on (Fig. 3.9.1). It can be seen that combining five profiles enables to compare the geomorphology of the trench and significantly increases recognition of the variations, depth and heights from plotting single profiles separately. Thus, the range of the density distribution in the elevations across the archipelago and trench can be seen (Fig. 3.9.1).

#### 3.9.6.1 Letter-value plots

The features shown on the letter-value plot (Fig. 5) are distribution of the bathymetric data samples across 25 profiles. The statistical approach is derived from the Tukey's original boxplot principle [248]. The letter-values plot shows a large number of quantiles in the bathymetric sample set, defined as 'letter values'. The layout of this plot is similar to a boxplot, modified and improved. Besides plotting a non-parametric representation of a depths distribution across the profiles, where data distribution corresponds to the observations, it plots more quantiles.

<figure><img src="image_127.png" /><figcaption>**Figure 3.9.3:** Mariana Trench: letter-value plot for data distribution by profiles, Python</figcaption></figure>

Letter-values plot gives more deep inside into the shape of the bathymetric data distribution. Hence, it can be seen clearly that the maximal depths of the Mariana Trench are located in the south-western part of its crescent. Comparing to the boxplot, the letter-value plot provides more detailed information in the tails using letter values on the bivariate big real data sets demonstrate to be more useful due to the more detailed approach on data analysis. The letter-value plots were done using Python code A.5.8.
Technical implementation of the letter values plots was based on the existing methods [248]; [246] embedded in Seaborn library. More sophisticated theoretical explanation on the boxplots is given [538]). Comparing box plots code with letter value plots, the letter value plots are better suited to the marine geological datasets, as they represent detailed information in the tails by letter values (Fig. 3.9.3) showing data distribution. The samples shown on the letter-value plot illustrate actual range of the samples (Y axe) by 25 profiles (X axe). The median, mean and quantiles are shown as horizontal lines on each box representing the set of the samples across each of the profile. The plots for the 25 profiles include reliable estimates of their corresponding quantiles (as Tukeys boxplot) showing outliers (profiles 2 and 3) labeled as observations beyond the most extreme letter values.

Calculation of the pairwise Pearson correlations and plotting correlation matrix between the geologi-
cal, geomorphic and bathymetric factors was done in R. Methods to plot correlation matrix (Fig. 4.12.4)
include R based data analysis that embed computing correlograms for the data arrays [201]. The meth-
ods and approaches of the Matplotlib and Seaborn libraries used for plotting figures by Python language
were derived from the existing literature describing statistical algorithms [263]. Finally, the geomorphic
steepness of the slopes was tested (Fig. 4.12.5) using ranking plotting algorithm available in R libraries
{ggsignif}, {graphics} and {tidverse}.

**3.9.6.2 Violin plots**

Analysis of data distribution for depths across the 1000km hydrographic profiles using violin plot approach by script. This was done using Pythons sb.catplot() function for categorical data analysis by code A.5.1. The violin plot (Fig. 3.9.4) combines a box plot with KDE aimed to analyze the distribution of the bathymetric data.

<figure><img src="image_128.png" /><figcaption>**Figure 3.9.4:** Mariana Trench: violin plots on depth ranges, Python, by code A.5.1 [415]</figcaption></figure>

Inside the violin quartile and whiskers are shown from the boxplot. As violin plot uses the KDE, the wider portion of the violin plot shows, the higher density of the bathymetric observations. Vice versa, and narrow regions indicate very deep or very shallow values with lower density, rare across the bathymetric profile. The presented plot shows distribution of the bathymetric values: depths along the profiles.

The inter quartile ranges in the boxplot and higher density portion in KDE are placed in the same region of their categories within the violin plot. There is a notable decrease of depth in profiles 23, 24, 25 (Fig. 3), crossing Caroline Plate, which may be caused by changes in local geologic settings. Profiles 21 to 24 show the deepest depth in data range for profiles 21 and 22. Since tectonic attribute values differ significantly, a comparative analysis of the data variation across tectonic plates was undertaken.
### 3.9.6.3 Hierarchically-clustered heatmaps

Cluster map was plotted by Seaborn function seaborn.clustermap() by Python code A.5.2 to group bathymetric values by ranges in 25 profiles. Cluster map is a method applied in this data analysis showing a matrix plot dataset represented as a hierarchically-clustered heat map. Core technique of the clustermap implies graphical representation of the bathymetric data ranging from 0 to -10.000 m, where samples are located in cells of a matrix, represented as colors (Fig. 3.9.5).

<figure><img src="image_129.png" /><figcaption>Figure 3.9.5: Mariana Trench: heatmap for the bathymetric data distribution by profiles. Python</figcaption></figure>

Structured representation arranges depth values by their semantic similarity using clustering method ignoring outliers in colormap limits. To plot clustermap, Python assigns each of the 518 samples to a specific cluster of depths, divided into 6 groups with max cluster of depths >7.500 m. Cluster centers serve as local target points with largest node resting at the center and attracting other values of depths that belong to this cluster. Clustermap (Fig. 3.9.5) highlights groups of samples with similar depths by colors assigned as increasing darkness to the deepest values.

### 3.9.6.4 Histograms plotted by Python

In a general mathematical sense, a histogram is a function $m_i$ that counts the Nr. of samples that fall into each of the groups (bins), while graph of a histogram is a graphical way to visualize a histogram. Thus, if $n$ is the total number of samples and $k$ is the total number of bins, the histogram $m_i$ meets the following conditions, Formula 3.9.6.4:

$$n = \sum_{i=1}^{k} m_i \qquad (3.26)$$
The data set contained environmental variables separately for tectonic plates to be analyzed. This required merging columns by category tectonic plates. Therefore, columns with the plate names were melted into one class to enable data analysis by plates. The data were processed by code A.5.4. The histograms show underlying frequency distribution of a submarine topography of the trench (Fig. 3.9.6). It allows data distribution (normal), as well as outliers, skewness. The data are distributed and colored by tectonic plates.

<figure><img src="image_130.png" /><figcaption>**Figure 3.9.6:** Mariana Trench: histograms of data distribution by tectonic plates. Python. Source: [421]</figcaption></figure>

**3.9.6.5 Strip plots by Python**

Visualizing strip plots showing the relationship between the sediment thickness and slope angles as compared by tectonic plates was done by Python code A.5.5. The particularity of the scatter strip plots (stripplot()) lies in its nature: this is a categorical type of plot showing variable dependence (max depths by profile) upon another variable (tectonic plates). It represents depth values by 25 profiles sorted along tectonic plates. One can see the difference on data distribution. Pacific and Mariana plates are comparable, while Philippine Plate covers the majority of the data. As can be seen on Fig. 3.9.6, the majority of the observation points are located in the Pacific and Philippine plates, following by Mariana, while Caroline only covers a few points [421]. Sedimentation processes differ significantly by four tectonic plates, due to the overall complexity of the sedimentation, trigger factors and mechanisms.

**3.9.6.6 Euler-Venn diagrams by Python**

The Euler-Venn diagram were used for visualizing logical relations between geological variable. It shows modelling intersection and correlation of various factors for four cases where overlapping in values or concepts are visualized. Fig. 3.9.7 (A) shows cumulative total sediment thickness by tectonic plates. Fig. 3.9.7 (B) shows detected igneous volcanic spots. Fig. 3.9.7 (C) shows slope degree and Fig. 3.9.7 (D) shows methodological overlay showing the approaches of this work: GIS data mapping and
digitizing, geological analysis and Python and R programming languages. Fig. 3.9.7 shows modelling intersection and correlation of various factors in the Mariana Trench project. In particular, it shows A): Bathymetric observations by plates, cumulative computing; B): Sediment thickness, slope degree and closeness of the volcanic spots; C): Subdivision of the slope steepness by classes; D): Methodological approaches. The Euler-Venn diagram was done by Python code A.5.26.

<figure><img src="image_131.png" /><figcaption>Figure 3.9.7: Mariana Trench: Euler-Venn diagrams, Python [415]</figcaption></figure>

### 3.9.6.7 Regression Analysis

The regression models propose that $Y_i$ is a function of $X_i$ and $\beta$, with $e_i$ representing an additive statistical error term (residual) that stands in for umodeled determinants of $Y_i$ or random statistical noise, Formula 3.9.6.7:

$$Y_i = f(X_i, \beta) + e_i \quad (3.27)$$

Initially, 25 profiles were visualized by area chart and facet grid (Fig. 3.9.8). This gives an overview of the geometry (shape and steepness) and bathymetry (depths) of the profiles.
<figure><img src="image_132.png" /><figcaption>Figure 3.9.8: Mariana Trench: area chart and facet grid of the profiles, Python (Seaborn)</figcaption></figure>

<figure><img src="image_133.png" /><figcaption>Figure 3.9.9: Mariana Trench: facet grid of the regression analysis of the profiles, Python</figcaption></figure>
Second, the regression analysis for the 25 profiles (Fig. 3.9.9) has been done using `lmplot()` function which is a figure-level function that combines `regplot()` and `FacetGrid()` functions of the Matplotlib, Python. The regression analysis was done using Python code A.5.7.

### 3.9.6.8 Cluster heatmap and hexagonal data distribution and Kernel Density estimation

Hexagonal mapping is another useful approach for analysis of the big data sets when there are many observation points that may overlap. In this case, the data set comprised 12.950 points. Hexagonal binning that plots density rather than points showing concentration between data values in specific pro- files (Fig. 3.9.10, right). Depth points are binned into gridded hexagons and distribution (the number of depths ranges per hexagon). As a result, it is displayed (Fig. 3.9.10, right) using the density of color within the hexagons. The KDE is a non-parametric approach that visualized the probability of the density of a depth variables by profiles (Fig. 3.9.10, left). Joint Hexagonal and KDE plots are drawn by Python code A.5.9.

<figure><img src="image_134.png" /><figcaption>**Figure 3.9.10:** Mariana Trench: hexagonal and KDE plots by profiles. Python code A.5.9</figcaption></figure>

The color palette was selected as reversed, i.e. the most deep values are visualized as light purple values, and vice versa. The x axis shows the bathymetric profiles, the y axis shows the observation points in each profile. The color shows the depth recorded values. The left y axis shows the sub-division of the observations into hierarchical dendrograms starting from the two groups and soon, grouped based on the similarity of the depths. The records of geologic data (sediment thickness), submarine topography (slope angle degree and steepness by profiles) and bathymetry (absolute depth values: maximal and mean) on the map was also analyzed with respect to relationship in the dependence of the parameters.

### 3.9.6.9 Correlograms by Python

Correlograms, or covariance matrices, give the background for all standard multivariate techniques. Correlograms allow to check randomness of data, and analyze data structure [176]. Three correlation methods research were tested (Pearson, Kendall and Spearman to visualize the relationship between the trench variables and representing a matrix of correlations schematically in forms numbers placed within the shaded squares (two of the three methods are shown on Fig. 3.9.11).
<figure><img src="image_135.png" /><figcaption>Figure 3.9.11: Mariana Trench: correlation matrices for bivariate analysis, Python</figcaption></figure>

The Unweighted Pair Group Method with Arithmetic Mean (UPGMA) algorithm constructs a rooted tree (dendrogram) that reflects the structure present in a pairwise similarity matrix (or a dissimilarity matrix). The mean distance between elements of each cluster is computed by Formula 3.9.6.9:

$$ \frac{1}{|A|*|B|} \sum_{x \in A} \sum_{y \in B} d(x,y) \qquad (3.28) $$

The numbers give the degree of the correlation, ranging from -1.0 to 1.0. In the shaded rows, each cell is given the assigned color, explained in the legend, subdivided into four classes ranging from two to four depending on the sign of the correlation. The intensity of color is scaled in proportion to the magnitude of the correlation. Several variations have interpreted the criterion of similarity of the properties and inter-relationships. A represented clustering of the matrix (a. k. a. heat map), made by Python code A.5.10, shows the variables with the displayed correlations. Plotting correlation matrix induces a partial-ordering on the geological variables as contiguous clusters. The covariance matrix $K_{xx}$ is the matrix whose (i, j) entry is the covariance estimated by Formula 3.9.6.9:

$$ K_{x_ix_j} = \operatorname{cov}[X_i, X_j] = E[(X_i - E[X_i])(X_j - E[X_j])] \qquad (3.29) $$

where the operator E denotes the expected value (mean) of its argument. The associated names of the environmental variables (slope angles, sediment thickness, max/min and mean depth values) are shown on the correlogram (Fig. 3.9.11) annotated on the axes. The matrices were done by NumPy and Pandas objects read in as tables and imported by Seaborn library. The data were processed by Python code A.5.6. The correlograms show dependancies between the factors affecting trench based on the relationships of the values.

The effectiveness of the correlograms lies in its properties: the diagonal shows distribution of each of the geological variables by a representative square plot which helps to analyze the relationship between the pairs of numerical variables within a matrix. Correlation between each pair of variables is visualized through a numerical symbols and highlighted by colors. The use of both Pearson and Kendall correlation techniques (Fig. 3.9.11) is used for exploratory analysis to observe a matrix of geological variables.
### 3.9.6.10 Data distribution

The box plots were made to show data distribution by 25 profiles via Python code A.5.11 based on the existing statistical methods [177]; [34]; [538]. Vertical lines extending from boxes (whiskers) indicate variability of depths outside the upper and lower quartiles.

<figure><img src="image_136.png" /><figcaption>Figure 3.9.12: Mariana Trench: box-and-whisker plot for data distribution by 25 profiles. [419]</figcaption></figure>

The depth ranges increases along the axis of the trench, from north to south, with a constant slope gradient, until it reaches depths >11,000 m (profiles 20 and 21, Fig. 3.9.12). Correlation between the factors is higher in the SW of the trench and decreased towards SW. The statistical analysis of samples of the dataset shown correlation between depths and location of the profiles.

### 3.9.6.11 Probability of the depths distribution by KDE plots

An implementation of the fundamental frequency estimation is presented by KDE. The algorithm of KDE is based on a frequency-domain approach. It was applied to visualize probability of the depth ranges and bathymetric patterns in various segments of the trench, Fig. 3.9.1 and 3.9.13.

Mathematically, KDE is a non-parametric way to estimate the probability density function of a random variable. The $(x_1, x_2, \dots, x_n)$ is a univariate independent, identically distributed sample drawn from distribution with a density $f$. Estimating shape of this function $f$ is based on its kernel density estimator by Formula 3.9.6.11:

$$ \hat{f}_h(x) = \frac{1}{n} \sum_{i=1}^{n} K_h(x - x_i) = \frac{1}{nh} \sum_{i=1}^{n} K\left(\frac{x - x_i}{h}\right) \qquad (3.30) $$

where $K$ is the kernel, i.e. a non-negative function and $h > 0$ is a smoothing parameter, the bandwidth. This method was implemented using Python code A.5.16. Python libraries Pandas, Matplotlib, Seaborn and OS were used to process data by an embedded algorithms to obtain probability frequency.
<figure><img src="image_137.png" /><figcaption>**Figure 3.9.13:** Philippine Trench: KDE of the bathymetric profiles, Python (Matplotlib and Seaborn)</figcaption></figure>

### 3.9.6.12 Data sorting and grouping

A geomorphological analysis (Fig. 3.9.14), based on Python code A.5.12, uses computed values of the slope steepness° by profiles in series of 25 transects assuming constant values of the slope aspect, using depths and sample points as a reference.

<figure><img src="image_138.png" /><figcaption>**Figure 3.9.14:** Mariana Trench: stem plot of the slope gradient steepness by 25 profiles.</figcaption></figure>

The analysis shown that profiles 6, 9, 3, 7, 23 and 24 have similar steepness and can be grouped as the least steep, as shown by the reverse values of the angles tg°. Conversely, profiles 20, 21, 22, and 18 have the deepest which can be explained by changes in geometry of the trench at various geospatial locations.
### 3.9.6.13 Hierarchical dendrogram and clustermap

The Hierarchical Cluster Analysis (HCA) as shown on Fig. 3.9.15, was done using SciPy embedded algorithm of clustering based on the weighted average linkage clustering algorithm, Weighted Pair Group Method with Arithmetic Mean (WPGMA). Mathematical background to the data partition is as follows. The WPGMA algorithm constructs a rooted tree dendrogram that reflects the structure presented in a pairwise distance similarity matrix. At each step, the nearest two clusters, i and j, are combined into a higher-level cluster $i \cup j$. Then, its distance to another cluster $\kappa$ is simply the arithmetic mean of the distances between $\kappa$ and members of $i \cup j$: Formula 3.9.6.13:

$$d_{(i \cup j, \kappa)} = \frac{d_{(i, \kappa)} + d_{(j, \kappa)}}{2} \qquad (3.31)$$

It demonstrates an approach of the method of cluster analysis used to build a hierarchy of bathymetric clusters based on the segmentation of the topographic features among 25 cross-section profiles. The dendrogram (Fig. 3.9.15) shows geomorphic similarity among profiles crossing the trench. Specifically, it illustrates the subdivision of the bathymetric values within each cluster composed by the U-shaped link between a main cluster and its branches representing bathymetric grouping. The dendrogram showing the hierarchical dendrograms clusters was plotted by Python code A.5.13 in five steps.

<figure><img src="image_139.png" /><figcaption>Figure 3.9.15: Mariana Trench: hierarchical cluster dendrogram of the profiles. Python</figcaption></figure>

The choice of an appropriate metric influences the shape of the clusters, since elements may be close to each other according to one distance and farther away to the other. Two first forks of the of the U-link ('parents' of clusters) indicate which clusters were merged, while the length of each two corresponding branches visualize the distance between the children. Marginal dendrograms are represented on the Clustermap that shows another approach of data grouping in statistical analysis where main area of the plot show ranging data and marginal dendrograms clusters of the similarity groups.
### 3.9.6.14 Principal Component Analysis (PCA)

The PCA was made by PCA function from Scikit Learn library of Python (from sklearn.decomposition import PCA). PCA) is a statistical procedure that applies orthogonal transformation to convert a dataset of probably correlated variables to a new dataset of linearly uncorrelated variables, principal components. PCA is mathematically defined as an orthogonal linear transformation that modifies the data to a new coordinate system. In this way, the greatest variance by scalar projection of the data lies on the first coordinate (1st principal component), the 2nd greatest variance on the 2nd coordinate, etc.

Using orthogonal transformation procedure embedded in PCA to convert a set of samples on sediment thickness and possibly correlated variables into a set of values of the linearly uncorrelated variables [2]. In order to maximize variance, the first weight vector w(1) in PCA satisfies Formula 3.9.6.14:

$$w_1 = \operatorname{argmax}_{\|w\|=1} = \left\{ \sum_i (t_1)_i^2 \right\} = \operatorname{argmax}_{\|w\|=1} = \left\{ \sum_i (x_i * w)^2 \right\} \quad (3.32)$$

The n^th^ component is found by subtracting the 1st k-1 components from X by Formula 3.9.6.14:

$$\hat{X}_k = X - \sum_{s=1}^{k-1} X w_s w_s^T \quad (3.33)$$

Using PCA is especially useful technique in case of marine geological large datasets and other applications of the big geodataset with many variables for each sample for exploratory data analysis [301]. The PCA enables to better visualize variations: sediment thickness, slope steepness and depth ranges.

<figure><img src="image_140.png" /><figcaption>**Figure 3.9.16:** Mariana Trench: PCA of the sediment thickness by 25 bathymetric profiles, 3D view.</figcaption></figure>

The PCA on the bathymetric spatial series was performed to assess the overall spatial geomorphic variability of the cross-section profiles and their similarities (Fig. 3.9.16). For this calculation the sediment thickness as a categorical value of the studied area was used.
### 3.9.6.15 3D scatterplot matrices for fata correlation

The analysis data by correlation was performed using 3D scatterplot showing dependancies between the environmental factors, Fig.3.9.17. 3-dimensional scatterplots (3D plots) enable to discover relationships between three variables at the same time. Geometrically transformed display 3D techniques of Python aims at finding transformations of the 3-dimensional datasets.

A point affine projection of the dataset into a 3D space represented in classic (X, Y, Z) format is a technique approach of the 3D scatterplot matrices. This is a data visualization method which links 3 factors, encouraging and imploring to access possible causal relationship between the plotted variables. The graph is made by means of the ax.scatter() function of the Matplotlib library using Python code A.5.14 in 3 steps.

<figure><img src="image_141.png" /><figcaption>**Figure 3.9.17:** Mariana Trench: 3D scatterplot of the relationships between environmental factors</figcaption></figure>

The 3D scatterplot matrices (Fig. 3.9.17) show variability of the geological, geometric and bathymetric factors (that is, sediment thickness, steepness of slope degrees and depth values) that is similar to the model by four tectonic plates. The sediment thickness is broadly correlated with the slope steepness degree, showing consistent geographic patterns by two pairs of plates: firstly, Mariana and Pacific, and secondly, Caroline and Philippine (Fig. 3.9.17).
### 3.9.6.16 ANOVA

The purpose of the ANOVA method in Python part of this dissertation was to test significant differences between the means of the marine geological variables, that is sedimentation, tectonic and geospatial factors. The definitional equation of sample variance is as follows in Formula 3.9.6.16:

$$s^2 = \frac{1}{n-1} \sum (y_i - \bar{y})^2 \qquad (3.34)$$

Where the divisor is called the degrees of freedom, the sum presents the sum of squares, the result is the mean square and the squared terms are deviations from the sample mean. The fundamental technique is a partitioning of the total sum of squares into components related to the effects used in the model. Test differences in means for groups of these variables for the statistical significance was done by existing algorithms [194]; [554]; [24]. The ANOVA is accomplished by analyzing variance: partitioning total variance into the components, due to true random error (i.e., within-group) and the components that are due to differences between the means.

<figure><img src="image_142.png" /><figcaption>Figure 3.9.18: Mariana Trench: one-way ANOVA box plots</figcaption></figure>

Computed F values (0.270259 and 0.459078) and statistical significance obtained from testing with ANOVA shows the effect of depth and model scenario on the sediment thickness estimated through the StatsModels library of Python (statsmodels.stats.anova anova_lms) by embedded function ANOVA by Python code A.5.15, and Pandas for table data processing. The correlation cluster dendrogram between a spatial series of the transects shows grouping between the profiles based on their geomorphic similarities. One-way ANOVA box plots were computed and plotted to show relationship between the selected environmental, bathymetric and geomorphological variables based on the analysis of similarities.

Deep-sea sediments have consistent correlation with the slope class of the steepness, Fig. 3.9.18 (c). Lower effect has the location of the igneous volcanic areas near the trench on the geometry of the
profiles, as referred by the angle° (Fig 3.9.18, d). While shape of the trench is less steep and absolute depths are lower in the NE part of the trench (Pacific and Mariana plates), the bathymetry is substantially deeper in its SE part (Caroline and Philippine Sea plates). These findings also support variations in the deformation of the subducting plate along the Mariana Trench [778] detected by analysis of flexural bending and normal fault features. Normal faults initiated outwards and grew toward the trench axis, and the trench relief and maximum fault throws are greater in the S (5 km and 320 m) comparing to the N and C (2 km and 200 m).

### 3.9.6.17 Flowchart network

Data analysis is aimed to find potential influence from various geological and tectonic factors affecting geomorphology of the trenches. Statistical analysis of the data set in marine geology requires adequate tools for big data processing. In this context, a Python-based methodology is combined with GIS geospatial data analysis. This thesis uses libraries Pandas, NumPy and SciPy. Data processing involves subsampling of the auxiliary masked data frames from the initial large dataset that only consists of target variables: sediment thickness, slope angle degrees and bathymetric samples. Flowchart includes 3 stages (Fig. 3.9.19) visualized as logical parts: QGIS data processing; statistical analysis; spatial analysis.

<figure><img src="image_143.png" /><figcaption>**Figure 3.9.19:** Methodological network, [428]</figcaption></figure>

1st block consisted in processing marine geological data by QGIS: data import, digitizing, data export into .csv tables, processing in Python. The cross-section transects were digitized. Their attribute tables contained numerical data on geology, tectonics, volcanism and bathymetry by sample points along each profile. 2nd block contained in data interpretation and approaches of the statistical data analysis. 3rd block presents geospatial analysis by visualizing interplay between geological and tectonic variables. Mathematical algorithms were applied by libraries [38] SciPy, NumPy.

### 3.9.6.18 Visualizing bathymetric pattern by stacked area charts

Plotting stacked area charts is one of the key approaches In marine geology to visualize depths range of the aimed to analyze, to what extent depths may reach in particular trench segment. Apart from visual clearance of the plot (Fig. 3.9.20), showing maximal abrupt depth by profiles 20 and 21 (SW of the Mariana), there are other features in this approach.

Various marine geological phenomena can be investigated in the data set by adding color ranges for stepwise visualization in the plot: the shallowest parts of the seafloor with minimal depths, 3rd quar-
tiles, mean, median depths, 1st quartile. This visualizes the variability of the geomorphic patterns by the segments that highlight bathymetric variability across the profile.

<figure><img src="image_144.png" /><figcaption>**Figure 3.9.20:** Mariana Trench: bathymetric patterns visualized by stacked area charts.</figcaption></figure>

The methodology was implemented by Python code A.5.17. Following Python libraries were used to plot stacked area charts: Pandas, NumPy, Matplotlib, Seaborn and OS.

### 3.9.6.19 Stacked bar plots

Variations in the distribution of the bathymetric data by stacked bar plots (charts) were made by following Python libraries: NumPy, Pandas, Matplotlib and OS (Fig. 3.9.21), using Python code A.5.19.

<figure><img src="image_145.png" /><figcaption>**Figure 3.9.21:** Mariana Trench: stacked bar plots on depths variations by profiles, Python.</figcaption></figure>
### 3.9.6.20 Radar charts for analysis of depth distribution

Radar chart is a special case of the statistical analysis in marine geological datasets. It is an interesting visualizing method in data analysis as graphical visualization of the multivariate data. Here they are presented in form of 2-dimensional circular chart of 6 quantitative variables with depths on axes attribute values on the circular axe: maximal, 3rd quartile, median, mean 3rd quartile and minimal values. Various values that can be visualized by profiles, which requires a facetted multi-plots technique possible in radar charts. Fig. 3.9.22 presents a series of the radar charts showing depth variation by selected profiles, plotted by libraries Math, Pandas, NumPy, Matplotlib, Seaborn and OS. The algorithm was based on Matplotlib library, Python code A.5.18.

<figure><img src="image_146.png" /><figcaption>**Figure 3.9.22:** Mariana Trench: radar charts of the variation in bathymetric data</figcaption></figure>

### 3.9.6.21 Stacked bar charts for analysis of sediment thickness

<figure><img src="image_147.png" /><figcaption>**Figure 3.9.23:** Mariana Trench: stacked bar charts showing geological factors. Python.</figcaption></figure>

Analysis of the distribution of sediment thickness by profiles and its correlation with closeness of the igneous volcanic areas as by distance is visualized by the stacked bar charts (Fig. 3.9.23) by Python code A.5.20 using libraries NumPy, Matplotlib, Pandas and OS.
### 3.9.6.22 Pie charts for circular visualization of the bathymetry vs tectonic plates

<figure><img src="image_148.png" /><figcaption>Figure 3.9.24: Mariana Trench: pie charts of the bathymetric data distribution by tectonic plates. Python.</figcaption></figure>

Regarding categorial distribution methods, a pie chart plotting is based on the analysis of the distribution of the bathymetric values by tectonic plates (Fig. 3.9.24). Circular visualization of the bathymetry showing relationship between tectonic plates and distribution of the data by pie charts was performed by Python code A.5.21 in 3 steps by libraries Pandas, NumPy, Matplotlib and OS.

### 3.9.6.23 Design matrix and model fit summary by Ordinary Least Squares

Variables of the geologic interest were stored in a table consisting of 18 rows where numeric information describes geology, bathymetry, geodesy and tectonics of the trench. To fit most of the models covered by StatsModels Python library, the design or regressor matrix was created using existing approaches [160]; [70]; [548]. The first is a matrix of endogenous variables of sediment thickness, which show the response or geological regress and varying environmental conditions: geographic location, depth or tectonic plate. The design matrix dmatrices shows the results of the first six lines representing values of explanatory variables in a set of geological attributes, Mariana Trench. The computing of the OLS by StatsModels of Python based on Formula 3.9.6.23:

$$b_1 = \frac{\sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y})}{\sum_{i=1}^{n} (x_i - \bar{x})} \qquad (3.35)$$
Where, n is the sample size; x is a constant and a scalar regressor; y is a random regressor, sampled together with x; h is the number of lags being tested. Each row of the calculated OLS coefficient estimates is an individual bathymetric profile with the successive columns corresponding to the geologic and oceanographic variables and their specific values across the profiles.

### 3.9.6.24 Quantile statistics (QQ)

Used algorithm is very straightforward with a selected function of qqplot() by StatsModels. The QQ regression is a common abbreviation for quantile by quantile statistical plot. The plot shows (Fig. 3.9.27, [424]) one quantile against another across various geological parameters: Sediment thickness; Slope angle degrees; four tectonic plates; distribution of the samples of igneous volcanic areas. Technically, the plotting was performed using Python code A.5.22 for each corresponding plot. The QQ statistics calculation has been based on the following Formula 3.9.6.24 after [513]:

$$Q = n(n + 2) \sum_{k=1}^{n} \frac{l_k^2}{n-k} \qquad (3.36)$$

Where, n is the sample size; rho is the sample autocorrelation at lag k, and h is the number of lags being tested. The comparison of the six subplots enables to analyze the form of their shape against a straight line. The quantiles are bathymetric sample observations with geologic attribute values placed in the ascending order. The QQ statistics is used over the pool of the sampling data to study their distribution. A QQ statistic is a visual representation of the quantiles of a standard normal distribution of the geological data set across the trench, showing their variation in space.

Quantile regression estimates conditional median and other quantiles of the response geological variables. Upper two rows of the plot show (Fig. 4 in [424]) data distribution for sediment thickness (m) versus geologic parameters across tectonic plates, data distribution for the cumulative sediment thickness and slope angle degree by profiles. The quantile regressions were plotted for the following variables: A) Sediment thickness; B) Slope angle degrees; C) Pacific Plate; D); Philippine Plate E) Mariana Plate; F) Volcanic spots, Fig. ?? by Python code A.5.24 by StatsModels.

<figure><img src="image_149.png" /><figcaption>Figure 3.9.25: Plotted QQ statistics for the data distribution</figcaption></figure>

Essentially, quantile regression is another approach of the linear regression used for the conditional median of the response geologic variable given changing bathymetric values southwards across the
trench. The quantitative results of the quantile regression (Tab. 3 in [424]) show computations for quantile regression for the sediment thickness against geologic parameters and tectonic plates: A) Pacific Plate B) Philippine Plate C) Mariana Plate D) Caroline Plate E) Cumulative sediment thickness F) Slope° by profiles.

<figure><img src="image_150.png" /><figcaption>**Figure 3.9.26:** Quantile regression plotted for sediment thickness (m) versus geologic parameters</figcaption></figure>

Each QQ profile has a given value for data distribution by quantiles across the trench profiles. The distribution of the geologic residuals show data correlation for several cases: frequency of data distribution by plates, sediment thickness, range of depths for max values and geomorphic shape as slope°. The results represent the computed numerical values of the quantile regression.
### 3.9.6.25 Weighted Least Squares

A WLS for the geological variables analysis data distribution: by tectonic plates, sediment thickness, bathymetry and slope°. Following variables are visualized: A) Pacific Plate, B) Philippine Plate, C) Mariana Plate, D) Sediment thickness, E) Depths (max); F) Slope angle degree.

A WLS is a statistical approach representing a special case of the generalized least squares. WLS is a standard approach in regression analysis to approximate the solution of the overdetermined systems for complex marine geological systems. The least squares algorithms has two sub-types: linear or ordinary least squares and nonlinear least squares.

<figure><img src="image_151.png" /><figcaption>**Figure 3.9.27:** Weighted Least Squares plotted for data distribution.</figcaption></figure>

In scope of this dissertation, only linear least squares were tested: OLS and its generalization, a WLS. The off-diagonal entries of the correlation matrix of geological residuals are null variances of the observations, yet unequal along the covariance matrix. The calculation is based on the principle of the Gauss-Newton algorithm that solves a non-linear least squares problem by modifying a Newton's method for finding a min of a function. The computation is based on general approach of the existing
equation [59], Formula 3.9.6.25:

$$S(\beta) = \sum_{i=1}^{m} r_{i}^{2}(\beta) \qquad (3.37)$$

Where m functions $r = (r_1, \dots, r_m)$ (residuals) of n variables $\beta = (\beta_1, \dots, \beta_n)$, with $m \ge n$

The calculations of the WLS for the data set shows computations of the WLS modelling for the data distribution by plates, [424], done according to the reported procedures [665]; [696] by the Python code snippet A.5.23.

### 3.9.6.26 Isotonic regression

The statistical numerical method of the isotonic regression was applied as a non-metric multidimensional scaling aimed at fitting a line to a sequence of the observations provided the line has variation in forms, that is non-decreasing and or non-increasing everywhere across the observations. Furthermore, the line lies most close to the observations pool. The concept of the isotonic regression has been introduced by [339] and further developed by [53]. The principle of the computation algorithm of the isotonic regression lies in the following Formula 3.9.6.26 of the problem of Quadratic Program (QP):

$$\min \sum_{i=1}^{n} w_i(x_i - a_i)^2 \qquad (3.38)$$

subject to $x_i \le x_j$ for all $(i,j) \in E$, where $x_i$ and $x_j$ are constraints; E are the edges or the is the set of pairs (i, j) for each constraint; w is a weights vector; n is a number of observations. The isotonic estimator, g minimizes the weighted least squares-like condition by Formula 3.9.6.26:

$$\min_{g \in A} \sum_{i=1}^{n} w_i(g(x_i) - f(x_i))^2 \qquad (3.39)$$

Where, A is the set of all piecewise linear, non-decreasing continuous functions; f is a known function. Further references on isotonic regression and its applications are given by ?? . The presented plot of the isotonic regression (Fig.3.9.28) shows a non-decreasing approximation of a function while minimizing the mean squared error on the training bathymetric data set of the 25 profiles.

<figure><img src="image_152.png" /><figcaption>Figure 3.9.28: Mariana Trench: isotonic regression for variables, Python [415]</figcaption></figure>

The benefit of this kind of a model is consists in the flexibility of the model, that is, the function does not assume any form for the target function such as linearity. Thus, the real data o the observed values
by 25 profiles can be compared with the isotonic curves, as can be seen on the isotonic regression graph for the selected variables: geological data on sediment thickness layer (A) and maximal depths recorded by 25 profiles (B) (Fig. 3.9.28). Further references are given by [363].

### 3.9.6.27 Dynamic regression model: SARIMA

The methodology of the dynamic regression model is based on the StatsModels embedded algorithm [665]. The abbreviation State Autoregressive Moving Average (SARIMA) stands for the model, initially developed by [15]. The concept of the application of the SARIMA time series estimation and post estimation lies in the following. Changes in variables taken as time lags in classic dynamic regression models are applied towards profiles (1 to 25). Unlike in time series case when ARIMA fits the univariate models with time-dependent disturbances, this case applies space-dependent disturbances, crossing sampling (X axe), [424]. Given a time series data $X_t$ where t is an integer index and the $X_t$ are real numbers, an ARMA (p', q) model is given by Formula 3.9.6.27:

$$ \left(1 - \sum_{i=1}^{p'} \alpha_i L^i\right) X_t = \left(1 + \sum_{i=1}^{q} \theta_i L^i\right) \varepsilon_t \qquad (3.40) $$

The model includes both dependent and independent variables, the selected type of SARIMA was SARIMAX (Python code A.5.25). The first group consists in changing variables: geologic settings and bathymetry (depths). The 2nd group (independent variables) is presented by the profiles lags that cross the trench with the distance between each of 100 km and the length of 1000 km. This cross-section profiles are taken as independent variables. Therefore, the dependent variables differ spatially in the segments of the trench. Python code for used SARIMAX algorithm and Statespace Model Results, [424]. Tested case is a Mariana Trench tectonics: sediment thickness across the Philippine Sea Plate. The model fits univariate model of the geomorphic structure of the trench by independent values of the distribution of the bathymetric samples by profiles with dependent disturbances of depths. Fitting the model was done using Python code A.5.25.

The conducted statistical data modelling and several types of the regression analysis were aimed to compare variance in the geological datasets of the trench explained by the complex interplay of the geomorphic, geological and oceanological attributes of the data and bathymetric factors of their location at several segments. Several linear models of correlation between factors were computed, analyzed and explained by the groups of variables. Tested environmental variables of the Mariana Trench include four main geological factors (location on the tectonic plates, slope steepness degree, sediment thickness of the layer and bathymetric depth and submarine volcanism) and attributes of the 25 cross-section bathymetric profiles (mean values, maximal depths, median values and two quartile sub-division of the data sets) and the shared variances between environment and attributes.

Shared variance arises due to correlations between the factor of sediment thickness and slope angle degree, e.g. because the attributes of the sediment accumulation are influenced by the trench shape apart from the directly or indirectly depending on the oceanological conditions of the submarine currents. The dynamic regression model is possible using Python function embedded in StatsModels library: State Autoregressive Moving Average. The model shows autocorrelation of the data by bathymetric profiles. The results demonstrated a correlation between the geological variables and geospatial location of the samplings across the trench, which proves the interplay between multiple factors affecting submarine geomorphology.
**3.10 LATEX for processing bathymetric data**

Bathymetric GIS data processing by QGIS and LATEX is an experimental approach of the GIS data processing after imported through QGIS digitizing, possible LATEX using available available resources [331].

<figure><img src="image_153.png" /><figcaption>**Figure 3.10.1:** Mariana Trench: selected bathymetric profiles. Plotting: LATEX</figcaption></figure>

After a square of the area was crossed by the profiles, the .csv table was imported for processing in LATEX. The coordinated were saved in a table with three columns: elevations, latitude and longitude. Some necessary packages have been installed from The Comprehensive TEX Archive Network (CTAN). The visualization of the profiles has been performed in LATEX. Technically, following TEXcode was used: A.2.1. QGIS processing resulted in cross-section profiles.

<figure><img src="image_154.png" /><figcaption>**Figure 3.10.2:** Mariana Trench: 3D-view of the 25 bathymetric profiles. Plotting: LATEX</figcaption></figure>
# Chapter 4

## Results
### 4.1 Classification of the submarine geomorphology

Oceanic trenches present important part of the oceanic lithosphere. They are developed on the oceanward side of both island arcs and orogens that form above subduction zones. Deep-sea trenches are the largest linear depressed features of the Earths surface, notable for their depth and continuity. For example, the PCT is 4500 km long and reaches depths of 7–8km BSL. The trenches in the western Pacific Ocean are typically deeper than those of the eastern Pacific Ocean, the greatest trench depths, of 10–11km, detected in the Mariana and Tonga Kermadec trenches.

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, elongated to upside-down-ridge-like, cascade-formed, circular-like, arcuate, symmetric of irregular: left- or right-sided.

For each geomorphological type (U, V, asymmetric and so on) characteristic steepness sub-types are identified: strong, very strong, extreme, steep, very steep. Furthermore, valley slopes are classified as follows: very high, high, moderate, low based on the degree of curvature. Hence, size classes and valley slope classes are analyzed in the context of physical environment as well as tectonic and geological development of the area of trench formation. Flexural bending shape of a subducting lithospheric plate has effects on the tectonic loading at the trench axis, strength as well as faulting effects of the subducting tectonic plate. Necessarily, it is reflected in the variety of the trench shape forms.

Main factor controlling the max depth of a trench is tectonics: the age of the subducted oceanic lithosphere, since it determines the depth to the oceanic crust entering the trench. The remarkable contrast between trench depths in the east and west Pacific Ocean is largely explained by the systematic difference in the age of the seafloor in these regions. Generally, plate age (relatively young or old plates) is a prevailing factor in controlling trench cross-sectioning shape, independent from the axial sediment loadings. Trench geomorphology and width of the cross-section profiles are mainly controlled by the elastic thickness reduced by tectonic faulting near the trench axis. In turn, they are affected slightly by the initial thickness of the subjecting tectonic plate. The reduction of the tectonic plate causes significant deepening and narrowing of trench valleys.

As seen in this dissertation based on the cross-sections, trenches are generally 50–100km in width. In cross-section they form an asymmetric V-shape with the steepest slope, of 8–20°, on the side opposite the subducting ocean floor. The sediment fill of the trenches vary greatly as demonstrated in this work, from virtually nothing, as in the TT and KT, to almost complete, as in the AT. This is explained by the supply of sediment coming from the adjacent continental areas. Trench depth is also reduced by the subduction of aseismic ridges.

Bending of the lithosphere plate gives rise to the topographic bulge presented in the subducting tectonic plate on the oceanward side of the island arc. Regional rise of the seafloor bathymetry is located between 100–200km from the trench axis. It has an amplitude of several hundred m. The presence of this bulge is a consequence of the downward deflection of the subducting plate.

In this dissertation, a systematic mapping and analysis of their geometric shape forms has been conducted to detail the detailed analyses of 20 trenches of the Pacific Ocean with a diverse range of characteristics (geology, tectonics and geophysical settings).

PT: irregular sharp V-shaped trench (in plan) trench. The cross-sections of the selected segment have a strongly asymmetric view which is caused by the Philippine Islands (Mindanao) located on the west. Therefore, western part of the cross-sections has a smooth gradual decrease in depths in a uniform way from -75 to 40 km in the left flank with a slope of 31°. The 2nd segment stretches from 40 until 10 km of the left cross-section with a gradient slope of 40,60°. The last and short segment on the left (10-0 km) has a steepness of 28°. The right flank of the profile has following variation in steepness. A segment of 0-20 km has a slope of 44°, following by the fragment of 20-35 km (41,70°) and 35-50 km (25,0°).
A long segment on the right (50-200 km) has a very uniform geomorphology with almost no significant variations and dominating depths at around -6,000 BSL. The deepest segment of the PT (10-0 km on the profile from the center line) has more gentle slope with a steepness of 28° versus 44° on the right flank for the same segment. The cross-sections of the PT have asymmetric profile caused by the adjacent Mindanao Island. PT has a V-shape form in its deepest parts. Fig. 4.12.1 and 4.17.1 show vertical cross section transects perpendicular to the segment for PT. A lower placed graph shows a median stacked cross-section profile (red line) and an interval of the error bars for the measurements. Variation in the trench gradient angles, steepness and elevation variation from the western and eastern flanks are caused by tectonic settings.

**KKT:** cascade-shaped trench. Variations in the geomorphology fo the KKT are as follows. Southern part reaches -8,200 m maximal depths while northern has -7,800 m. Trench morphology in the northern termination of the southern segment of the KKT, just approaching the Bussol Strait is at the U-trending seafloor rise. Wedge-shaped form of the trench is being formed in its S part on the W trench slope. Area around the northern part of the trench identified features of the trench geomorphological system (submarine canyons, trench wedge, submarine ridges, and basins) in the N part of the trench. Wedge-like or U-shaped morphology of the trench in two parts S- and N are distinct from the inner-ward slope and in longitudinal direction along the trench axis. S profiles along the trench, comparing to the N segment, located near the intense volcanic area demonstrate erosional moats at -3000 m depths. Orthogonal bathymetric profiles across the S and N segments of the KKT identify two distinct U- and V-shaped morphologic features with affecting dominant processes: (1) in its N segment, the dominant processes along the Kamchatka Peninsula are sediment deposition; (2) S part is distinct by the processes of erosion and intense sediment transportation. N part of the trench show deeper values and less steep slopes comparing to the S segment, which refer to the erosional moats and drift of the submarine sediments. S part has deeper bathymetric ranges and steeper gradient slope. This is influenced by the intensive seismicity and volcanic activities. KKT forms more V-shaped valley in its S segment with about 3 km wide in profiles and more U-shaped form in the N part.

**KT:** elongated to upside-down-ridge-like trench. Profiles in Fig. 4.6.1 are roughly perpendicular to the trench axis. Latitude beside each profile indicates position of the axial bottom. KT has gentle shape form of the western slope off Tonga Ridge (Fig. 4.6.1, B), abrupt shape with 2,641 depth records from -6,600 to -6,800 m (Fig. 4.7.2). Comparing the deepest values >9,000 meters for the KT there are 251 samples proving that TT is deeper than KT. For seafloor depths at range from -6,000 to -5,000 KT has more values in this range: 6167 samples [420], Fig. 4.7.2. KT has gentle slope and shallow depths which can also be seen on Fig. 4.6.1. This illustrates tectonic and geological local variations, as well as different sedimentation of the KT causing variations in their geomorphic shape despite their close location.

**PaT:** irregular V-shaped left-sided trench. Bathymetric profiles of the axis of the PaT have very sharp yet irregular V-shape except for its northern portion. Such a form indicates bare bottom or very thin sediment cover of the axis. Northern portion about 25 km long north of 7°38′N has U-shaped bottom that becomes gradually wider in northward direction. Fig. 4.15.1 shows bathymetric profiles of the PT. The average dip of the landward slope is ca. 12°, whereas much steeper 10 to 50 m high scarps are occasionally take place. Barrow-like bathymetric pattern is recognized on the lower landward slope close to the trench axis. It may be a slumped block with no deep-sea terraces which have trapped sediments. If surface sediments or rocks are collapsed into the axis, the V-shaped bottom will be buried to form flat basin, unless subduction or tectonic erosion proceeds. Such avalanche appears to occur easily and frequently, as dipping angles of the lower landward slope exceeds slope failure limit in PT.

**MnT:** sinuous-shaped trench. It is a relatively young, shallow and narrow subducting plate trench (ca. 15–36 Ma), small trench relief and width. Along the trench axis, the bathymetry is deeper and narrower at the S and C sections than that of the N area. Depth and geomorphology of the slope of the MnT vary significantly for the depths range -3,500 to -4,500: minimal values are notable for the northern part of the
trench where 526 observation points (18,2%) are recorded for the depths -4,000 to -4,200 m, as shown on Fig. 4.13.2 A. S part of the trench has steeper slope gradient from the oceanward part. N part is steeper in the continental slope part (Fig. 4.13.1). Submarine terraces can be noted on the N segment of the trench (Fig. 4.13.1) at depths -2,000 m. Generally, model of the N part shows more steep degree of the oceanward slope (Fig. 4.13.1). Gradually slanting terrace on the S segment can be seen on the fragment 50-100 of the cross-section with depths from -1,100 to -1,300 m. Two segments of the trench located on the N and SE part of the trench differ: N segment has shallower values of depths comparing to the S, but more steep slope on the Luzon Island side.

**AT:** elongated. NW part of the trench on the side of the islands chain has steeper and shorter slope with located series of marine terraces. The average slopes of its northern side range between 3° and 4°, while those of its even shallower southern side are 1° or 2°

**MT:** V-shaped (in plan). Large trench relief and width. Dominating are steep (7-9°) slopes and flat bottom. Gradient slopes markedly symmetric accurate 'V'-shape form in its deepest parts. Perpendicular transects cross-sections: steeper gradient slope on the N (Fig. 4.17.1, below) flank on the archipelago's side (Fig. 4.17.1).

<table><caption>**Table 4.1.1:** Mariana Trench: variations in gradient steepness of the right flank</caption><tr><td>Segment at distance 0-28 km</td><td>Slope steepness of 40,5° from the vertical plummet.</td></tr><tr><td>Segment at distance 28-37 km</td><td>Slope steepness of 43,33° from the vertical plummet.</td></tr><tr><td>Segment at distance 37-50 km</td><td>Slope steepness of 30,43° from the vertical plummet.</td></tr><tr><td>Segment at distance 50-90 km</td><td>Arc-shape gradual increase of the elevation which stabilizes after 100 km <-4000 BSL.</td></tr></table>

<table><caption>**Table 4.1.2:** Mariana Trench: variations in gradient steepness of the left flank</caption><tr><td>Segment at distance 0-15 km</td><td>Slope steepness of 33,10° from the vertical plummet.</td></tr><tr><td>Segment at distance 15-25 km</td><td>Slope steepness of 45° from the vertical plummet.</td></tr><tr><td>Segment at distance 25-40 km</td><td>Slope steepness of 30,75° from the vertical plummet.</td></tr><tr><td>Segment at distance 40-50 km</td><td>Slope steepness of 28° from the vertical plummet.</td></tr></table>

The relief surrounding left flank slope: uneven submarine terrain comparing to its right flank with complex submarine relief. It presents accurate and steep in its shape form. Major depth samples are between -3000 - -5000 m (Fig. 4.17.2) **AT:** elongated. Geomorphology in NW part of the trench on the side of the islands chain has steeper and shorter slope with located series of marine terraces. The average slopes of its northern side range between 3° and 4°, while those of its even shallower southern side are 1° or 2°

**MT:** V-shaped (in plan). Large trench relief and width. Dominating are steep (7-9°) slopes and flat bottom. Gradient slopes markedly symmetric accurate 'V'-shape form in its deepest parts. Perpendicular transects cross-sections: steeper gradient slope on the N (Fig. 4.17.1, below) flank on the archipelago's side (Fig. 4.17.1). Slope steepness on the left flank: 33,10° at 0-15 km from the center of the vertical plummet. A segment 15-25 km has a steepness of 45°, segment of 25-40 km - 30,75°, a segment of 40-50 km - 28°. Gradient steepness of the right flank varies: the segment at distance 0-28 km has a steepness of 40,5° from the vertical plummet, 28-37 km from the start point has a 43,33° of steepness. It then gradually gentles to 30,43° until 50th km. The segment 50-90 km: arc-shape gradual increase of
the elevation which stabilizes after 100 km <-4000 BSL (Fig. 4.17.1). The relief surrounding left flank slope: uneven submarine terrain comparing to its right flank with complex submarine relief. It presents accurate and steep in its shape form. Major depth samples are between -3000 - -5000 m (Fig. 4.17.2).

**TT:** U-shaped (in plan). TT has shallower depths comparing to the KT. The deepest values of TT >9,000 meters cover 320 samples. Variations of the seafloor depths at range from -6,000 to -5,000 m (Fig. 4.7.2) that TT has 4803 samples [420]. Cross-section profiles demonstrated that TT geomorphology has steeper gradient slope on the western flank (Fig. 4.6.1, A). The results show deeper bathymetry and more seafloor roughness for the TT. Hence, TT has steep gradient of the profiles.

**MAT:** symmetric-shaped. The submarine geomorphic structure of the trench has straight shape form of the slopes with more steep oceanward forearc. Its geometry has relatively steep and strait shape which correlates with seismicity. Major depth observation samples have values of -3000 to -6200 m, seafloor is 3-5 km wide. The output model of the cross-section profiles is provided at Map 4.4.1 showing the initial bathymetric map (Map 4.4.1 B) and above a plotted series of the profiles (Map 4.4.1 A), such as oceanward backers of the trench and more steeper slope on the continental side. However, other areas, particularly the terraces were less well defined, perhaps due to the local specific geomorphic features of the study object: the geomorphology of the MAT presents a very straight steep slopes without significant curvatures. Spatial analysis performed by the GMT revealed that major depth observation points of the MAT are located in between the -3000 and -6200 meters (Map 4.4.1 and Fig. 3.3.3). The deepest bathymetry data range with 1,111 records takes 25% of the total pool of the sample observation points. As can be seen on Fig. 4.4.1, right, the mean values of the observation samples have a value of -2,200, while LMS mode indicates 7,5% of values at -4,000 to -3,500 m, and median values have values of -3,200 to -3,300 m.

**PCT:** asymmetric-shaped. Geomorphology differs in two parts. Chilean segment: deeper bathymetry and steeper gradient slope on the Nazca Plate (Fig. 4.5.1, A). Peruvian segment of the trench (Fig. 4.5.1, B) demonstrates a gentler landform on the oceanward side. A comparison of the highest data frequency for both segments: Peruvian segment has the majority of data (23%) 1,410 samples (-4,500 m to -4,700 m are the most frequent values for the Peruvian segment of the trench). It shows a peak of all data with a very steep pattern in distribution. Other data in the neighbor diapason are much lower: 559 samples (-4,700 to -5,000 m) and 807 (-4,200 to -4,400 m) for the Peruvian segment. Chilean segment has more even data distribution. Furthermore, data range: 205 samples for -4,700 m to -5,000 m (agains 559 for the Peruvian segment); 731 for -4,200 m to -4,400 m (against 807 for the Peruvian segment); 317 for the range of -4,500 m to -4,700 m (against 1,410 in the Peruvian segment). The most frequent depth for the Peruvian segment of the PCT is a range -4,000 m to -4,200 m (827 samples) against a range of -4,500 m to -4,700 m for the Peruvian segment (1,410 samples). Peruvian segment is generally deeper and steeper with abrupt slopes (Fig. 4.5.1).

**NBT:** asymmetric U-shaped. NBT: asymmetrical U-shaped cross-section dipping westwards with 35° eastward slope, and 41° slope (Fig. 4.11.2, A). The SCT has 33° slope oceanwards while 33,69° landwards increasing in relief more direct on the oceanward side. The increase in depth by NBT is more gentle: in 50 km segment it reaches 2000 m BSL (-7,000 to -5,000) on the Solomon Sea side. In the cross-section graph, the NBT is markedly asymmetric in the landward slope and has an arc form in the east (Fig. 4.11.2, B). NBT is shallower comparing to the SCT: it has median <-7,000m. The highest sample values are recorded in range -5,000 to -5,200 m (272) gradually followed by ranges -5,200 to -5,000, -5,000 to -4,800 and -4,800 to -4,600 m (number of samples: 204, 189, 157). The depth to the bottom of the NBT becomes gradually deeper in a segment of -50 to 0 m in cross-section, decreasing from -3,000 to -7,000 in WE direction, with maximum depths reaching -7,000 m and median value -8,000 m.

**SCT:** symmetric, 'V'-shaped. The SCT is symmetric with accurate 'V' form for the segment of -50 to -50 km on the graph (Fig. 4.11.2, A). The increase for SCT is abrupt in profile: for the distance 0-50 km the depths rise from -8,000 to -5,200 m. For the transect segment of -50 to 0 m in a cross-
section the depth to the bottom becomes deeper decreasing from -3,000 to -8,000 in WE direction, that is, the gradient slope is steeper. Geomorphology is explained by the context of the historic and actual geomorphic and sedimentary processes that finally affected trench formation and structure. San Cristobal is deeper comparing to the NBT with bathymetric data reaching -9,000 m while NBT has only 1 sample point <-8,000 m. The most frequent values for the SCT lie in range -4,600 to -4,800 m (217 samples), -4,600 to -4,400 (202 samples) and -4,400 to -4,200 (167 samples), Fig. 4.11.2. The majority of the observation samples for both trenches lie in range of -4000 to -5,000 m.

**HT:** arcuate barrel-shaped, trench. **HT** has trough-like shape form in its cross-section. Whilst it serves as a terrain variable in this study to examine the geomorphic landform, variation of the geomorphic slopes were compared for the trenches and following findings noted: **HT** has a trough-like flat wide bottom, steeper gradient slope on the North Island flank (Fig. 4.21.1, A). Data distribution for the **HT** (Fig. 4.20.1, B) [387] demonstrates equalized patterns for depths -600 m to -2,600 m with samples <60 for each bin, most data are detected at ca. 30-40 samples for a bin, ≤ 4%. Notable peak have data located on the shallow areas (≤ -500 m) where max values are detected for interval -400 to -300 m (258 samples): a slope of the North Island (frequency from 5%-12%). Geomorphic curvature and distribution of the seamounts near the Puysegur, Hjort and HTes reflect the interplay between the complex tectonic processes occurring at a subducting tectonic plates and variations in the tension between the spreading ridges and the transform faults [403]. Generally, a strong correlation of the trenches axe with borders of three lithospheric tectonic plates (Antarctic Plate, Pacific Plate and Info-Australian Plate) can be noted, which underpins strong correlation of the trench geomorphology with direction of the tectonic slab lineaments, as plate movements, geologic lineaments and extend of the fracture zones in the final end lead to the formation of the trench axis through the plates motions. Local geologic settings, linear extension of the tectonic slabs, complex tectonic processes and sedimentation of the **HT** might explain variations in their geomorphic structure and shape of the submarine landforms.

**YT:** V-shaped (in plan) trench. General pattern for **YT**: steep slopes and deep bathymetric records. Profiles of the axis of the **YT** have sharp V-shape. **YT** reaches -7000 m in its median curve of the profiles, while the **PT** only -6200 m, that is the **PT** is shallower. Second, **YT** has distinctly more abrupt slope and steep gradient of the profiles oceanward (Fig. 4.15.1, B) comparing to the **PT** (Fig. 4.15.1, A). [332] earlier pointed that extensive slope failure is occurring on the landward slope of the **PT** from which great amount of mass is wasted to the trench axis. Hence, if surface sediments or rocks are collapsed into the trench axis, the V-shaped bottom will be gradually buried to form a flat basin, unless subduction or the tectonic erosion proceeds. Submarine sediment avalanches occur easily and frequently, when dipping angles of the lower landward slope partly exceed slope failure limit. Inner slopes on the **YT** are more steep and abrupt (Fig. 4.15.1, B). The average dip of the landward slope is recognized as about 29°for the **YT** westwards 41°eastwards. Data distribution for the **YT** (605 samples): depth range at -5000 to -6000 m: **YT** has 53 samples. **YT** has steep slopes and generally deep sample records, deep values within range of -7000 m - -6000 m: 68 samples.

**PsT:** sharp irregular right-sided V-shaped trench. PsT (Fig. 4.21.1, B: middle plot) has asymmetric V-form with steep gradient on the E slopes and more gentle slope on the W. The results of the comparison of the median values (red line on Fig. 4.21.1 for the trenches show that selected segment of the **HT** has shallower depths with maximal not exceeding -2,500 m (Fig. 4.21.1, A), while PsT is reaching up to -6,000 m (Fig. 4.21.1, B). PsT (Fig. 4.20.1, A) has a bimodal type of data distribution with two peaks. The first peak has three notable bins range -4,200 to -4,400 m (373 samples detected, data frequency 18%), range -4,400 to -4,200 (182 samples, data frequency 9,6%) and -4,400 m to -4,600 m (216 samples, data frequency 11,5%). The second peak covers depths from -2,200 to -3,000 m with the data frequency below 7,5%. The peak corresponds to the Macquarie Arc bordering the trench slope.

**JT:** irregular-V-shaped trench (north); elongated left-sided trench (south). Comparative analysis of the two parts of the a series of cross-section profiles (Fig. 4.19.1) revealed more steep slope on the
northern part of the trench on the continental slope part beneath the frontal wedge of the North American tectonic plate. Southern part of the JT shows more gentle slope on the Honshu Island. The location of the swath profiles traversing the two segment of the JT South-West to North-East direction is shown in Fig. 4.19.1. 12 profiles were drawn for the southern segment and 14 profiles for the northern one. Highly rugged geomorphological structure of the median profiles, and altitude increase in the S-N direction can be seen on the northern part of the JT (Fig. 4.19.1). geological settings and seismicity of the area impact the trench roughness and general geomorphic shape when studying bathymetric patterns and variations by the cross-sectioning profiles of JT, which is shown on the geological map (Fig. 2.18.2) with high level of the seismicity, repetitive earthquakes and volcanism along the JT. W part of the trench (Honshu Island slope) has steeper slopes comparing to the oceanward slopes for both segments, which can also be seen on the 3D model shown on Fig. 2.18.5.

**IBT:** asymmetric trench in two different parts. Southern segment of the trench is deeper and has more accurate V-form with a steeper gradient slope on both slopes (Fig. 4.18.1, B). Northern part of the trench (Fig. 4.18.1, A) has asymmetric shape of the slope with notable submarine terrace on the western part and more straight shape on the oceanwards side (Fig. 4.18.1, A). Comparison of the oceanward flank of the both segments of the IBT show similar geomorphic shape but seamounts on the N segment (Fig. 4.18.1). The 3D model of the Izu-Bonin shows (Fig. 4.18.2) seamounts on the E part of the trench, [405].

**VtT:** trough-like W-shaped trench. Selected segment of the VtT has shallower depths with maximal $\le -5,000$ m while VT is depths with $\ge -6,000$ m. The deepest values $>5,000$ m for VtT give in total 413 values (238, 130, 29, 14, 2) samples. The geometric form of the two trenches varies: VtT has more flat wide bottom similar to a trough, with steeper gradient slope on the eastern flank (Fig. 4.8.1, A: upper plot). Neighboring surface is rather flat for VtT, surrounded by both North Fiji Basin (eastern flank) and its western flank (Fig. 4.9.2) [388]. A strong correlation of the VtT axes with borders of the 2 tectonics plates (Pacific Plate and Info-Australrian Plate) takes place. Hence, geomorphology of the VtT correlates with slabs, continuation of the plate movements, lineaments and extend of fracture zones which lead to the formation of the VtT axis determined by the plate motion. Geological settings, stretch of the tectonic slabs, tectonic processes and sedimentation of the VtT explain differences in their structure and geomorphic shape form.

**RT:** irregular-shaped: right-sided trench with differences for two segments. Median stacked profile on the S part (Fig. 4.14.1 A) show more gentle slope shape with submarine terraces, while there are no clear on N part of the forearc slope (Fig. 4.14.1 B). At the same time the depth of the forearc slope increases in the northern segment of the RT (Fig. 4.14.1 B). Certain differences in the depth ranges of the RT along the Ryukyu island arc. The depth and geometry of the slope vary considerably: maximal values of -5,500 to -5,700 m are notable for the N part of the RT where 334 samples are recorded, Fig. 4.14.2 (A). With a slight difference, S part demonstrates comparable values (329 samples) for the same depth range. More clear differences are for the depths >6,000 m. S part of the trench (Fig. 4.14.2 (B)) has only 9 samples with depths >6,800m while N part shows 41, 59 and 23 records, with a maximal values reaching -7,460 m. Moderate to shallow depths with ranges from -3,000 to -5,000 m are comparable for both parts of the RT with values not exceeding 68 samples for the N part and 42 samples for the S. The forearc basin >2,000 m and <1,000 m BSL shows more samples in the N part of the trench than in the S. In contrast, a wide terrace with depths shallower than 25 m is generally larger (161 samples) in the S part of the RT. 2 spatially different segments of the RT show that N part of the RT has deeper values comparing to the S, Fig. 2.13.1: deepest values of the C part are seen in the deep area extending seaward offshore from the Miyako Fault (Fig. 2.13.2) [392].

**VT:** crescent-shaped circular-like (in plan) trench. Bathymetry of VT is generally deeper with depths reaching -7,000 m in values in the selected segment. VT is depths with $\ge -6,000$ m. VT is surrounded by more complex submarine relief of both North Fiji Basin (eastern flank) and its western flank (Fig. 4.9.2). Generally, a strong correlation of the trench's axes with borders of the two tectonics plates
(Pacific Plate and Info-Austrlian Plate) is evident. This implies that the geomorphology of the trenches is strongly correlated with slabs, since continuation of the plate movements, lineaments and extend of fracture zones leads to the formation of the trench axis strongly determined by plates motions. Local geological settings, stretch of the tectonic slabs, complex tectonic processes and sedimentation of the VT explain differences in their structure and geomorphic shape form. Correlation between seafloor geology with VT location can be illustrated by sediment accumulation resulted in a significant load. Thick VT fill sediments may lead to the obscured morphologic expression of the outer part of the trench or deformed front. Sediment accumulation affect the lithospheric downward deflection in the vicinity of the VT and the amount of the subducted sediments.

Deep-sea trenches are one of the most enigmatic and not enough studied geomorphic systems on the Earth. At the same time, it is in deep-sea trenches that geophysicists can observe how sections of the oceanic seafloor of the old Earth's crust is subducting, and model processes that tee place in the mantle of the Earth, to see how it interacts with the oceanic crust. In view of this, the importance of the studies of deep-sea trenches is clear for Earth science. In total, about two dozen deep-sea trenches in the Atlantic, Pacific and Indian oceans are known. Their depth in general exceeds 6,000 m. The six deepest trenches of the Earth are located in the Pacific Ocean: MT (11 034 m), JT (10 554 m), KKT (10 542 m) and PT (10 540 m) trenches, as well as TT trenches (10 882 m) and KT (10 047 m).
## 4.2 Kuril-Kamchatka Trench

**Southern segment of the KKT** Comparative assessment of the bathymetry of the KKT was performed for southern and northern segments separated by the Bussol Strait. The profiling was undertaken using GMT modules 'grdimage', 'grdtrack' and 'psxy'. Modelling consists of the collected data of 10706 observation samples from 52 profiles in southern part and 12726 from 62 profiles in the northern segment. The GMT modules psrose and pshistograms were used to plot histograms and rose diagrams visualizing bathymetric variables of depths. The geology was mapped using GMT modules pscoast, grdcut, grdcontour and psxy to plot lineaments and geological objects (ophiolites, faults, earthquakes, trench, magnetic anomalies, tectonic slabs, fracture zones and volcanoes).

The results show that the southern part has deeper bathymetric values, vary in geomorphic structure and has steeper gradient slopes comparing to the north, which is caused by the seismicity, volcanism, geologic and tectonic settings. The comparison of the bathymetry in KKT shown variations in the northern and southern segments: southern part reaches -8,200 m maximal depths while northern has -7,800 m. This is influenced by the geological settings: earthquakes magnitude and seismicity are higher in the south-west. The submarine terraces and floodplains were observed at -4000 m depth forming landforms located southwards off the Bussol Strait. This geospatial analysis contributes to the development of the geological mapping with an example of the Kamchatka area, a region with high seismicity and repeated earthquakes. Technically, the comparison of the two parts of the trench was performed by means of the set of the cross-section profiles plotted by GMT in an automatic regime for northern and southern part of the trench. The morphology of both segments was then compared with aim to identify the characteristics of the trench geomorphology.

<figure><img src="image_155.png" /><figcaption>**Figure 4.2.1:** Kuril-Kamchatka Trench: median stacked profiles</figcaption></figure>

The summary interpretation of the 52 NE-SW cross-section profiles across the KKT in the southern
part (highlighted as green line on the Fig. 3.5.2) shows following results. At the S region (in the proximity of the active volcanic spots, Fig. 3.5.2), the seafloor depths are found to decrease with increasing latitude gradually while moving northwards. Trench morphology in the northern termination of the southern segment of the KKT, just approaching the Bussol Strait is at the U-trending seafloor rise. Besides, there is indication of the slab subduction along the NW margin of the trench facing the Sea of Okhotsk.

Trench-parallel, linear system of islands stretches along the southern part of the Greater Kuril Chain: Iturup Island, Urup, Shikotan and minor chain of islands. These islands provide a natural barrier to the extensive downslope sediment transport from the southern Sea of Okhotsk to the trench. To a certain extent, they are trapped behind the chain of islands and further restrict the supply of the sediments to the central part of the trench downslope. The submarine terraces are presented along the southern part of the trench axis. A common trend is that southern part of the seafloor depths are found to vary considerably, even within neighboring selected profiles.

Wedge-shaped body of the trench is being developed in its southern part on the westwards trench slope in front of the Sea of Okhotsk inner trench wall closer to the islands Kunashir, Urup and Iturup from the Kuril Greater Chain. It shows a typical and distinct morphologic impact from the nearby subduction zone. The areas of the submarine terraces interspersed with the floodplains have been observed in the southern part of the trench at -4000 m depth forming distinctive patterns on the landforms located immediately southwards the Bussol Strait. The southern part of the KKT near Kuril Basin (Fig. 3.5.2) creates conditions for the short sediment transport from the islands between the terrestrial sediments of the Sakhalin Island and the Kurils with marine sediments sinking to the trench.

<figure><img src="image_156.png" /><figcaption>**Map 4.2.1:** Kuril-Kamchatka Trench: enlarged view of the profiles</figcaption></figure>

In the southern segment, the sedimentation system goes in a transverse direction of the trench stretching: it brings the sediments from the islands located on the south of Sea of Okhotsk: Hokkaido, Kunashir, Iturup, Urup and minor islands of the southern part of Greater Kuril Chain [398]. Southern part of the trench serves as a sink for the sediment dispersal systems along the active margins of the Kuril Islands. It
is longitudinally fed by the sediments derived from the southern Kurils. The location of the compression-
extension tectonic slabs as demonstrated on Fig. 3.3.9 and average deformation of the trenches axis is
directly affected by the source of earthquakes located in the south of the Greater Kuril Chain.

**Northern segment of the KKT** Northern part of the KKT (red line segment on Fig. 3.5.1) reflects in its morphological features: combined effects of the tectonic slab subduction, collision and sedimentation processes and closeness of LIPs in northern part of the Pacific Ocean near Bering Sea. In particular they are determined by the rate of the Pacific Plate convergence, sediment supply and topography of the subducting seafloor [409]. Fracture zones oriented in NW direction from the Pacific Plate towards the trench affect sediment deposition. Sediment deposits filling the trench are controlled by the rate of the Pacific Plate convergence towards Okhotsk Plate.

The analysis and interpretation of the 62 NE-SW cross-section profiles across the KKT in the N part shows following results. The sequence of profiles crossing the northern part of the trench show truncated landforms at the canyon walls of the Greater Kuril Chain, which rise ca. 1000 m Above Sea Level (ASL) while southern part of the islands chain (Iturup and Urup islands) show ca. 400 m ASL. The area around the northern part of the trench identified features of the trench geomorphological system (submarine canyons, trench wedge, submarine ridges, and basins) in the northern part of the trench. The trench-parallel longitudinal sedimentation system carries sediments eroded from the Kamchatka orogenic system to the east, along the Kamchatka Peninsula. These deposits are then moved to the NE part of the trench, Fig. 2.1.1.

Northern part of the KKT receives the sediments transported in a southwest direction from the downslopes of the Kamchatka and northern islands. Trench-fill sediments are derived from the volcanic arcs formed by the Kamchatka Peninsula and northern segment of the Kuril islands, and adjacent volcanic basins. Sediments are transported to the northern part of the trench From the Kurils through the set of submarine canyons directed transverse to the axis of the N KKT filling it by terrigenous sediments.

Orthogonal profiles line Nr. 1 and 7, located north of the KKT in perpendicular direction off the Academy of Sciences Rise (Fig. 2.1.1) reveal steeper shape form caused by seismic settings and sedimentary accumulation patterns of the SE parts of Sea of Okhotsk. The increased number of profiles in northwards direction show shallower depths which illustrates previous discussion on the variations in the sedimentation processes of N and S segments, and the landform of the profiles.

Farther down the trench and NE of the Shiashkotan Island from the Kurils, profile line 42 and 49 cross take the form of an irregularly shaped trough of ca. 8 km width. Trench floor morphology here is characterized by the gentle slope patterns and depths not exceeding 7,400 m. On both profiles, the N wall of the trench is the island-ward sloping surface of the Kuril islands. Profile line 56 shows mainly flat and parallel form at the shallower depths that extend to about 7,200 m in the seafloor. Profile line 62 shows more shallower depth and gentle slope shape following the enlargement of the trench valley starting northwards from the Paramushir Island. Stacks of local small hills and bulges across the trench indicate multiple sediment deposition processes along the trench slope in its N part.

**Comparison of the southern and northern segments** Comparing northern and southern parts of the trench, the wedge-like or U-shaped morphology of the trench in two parts south- and northwards from the Bussol Strait are distinct not only from the inner-ward slope facing the Sea of Okhotsk and the Greater Kurils Chain, but also in longitudinal direction along the trench axis [409]. This particular may be explained by the response to the gradual changes in seafloor topography patterns. Another impact can be added by the oceanological factors, such as intensive bottom currents. Diverse types of the sediment deposits filling the trench in its northern and southern part differ in substrate content: more volcanic sediments are in the south comparing to the northern part, which can be influenced by the different seismic settings and sediment transport paths. Steeper landforms are in general observed in the southern
part of the trench comparing to those in its northern segment. In both southern and northern parts of the trench, the floor deposits are influenced by the lateral and longitudinal sediment supply.

The results of the comparative analysis of the two distinct parts of the trench located north- and southwards from the Bussol Strait show that southern part is deeper reaching -8,200 m depth while northern part has -7,800 at maximal records. The data analysis was performed to examine the spatial variation of the trench geomorphology including the interrelationships of the factors affecting its variation (geodetic, seismic, geologic and bathymetric). Variations in the earthquakes magnitude and seismisity in the study area that are higher in the south-western part of the trench, as well as to a lesser extent location in the magnetic anomalies located in the north-east, influence the bathymetric patterns that show deeper values in the southern part and geomorphic landforms distribution. The cross-section profiles across the northern part of the KKT show deeper values of the depth and less steep slopes comparing to the southern segment, which refer to the erosional moats and drift of the submarine sediments.

On the contrary, southern profiles along the trench, comparing to the northern segment, located near the intense volcanic area demonstrate erosional moats at -3000 m depths adjusting the Greater Kuril Chain. This reflects a series of recent earthquakes erupted near Shikotan Island in the southern part of the KKT. Besides, the anomalous metallogeny of the southern Kuril segment is determined by the deep geodynamics, which provided the impact of fluid energy fluxes from sub-subduction and subduction asthenospheric zones. The profiles in the southern segment of the trench show more complex pattern of the processes of erosion along the upper slope and sediment deposition comparing to the northern segment. Therefore, the landward oriented submarine ridges of the trench facing the Greater Kuril Chain form oblique landform shape towards the coast of the southern Kurils. Southern part of the trench follows the exposition to the erosion processes in NE to SW direction caused by the intense earthquakes located on the south of the Greater Kuril Chain.

Northern part of the trench affected by numerous earthquakes located on the Kamchatka Peninsula is influenced by the large amount of the accumulated sediments. Necessarily it forms the shallower depths and gentle flat slopes in the morphology of the northern part of the KKT. On the contrary, the southern part is presented by V-shape formed slopes and steeper morphology which is affected by the specific geophysical conditions of the southern part of the Kuril Islands characterized by the magnetized anomaly zones of the oceanic crust. The junction of two different segments of the Kuril Islands, southern and northern, corresponds to a sub-latitudinal magnetic anomaly. The north-east direction of the magnetic anomalies are recorded on the oceanic slope of the trench up to its island slope while the southern segment of the trench axis is oriented in parallel.

The orthogonal bathymetric profiles across the southern and northern segments of the KKT identify two distinct U- and V-shaped morphologic features with affecting dominant processes: (1) in its northern segment, the dominant processes along the Kamchatka Peninsula are sediment deposition; (2) southern part located near the Hokkaido, Iturup and Urup islands and minor island of the Greater Kuril Chain are distinct by the processes of erosion and intense sediment transportation. The explosive volcanism in different parts of the KKT explain a high mass of sediments formed by gradual sediment accumulation.

Comparing the resulting two graphs (Fig. 3.5.1 and 3.5.2), two parts of the trench located north- and southwards from the Bussol Strait show that southern part is deeper reaching -8,200 m depth while northern part has -7,800 at maximal records. It can be explained by the fact that northern part of the trench affected by numerous earthquakes located on the Kamchatka Peninsula is influenced by the large amount of the accumulated sediments. Necessarily, it forms the shallower depths and gentle flat slopes in the morphology of the northern part of the KKT. The results of the dataset analysis were compared and found out that profiles located in the southern and northern parts vary in depths and geomorphic steepness, due to the impact of the geological settings and seismicity.

In general, the profiles in the N part of the trench show deeper values and less steep slopes comparing to the S segment, which refer to the erosional moats and drift of the submarine sediments. On the contrary,
S part of the trench has deeper bathymetric ranges and steeper trench gradient slope, comparing to the N part. This can be influenced by the intensive seismicity and volcanic activities in this particular area of the trench. Furthermore, the trench forms more V-shaped valley in its S segment with about 3 km wide in profiles and more U-shaped form in the N part. The shape of the slopes and steeper morphology of the S part is affected by the specific geophysical conditions of the S part of the Kuril Islands characterized by the magnetized anomaly zones of the oceanic crust.

The V-shaped valley of the trench segment neighboring Iturup and Urup islands goes in east-northward direction where is faces the Bussol Strait. Afterwards, the trench continues direction in the N segment along the islands Shiashkotan, Kharimkotan, Onekotan, Paramushir and Shumshu and finally reaches Kamchatka Peninsula. Variations in S and N part of the trench reflect differences in local geological settings of the respecting segments. The trench is associated with tectonic subduction zones. Besides geological factors, trench geomorphology is affected by local tectonic settings. Collision of the tectonic plates can be the reason that affected both the **KKT** motion and the morphology of the subducting Pacific Plate at depth in N and S parts of the trench that could be sliced in the two distinct morphologies in its N and S parts. Distinct changes in trench geomorphology is illustrated by [545]: changes between the horizontal slab and vertical arcs. Variations in physical slab settings can be the result of the plate distortion as its shape transforms between near horizontal to near vertical and change in the velocity of the subduction plate, or a combination of both.

Various geophysical factors affect the geomorphological structure of the trench and cause variations in its morphological structure among which tectonics plays the major role in the trench formation. For the case of the **KKT**, two tectonic plate have responses to its tectonic history, which could be caused by the ongoing subduction of the major Pacific Plate under the minor Okhotsk Plate [58]. The latter is a region of the active grabens located southwards of a chain of small sedimentary basins in the Cherskii Mountains, North Russia [656].

A longitudinal main channel is usually formed on the bottom of the deep-sea trench under the influence of turbidite currents [590]. It creates favorable conditions for the distinctive movements of the sand sediments that shape large elongated accumulations of the sediments [158]. For example, in case of the Mariana Trench, there are two general types of sedimentation. The 1st one is represented by the pelagic sediments, accumulated as a result of gravitational forces moving suspended matter from the water column into the deeper parts. The 2nd type is represented by the aleurite-clay sediments. In high latitudes they may be mixed with the iceberg sediments. The speed of such terrigenous sedimentation can reach 175-200 mm/1000 years. The sediments in the bottoms of deep ocean trenches mainly accumulate horizontally sand and silt turbidites with gradational stratification. Correct analysis of possible accumulation of the sediments within trench, its underlying tectonics and geology may visualize correlation between the deep-sea deposits and changes at the sea surface. Big data sets in geology are especially well suited to quantifying correlations between trench seafloor geomorphology and sedimentation. Deep-sea trench modelling is necessary for the robust estimation of the ocean mineral resources. Estimation of the deposits in seafloor resources requires precise computation of the big geological data sets.
**4.3 Aleutian Trench**

The GMT based modelling demonstrated following results. The topography in the study area ranges between -8200 to 2000 m (Map 4.3.1). The geoid gravitational model shows the majority of values lies in the interval between -10 to 20 mGal with the lowest values along the trench. The profiles plotted in an automated regime are located with a distance of 20 km between each pair, spaced every 2 km along the profiles itself and have a total length of 400 km, that is 200 km to both sides from the trench. According to the statistical histogram modelling, the most often depth value in the study area is - 4800 with frequency 1722.

<figure><img src="image_157.png" /><figcaption>Map 4.3.1: Aleutian Trench: digitized cross-section profiles. [370]</figcaption></figure>

The derived cross-section profiles on a selected segment of the trench (a map on Map 4.3.1) includes a combination of the ETOPO1 bathymetric map with a set of the automatically drawn profiles (Map 4.3.1, lower part), and a statistical visualization of the median stacked profiles with error bars (Map 4.3.1, graph on the upper part). Four approaches for the profile linear trend modelling were tested. Finally, a surface modelling was performed using xyz modelling from the ASCII data. The results of this study demonstrated (Map 3.3.16) distribution of the local occurrence of bathymetric data along the cross-section profiles. Visualizing submarine geomorphology provided context and guidance for further geological and oceanographic discussions regarding hadal trench shape, occurrence of terraces, steepness of the slopes, location of canyons, as well as their distribution.
<figure><img src="image_158.png" /><figcaption>**Figure 4.3.1:** Aleutian Trench: modeled trend curves of the relief of the mean cross-section profile. [370]</figcaption></figure>

The bathymetry was visualized using geo color palette. The original data is derived from the sea-surface satellite altimetry measurements and ocean soundings provided by numerous global geospatial sources. Therefore, the ETOPO1 includes existing bathymetry data and uses interpolated gravity values elsewhere. The statistical analysis of the bathymetric data shown (Fig. 4.3.2) that depths with ranges between -5600 and -5400 m have occurrence of 1106 (8%); depths with ranges -5400 and -5200 m have occurrence of 846 (7%); depths with ranges -4900 and -5200 m have occurrence of 1297 (11%). The highest values demonstrated values of -4900 and -4700 with 1722 occurrences (13,2%). Significantly low values demonstrated shallow areas with depth ranges between -4500 and -3000 m with occurrence of below 200 (all of them less than 2,5%). Values with ranges shallower than -500 m naturally belong to
the coastal shelf areas near the Aleutian Islands adjusting the trench.

<figure><img src="image_159.png" /></figure>

<figure><img src="image_160.png" /><figcaption>**Figure 4.3.2:** Aleutian Trench: histogram chart on bathymetric data distribution by profiles. [370]</figcaption></figure>

Furthermore, a geomorphic analysis (Fig. 4.3.1 and 2.2.4) shown that north-western part of the trench on the side of the islands chain has steeper and shorter slope with located series of marine terraces. Several models and maps illustrate geological, geophysical and geomorphic modelling of the area including an outline of the shell scripting, the model, and visualized maps. To assist in the identification of the geomorphology of the AT, bathymetric ETOPO1 data were contoured at 500 m intervals using GMT module grdcontour (Fig. 2.2.2).

<figure><img src="image_161.png" /><figcaption>**Map 4.3.2:** Aleutian Trench: topographic surface modelling from the ASCII xyz data. [370]</figcaption></figure>

Geomorphic analysis in GMT module trend1d was applied to build statistical trends with four functional approaches (Fig. 4.3.1) and visualization of median and mean profiles (Map 4.3.1). The results of the surface modelling (Map 4.3.2 and 3.3.16) are received by modelling xyz dataset.
**4.4 Middle America Trench**

The results of the geospatial data modelling show geomorphic shape of the submarine landforms in the **MAT** and surface trends that reveal unevenness in the structure. The submarine geomorphic structure of the trench has straight shape form of the slopes with more steep oceanward forearc. Its geometry has relatively steep and strait shape which correlates with seismicity. Major depth observation samples have values of -3000 to -6200 m, seafloor is 3-5 km wide. The marine vertical gravity at the trench correlates with the tectonic slab contours. The intermediate values (20-30 mGal) are detected on the trench slopes while the highest values on the mountainous areas. The output model of the cross-section profiles is provided at Map 4.4.1 showing the initial bathymetric map (Map 4.4.1 B) and above a plotted series of the profiles (Map 4.4.1 A), such as oceanward backers of the trench and more steeper slope on the continental side.

<figure><img src="image_162.png" /><figcaption>**Map 4.4.1:** Middle America Trench: cross-section profiles on the selected segment, [413]</figcaption></figure>

However, other areas, particularly the terraces were less well defined, perhaps due to the local specific geomorphic features of the study object: the geomorphology of the **MAT** presents a very straight steep slopes without significant curvatures. Multiple factors affect **MAT** geomorphology which is formed as a results of the complex interaction between the geochemical situation, lithology, sedimentation processes, geological trigger factors and tectonic mechanisms in the study area. These factors include
among others tectonic history of the study area, strong influence of the volcanic active zone, geochemical content of the substrate. The position of the bathymetric observations in the trend analysis (Fig. 3.3.3, A) indicates the main common trend similarities in the sea floor characteristics between the geomorphic units. Thus, Fig. 3.3.3, A shows clear separation of the oceanward slope with steeper angle Cocos Plate basin, trench valley and rocky Charts block unit.

Modelling geospatial data highlighted the geomorphology and geometric shape of the trench. The geometry of the trench demonstrates its relatively steep and strait shape which correlate with previous seismic record results [674] showing the trench floor to be 3-5 km wide. The spatial analysis performed by the GMT revealed that major depth observation points of the MAT are located in between the -3000 and -6200 meters (Map 4.4.1 and Fig. 3.3.3). Frequency data distribution of the bathymetric samples shows notable changes within the MAT by the profiles. There is a certain increase of frequency in depths between -3000 and -4000 m (Fig. 4.4.1) that may be caused by the specific geologic properties of the Cocos Plate.

<figure><img src="image_163.png" /><figcaption>**Figure 4.4.1:** Middle America Trench: histograms of the samples distribution by cross-section profiles, [413]</figcaption></figure>

The deepest bathymetry data range with 1,111 records takes 25% of the total pool of the sample observation points. As can be seen on Fig. 4.4.1, right, the mean values of the observation samples have a value of -2,200, while LMS mode indicates 7,5% of values at -4,000 to -3,500 m, and median values have values of -3,200 to -3,300 m. The North-West distribution of the rose diagram (Fig. 4.4.1, left) shows steep slope on the oceanward forearc of the trench with relatively few observation samples due to the steepness on the continental slope with denser samples on the Cocos Plate where the geomorphology has more gentle form that can also be noted on (Map 4.4.1, A). A geoid gravitation model differs significantly by movements in a south-east direction along the study area with clearly detected stripe of the Pacific Volcanic Chain (Fig. 3.3.10, [413]).

Maximal values of the gravity above 20 mGal are recorded in the south-eastern part of the region southwards Nicaragua: Nicoya Peninsula and Costa Rica (colored red on the modeled map) [411], [413]. The area of Nicaragua has mainly values of 5-15 mGal (orange on the modeled map). The area of Gulf of Tehuantepec has the lowest detected gravity values in the range of -15 to -25 mGal (Fig. 3.3.10). The results indicate that marine vertical gravity at the MAT are clearly connected with the trench tectonics correlating with contours of the major lines of tectonic slabs. The intermediate values (20-30 mGal) are locate on the trench slopes (colored orange on the Fig. 3.3.13), while the highest values are detected mainly on the mountainous areas (colored rose on the Fig. 3.3.13). The findings suggest that submarine geomorphic structure of the MAT has straight shape form of the slopes with more steep slope on the oceanward forearc.
### 4.5 Peru-Chile Trench

Findings in the automatic digitizing of the transect profiles, visualized as two segments (Fig. 4.5.1) are as follows: the Chilean trenches geomorphology has deeper bathymetry and steeper gradient slope on the Nazca Plate (Fig. 4.5.1, A); on the contrary, the Peruvian segment of the trench (Fig. 4.5.1, B) demonstrates a gentler landform on the oceanward side. The comparison of the histograms on bathymetry in both segments shows that Peruvian segment has shallower depths with the maximal reaching -6,500 m, while the Chilean segment reaches -7,800 m in depth (Fig. 4.5.2). The frequency of the depth records in the Chilean segment has more unified data distribution for the depth range of -6,000 m to -7,000 m with number of sample points: 86, 88, 88, 72.

<figure><img src="image_164.png" /><figcaption>**Figure 4.5.1:** Peru-Chile Trench: profiles in Chilean (A) and Peruvian (B) parts [411]</figcaption></figure>

For the same depth diapason, the Peruvian part has 56 and 84 samples. A comparison of the highest data frequency for both segments shows: the Peruvian segment has the majority of data (23%) 1,410 samples (-4,500 m to -4,700 m are the most frequent values for the Peruvian segment of the trench). It shows a peak of all data with a very steep pattern in distribution. Other data in the neighbor diapason are much lower: 559 samples (-4,700 to -5,000 m) and 807 (-4,200 to -4,400 m) for the Peruvian segment. At the same time, the Chilean segment has more even data distribution. Taking a look at the same data range: 205 samples for -4,700 m to -5,000 m (versus 559 for the Peruvian segment); 731 for -4,200 m to -4,400 m (versus 807 for the Peruvian segment); 317 for the range of -4,500 m to -4,700 m (versus 1,410 in the Peruvian segment, which is the most frequent). On the contrary, the most frequent depth for the Peruvian segment of the PCT is a range from -4,000 m to -4,200 m (827 recorded samples) versus a range of -4,500 m to -4,700 m for the Peruvian segment (1,410 recorded samples). This demonstrates that Peruvian segment of the PCT is generally deeper and the geomorphology is steeper with abrupt slopes (Fig. 4.5.1).
<figure><img src="image_165.png" /><figcaption>Map 4.5.1: Peru-Chile Trench: digitized profiles of Peruvian and Chilean segments [411]</figcaption></figure>

<figure><img src="image_166.png" /><figcaption>Figure 4.5.2: Peru-Chile Trench: histograms for the Peruvian and Chilean segments [411]</figcaption></figure>
**4.6 Kermadec Trench**

The resulting profiles are visualized as two segments (red and green for both trenches) on Fig. 2.6.1,
left. Automatic digitizing of the cross-section profiles for KT demonstrated that KT (Fig. 4.6.1, B) has
more gentle shape form of the western slope off Tonga Ridge.

<figure><img src="image_167.png" /><figcaption>**Figure 4.6.1:** Kermadec and Tonga trenches: geomorphological modelling. Source: [420]</figcaption></figure>

The results of the comparative analysis of the two trenches show that KT has more abrupt shape with 2,641 depth records from -6,600 to -6,800 meters (Fig. 4.7.2). Comparing the deepest values >9,000 meters for the KT there are 10, 18, 48, 72 and 103, which gives together 251 observation samples proving that TT is deeper than KT. If we compare the variations of the seafloor depths at range from -6,000 to -5,000, it can be seen (Fig. 4.7.2) that KT has more values in this range: 1820, 2641, 1203 and 503 giving together 6167 samples [420]. KT has more gentle slope and shallower depths which can also be seen on Fig. 4.6.1. This illustrates tectonic and geological local variations, as well as different sedimentation of the Kermadec and Tonga trenches causing variations in their geomorphic shape despite their close location.

Oceanic lithosphere has a complex structure varying in different parts of the trenches located in the areas of the subducted tectonic plates. There are reports on typology and classification of the sediments on the seafloor [138], [139]; [148]; [349]; [506]; [722]. The approaches are generalized into standard geospatial analysis aimed at the description of the location and features of the sediments in various trenches of the ocean. Correlation between the subduction, earthquakes, sediment thickness on the seafloor of the trench and upper plate strain is discussed [239].
## 4.7 Tonga Trench

TT has shallower depths comparing to the KT. Comparing the deepest values >9,000 meters for TT there are 5, 39, 72, 95, 109 observations, that sums together 320 samples. Comparing the variations of the seafloor depths at range from -6,000 to -5,000, it can be seen (Fig. 4.7.2) that TT has 4803 numbers of samples (a sum of 1221, 2089, 1019 and 544), [420]. Automatic digitizing of the cross-section profiles demonstrated that TT geomorphology has steeper gradient slope on the western flank (Fig. 4.6.1, A).

<figure><img src="image_168.png" /><figcaption>**Figure 4.7.1:** Kermadec and Tonga trenches: models of slope gradient trends by profiles. Source: [420]</figcaption></figure>

The results show deeper bathymetry and more seafloor roughness for the TT. Hence, comparing to the KT, TT has steeper gradient of the profiles.

<figure><img src="image_169.png" /><figcaption>**Figure 4.7.2:** Kermadec and Tonga trenches: histograms of the bathymetric data distribution. [420]</figcaption></figure>
**4.8 Vanuatu (New Hebrides) Trench**

Modelling cross-section profiles for VT demonstrated (Fig. 4.8.1) that bathymetry of VtT is generally shallower with depths not exceeding -6,100 m, while VT reaches -7,000 m in values in the selected segment [407].

<figure><img src="image_170.png" /><figcaption>**Map 4.8.1:** Vanuatu and Vityaz trenches: cross-section profiles map</figcaption></figure>

The histogram shape varies by two trenches: VtT has a 'bell-shape' data distribution with two peaks at range -3,500 to -3,700 m, and the second at -4,800 to -5,000 m, that is the majority of values are in general within the range of -5,000 to -3,000 m. Here there are 1605 samples (517, 653 and 435) recorded at the first peak with depth values from -3,300 to -4,000 and 839 sample observations (238, 304, 297) at depths from -4,500 to -5,200 m [407].

<figure><img src="image_171.png" /><figcaption>**Map 4.8.2:** Vanuatu and Vityaz trenches: histograms of the cross-section profiles</figcaption></figure>
## 4.9 Vityaz Trench

The comparative analysis of the geomorphic modelling of VtT and VT shows in following findings. VT (Fig. 4.8.1, B: lower plot) has more V-form classic shape for the trench with gentle shapes on both western and eastern slopes. The comparison of the median values (Fig. 4.8.1) of two trenches shows that selected segment of the VtT has shallower depths with maximal $\le -5,000$ m while VT is depths with $\ge -6,000$ m. The deepest values $>5,000$ m for VtT give in total 413 values (238, 130, 29, 14, 2) samples [407]. The geometric form of the two trenches varies: VtT has more flat wide bottom similar to a trough, with steeper gradient slope on the eastern flank (Fig. 4.8.1, A: upper plot).

<figure><img src="image_172.png" /><figcaption>Map 4.9.1: Vanuatu and Vityaz trenches: digitizing cross-section profiles</figcaption></figure>

Surrounding relief of the two trenches varies: for VtT, neighboring surface is rather flat while VT is surrounded by more complex submarine relief of both North Fiji Basin (eastern flank) and its western flank (Fig. 4.9.2). Generally, a strong correlation of the trench's axes with borders of the two tectonics plates (Pacific Plate and Info-Austrlian Plate) is evident. This implies that the geomorphology of the trenches is strongly correlated with slabs, since continuation of the plate movements, lineaments and extend of fracture zones leads to the formation of the trench axis strongly determined by plates motions. Local geological settings, stretch of the tectonic slabs, complex tectonic processes and sedimentation of the VT and VtT explain differences in their structure and geomorphic shape form [407].
<figure><img src="image_173.png" /><figcaption>**Map 4.9.2:** Vanuatu and Vityaz trenches: 3D models</figcaption></figure>

The comparative analysis of the geophysical grids revealed structural characteristics of the two oceanic trenches, VT and VtT and adjacent oceanic crust of the north Fiji Basin. The coherency of the grids using gravity and vertical gravity gradient data confirms that grids coincides well for 10 mGal with the 3-10 mean gravity anomaly transition zone where the coherence increases. In the interval of 10-20 mGal the coherency declines to a very narrow interval, and correlation coincides until 100 mGal of the tested values. Gravity gradient coherency add new information to the interpretation of the data covering active convergent margins in Pacific Ocean. Data distribution for the VT is more even with slight increase of depth in -2,200 to -800 m with total 1,353 samples (281, 173, 230, 225, 245, 199 for the relevant bins on the histogram left) [407]. The histograms for the transecting profiles of the VT and VtT are visualized of Fig. 4.8.2.

Correlation between seafloor geology with trench location can be illustrated by sediment accumulation resulted in a significant load. Thick trench fill sediments may lead to the obscured morphologic expression of the outer part of the trench or deformed front. Sediment accumulation affect the lithospheric downward deflection in the vicinity of the trench and the amount of the subducted sediment [109]. The effective elastic thickness at the subduction zones of the tectonic plates has important impacts on the tectonic and geodynamic processes of the trench, providing restrictions for the strength of the oceanic lithosphere [755].
**4.10 New Britain (Bougainville) Trench**

NBT has asymmetrical U-shaped cross-section dipping westwards with 35° eastward slope, and 41° westward slope (Fig. 4.11.2, A) [383]. The SCT has 33° slope oceanwards while 33,69° landwards increasing in relief more direct on the oceanward side.

<figure><img src="image_174.png" /><figcaption>**Figure 4.10.1:** New Britain and San Cristobal trenches: graph model of the cross-section profiles</figcaption></figure>

The increase in depth by NBT is more gentle: in 50 km segment it reaches 2000 m BSL (-7,000 to -5,000) on the Solomon Sea side. In the cross-section graph, the NBT is markedly asymmetric in the landward slope and has an arc form in the east (Fig. 4.11.2, B). NBT is shallower comparing to the SCT: it has median <-7,000m. The highest sample values are recorded in range -5,000 to -5,200 m (272) gradually followed by ranges -5,200 to -5,000, -5,000 to -4,800 and -4,800 to -4,600 m (number of samples: 204, 189, 157) [383]. The depth to the bottom of the NBT becomes gradually deeper in a segment of -50 to 0 m in cross-section, decreasing from -3,000 to -7,000 in WE direction, with maximum depths reaching -7,000 m and median value -8,000 m. The histogram has a slightly more skewed truncated view (right-sided) with visually dominating values in shallower depths, while diminishing values below -5,000 m BSL comparing to the SCT with bell-shaped normal data distribution [383].

Mathematic approximation of the gradient slopes for the NBT and SCT applied 3 types of the Fourier functions modelling the general trend of the profile curves (median) and residuals plotted for both trenches (example for the SCT is shown on Fig. 4.11.1). Free-air gravity anomalies Fig. 2.10.3 derived from the satellite altimeter measured Sea Surface Height (SSH) have an anomaly range from -60.0 mGal and lower over the hadal trenches (deep purple color). The NBT and SCTes are distinguishable by the isolines. Lower geoid values (42-65 mGal, lime color) are notable for the regions north-east off the Solomon Islands. The tectonic and bathymetric complexity of the area (linear features, isolated constructs on the seafloor, seamounts) explains variations in the bathymetry of the NBT [383].
**4.11 San Cristobal Trench**

The SCT is more symmetric having accurate 'V' form for the segment of -50 to -50 km on the graph (Fig. 4.11.2, A). The increase for SCT is abrupt in profile: for the distance 0-50 km the depths rise from -8,000 to -5,200 m.

<figure><img src="image_175.png" /><figcaption>**Figure 4.11.1:** San Cristobal Trench: modeled trends of the slopes gradient by profiles</figcaption></figure>

For the transect segment of -50 to 0 m in a cross-section the depth to the bottom becomes deeper decreasing from -3,000 to -8,000 in WE direction, that is, the gradient slope is steeper. Geomorphology
is explained by the context of the historic and actual geomorphic and sedimentary processes that finally
affected trench formation and structure. Associated to the oceanward side of the SCT, several separated
minor canyons and furrows has been defined (Fig. 4.11.2, A).

<figure><img src="image_176.png" /><figcaption>**Map 4.11.1:** New Britain and San Cristobal trenches: segments of the digitized profiles</figcaption></figure>

The statistical comparison of the two trenches (Fig. 4.11.2) shows that the San Cristobal is generally
deeper with bathymetric data reaching -9,000 m while NBT has only 1 sample point <-8,000 m. The
most frequent values for the SCT lie in range -4,600 to -4,800 m (217 samples), -4,600 to -4,400 (202
samples) and -4,400 to -4,200 (167 samples) [383]. The majority of the observation samples for both
trenches lie in range of -4000 to -5,000 m. The geoid shows density of the topographic structures and
the upper mantle lithosphere, the geoid anomaly or height can be high irrespective of the real topography.
Therefore, as can be seen (Fig. 2.10.1), the color of geoid for the Papua New Guinea Island has similar
range (65-85 mGal) as Bismark Sea (orange color on the map).

Many factors affect deep-sea trench structure, dynamics and present geomorphology. The complex-
ity of the processes happening at subduction zones in illustrated [746]: oceanic crust and sediment is
recycled into the mantle, causing mantle melting and heterogeneity, arc magmatism, degassing and the
creation of new continental crust. However, the trench is being formed at such areas of the tectonic
plates subduction. The subduction and subduction-related volcanism are the major processes through
which geochemical components are being recycled between the Earths crust, lithosphere and mantle
[764]. A large proportion of the earthquakes and volcanoes are related to the subduction [319].
<figure><img src="image_177.png" /><figcaption>**Figure 4.11.2:** New Britain and San Cristobal trenches: histograms for bathymetric samples</figcaption></figure>

Deep-sea volcanism results from a range of processes including dehydration, melting and melt migration. The deepest known earthquakes occur in the subducted lithosphere at depths of 660–700 km but their cause is still not explained. In addition, motion and velocities of the lithospheric plates at the Earths surface are largely controlled by the buoyancy forces that drive subduction. Because of the wide variety of processes involved, subduction zones can be regarded as natural laboratories through which dynamic behaviour in the Earths mantle can be studied. These factors explain variations in the submarine geomorphology of the trenches.
**4.12 Philippine Trench**

PT (Fig. 4.12.1) has a classic symmetric bell-shape of histograms showing depth distribution with a notable sharp increase in data frequency at the interval of -5,000 to -6,000 m BSL taking together 1,117 samples (22% and 15% of the data pool, i.e. 37%).

<figure><img src="image_178.png" /><figcaption>Map 4.12.1: Cross-section transect profiles: Philippine Trench</figcaption></figure>
It indicates an abrupt gradual decrease of depths which can also be seen on Fig. 4.12.1. The cross-sections of the selected segment have a strongly asymmetric view which is caused by the Philippine Islands (Mindanao) located on the west. Therefore, western part of the cross-sections has a smooth gradual decrease in depths in a uniform way from -75 to 40 km in the left flank with a slope of 31°. The 2nd segment stretches from 40 until 10 km of the left cross-section with a gradient slope of 40,60°. The last and short segment on the left (10-0 km) has a steepness of 28°. The right flank of the profile has following variation in steepness. A segment of 0-20 km has a slope of 44°, following by the fragment of 20-35 km (41,70°) and 35-50 km (25,0°). A long segment on the right (50-200 km) has a very uniform geomorphology with almost no significant variations and dominating depths at around -6,000 BSL. The deepest segment of the PT (10-0 km on the profile from the center line) has more gentle slope with a steepness of 28° versus 44° on the right flank for the same segment.

<figure><img src="image_179.png" /><figcaption>**Figure 4.12.1:** Philippine Trench: faceted plot of the profiles. Plotting: QGIS</figcaption></figure>

Unlike MT, the cross-sections of the PT have asymmetric profile caused by the adjacent Mindanao Island. Both trenches have a V-shape form in their deepest parts. Fig. 4.12.1 and 4.17.1 show vertical cross section transects perpendicular to the segment for MT and PT. A lower placed graph shows a median stacked cross-section profile (red line) and an interval of the error bars for the measurements. Comparison of the two graphs clearly shows the variation in the trenches gradient angles, steepness and elevation variation from the western and eastern flanks of the trenches. 3D bathymetry model of the selected segment of the PT shows to the S (left) of the trench the margin is characterized by steep slope
adjacent close to the Mindanao Island, to the N by sediment accretion, and to the NW by tectonic erosion. 3D relief map of the MT showing selected segment of the Challenger Deep is characterized to the NW (left on the figure) off the trench a Mariana Ridge, to the NE a chain of seamounts, and to the south by sediment accretion.

Comparing the MT and PT, seafloor bathymetry varies more notable westward of the trench axis by the MT. West Mariana Ridge (Fig. 4.17.1) is located N off the trench axis at distance of 0-50 km, while N part of Caroline Ridge is intersected by the S transects of the trench profiles at a distance of ca.180-200 km. A prominent trench-parallel belt of seamounts visualized on the 3D graphindex[concepts]plot!3D (Fig. 2.16.2), appears at 148°-149°E. A group of seamounts eastwards off the PT (Fig. 2.11.5), stretching in a south-east direction with a narrow seamount bases and shallow apexes appears E off the archipelago at the trench axis at 130°E-135°E. However, at an area of the plate subduction 127°E-129°E, seamounts are absent and the seafloor has more unified pattern. Starting at 16°N-126°E, a ridge can be noted near the trench axis (Fig. 4.12.1). Marine free-air gravity anomaly in the PSB is presented on Fig. 2.11.4. Higher values (> 80 mGal) of the gravity fields structure are apparent at volcanic areas and Philippine archipelago, as seen in orange to red colored areas on Fig. 2.11.4. Anomalies > 60 mGal are evident in the trench areas Fig. 2.11.4, which are consistent with previous results ([753]; [649]; [688]).

<figure><img src="image_180.png" /><figcaption>**Figure 4.12.2:** Philippine Trench: strip plots by tectonic plates and geology, Python code A.5.5 [414]</figcaption></figure>

The results were compared with existing 2D and 3D geophysical and geological maps, visualization of the slab gaps or tears in the PSP (e.g. [622]; [311]; [588]; [582]; [266]; [280]; [127]) to highlight the geology, geophysical settings and geomorphic features of the submarine trenches in PSB area. Comparative analysis of the MT and PT located on the margins of the PSB is focused on modelling and mapping of the 2 oceanic trenches located on the margins of the basin of the PSP: MT and PT. The aim was to analyze and compare variability of their geomorphological shape forms.

Two segments in the southern parts of the trenches were transected by a series of the cross-section profiles. A segment of the PT is located south off Mindanao, a segment of MT – in the Challenger Deep area. Samples received through transecting were analyzed and compared. A statistical analysis shown variability in depths by samples in both trenches. PT has a classic symmetric bell-shape form of histograms showing depth distribution with a notable sharp increase in data frequency at the interval of -5,000 to -6,000 m (37% of the data pool). The cross-sections of the segment have an asymmetric view. Western part of the cross-sections has a smooth gradual decrease in depths at -75-40 km with a slope of 31°.

The analysis of the PT geomorphology by Python and R shown correlation between changes in the slope steepness and geological variables. Variations in the slope steepness correlate with sediment thickness across the trench. Variations in the landform are explained by a combination of factors: geology, tectonics, increasing depths in bathymetry, caused by the tectonic slab dynamics. The results obtained
by different statistical approaches performed using R and Python libraries are analyzed and integrated in order to provide a more detailed understanding of the character of the geomorphic structure of the PT. The cross-sectional profiles give the insight to the variations in the geomorphological shape of the PT and archipelago (Fig. 4.12.1). The extreme depths values in the data set are notable for the following profiles: profile Nr. 12 (max depth: -9.700 meters), profile Nr. 13 (max depth: -9.600 m), profile Nr. 14 (max depth: -9.400 m), profile Nr. 18 (max depth: -9.450 m). All these profiles are cross sectioning the PT in its central part where its geomorphic pattern can be characterized from steep slope (profiles Nr. 12 and 13, see Fig. 4.12.5) to very steep slopes (profiles 14 and 18). Other profiles have depths not exceeding 9.000 m.

<figure><img src="image_181.png" /><figcaption>**Figure 4.12.3:** Philippine Trench: samples distribution by tectonic plates. Plotting: Python, and R [414]</figcaption></figure>

The strip plots (Fig. 4.12.2) comprise two visualized relationship types of the data distribution, namely by seven types of the underlie geology with code given and explained in Table 1 (Fig. 4.12.2, A) and two tectonic plates: Sunda Plate and Philippine Plate (Fig. 4.12.2, B). The distribution of the samples across the two tectonic plates is uneven (Fig. 4.12.2, A). The Philippine Plate comprise more samples located in the Philippine Sea eastwards from the PT. The maximal sampling can be seen on the profiles 21, 22 and 23 in the southern part of the cross-sectioning (as shown on Map 3.6.2) with 765, 651 and 652 taken samples, respectively. This is explained by the complex submarine landforms in the western part of the Philippine Sea (Fig. 4.12.1, the cross section profiles). Profiles 1-4 demonstrate (Fig. 4.12.2, A) significantly lower density of the observation samples not exceeding 200. This part of the section is located in the northern part of the PT and is characterized gentle landforms of the submarine geomorphology.

As can be seen from the Fig. 4.12.2 (B), the Precambrian (shield) rocks (P) are only present in profiles 5, 7, 24 and 19 and do not exist in the sample sets of other profiles. South tended data distribution is characterized for lithology types basic-ultrabasic plutonic rocks (Pb) where data are recorded for the profiles Nr. 12, 18-19, 21, 23-25, that is on the southern part of the Philippine archipelago. The metamorphic rocks (M) are noted across the profiles 23-25, 4, 6, 15-16, that is their distribution is more evenly
spread across the study area. In this dissertation, data with complex lithology of Mesozoic, Jurassic and Cretaceous origin (C) and Metamorphic rocks (M) have comparable spatial heterogeneity with observation records not overcoming 80 sample observation points. On the contrary, basic volcanic rocks of the Cenozoic volcanic formations (Vb) have significantly higher values reaching maximal values at profiles 18, 17 and 15 having values of 120, 131 and 141 registered samples, respectively.

Mixed sedimentary consolidated rocks of Cenozoic formation (Smi) had the highest detected values at profile 14 with 106 recorded points following by profile 8 (95 recorded samples). Finally, alluvial deposits of the Quaternary have the highest data density recorded at profiles 16 (58 samples), following below by profile 13 and 15 (27 and 21 samples, respectively), that is the central part of the Philippines crossing the archipelago in the shelf area (Fig. 4.12.2, B). The distribution of the sample points across the PT has four distinct groups with the increased values caused by the steep pattern in the geomorphology (Fig. 4.12.3, B): first group include profiles Nr. 5, 6, and 7 (northern part of the PT crossing further Manila Trench). Second group comprises a group of profiles Nr. 13, 14 and 15: central transect of the PT moving westwards towards the Negros Trench. Third part includes two profiles Nr. 17 and 18. Finally, fourth part includes profiles Nr. 21, 22, 23, 24, that is a southern part of the PT where the transects move further crossing Sulu Trench and Cotabato Trench on the block of Sunda tectonic plate.

<figure><img src="image_182.png" /><figcaption>**Figure 4.12.4:** Philippine Trench: correlation matrix of variables (Spearman method), Plotting: R [414]</figcaption></figure>

The opposite distribution is for the Sunda Plate (Fig. 4.12.3, A): profiles 1-4 (northern Sunda) have increased values, followed by the 2^nd^ group of profiles (8-12), then 3^rd^ group (profiles 16-17 and 19-20) and profile Nr. 25 with max value for the Sunda Plate. The sediment thickness has a relatively even distribution for all the profiles, which is described by [238] who pointed that the sedimentation in the PT is not significantly high to fill in the axis along the trench. However, the exception have profiles Nr. 2 and 3 with a distinct rise in the sedimentation volume caused by geologic conditions of the sedimentation in PT [355] (Fig. 4.12.3, D). SE part of the trench (profiles 17-21) has a distinct steeper geomorphic shape (Fig. 4.12.3, C) which is caused by complex relationship between tectonic slabs and geological settings.

Fitted model of the matrix shows (Fig. 4.12.4) variables representing correlation between the elements
of the trench system: elevation as topographic and bathymetric samplings (max/min, mean depth values and heights), sediment thickness, number of samples across tectonic plates (Sunda Plate and Philippine Plate), geomorphic elements of slope across the trench (tangent°, slope aspect), types of the geology (rock lithology). Absolute coordinates were excluded as a factor to remove the effect of the location. In total 15 variables were tested by attribute classes: geology: 7, geomorphology: 2, sedimentation: 1, tectonics: 2 and bathymetry: 3. Computed matrix visualizing impact factors is shown on Fig. 4.12.4. The effects of the variations in the slope° were tested by computed tangent° for the profiles (Fig. 4.12.5 plot 1) and repeated the analysis by ranking profiles according to the slope steepness (Fig. 4.12.5 plot 2). Grouping profiles by slopes was performed by dividing the range into 5 classes: strong slope, very strong slope, steep slope, very steep slope, extreme slope.

<figure><img src="image_183.png" /><figcaption>**Figure 4.12.5:** Philippine Trench: ranking geomorphology of the cross-section profiles. Plotting: R [414]</figcaption></figure>

The results highlight geomorphic variation and complex characteristics of the impact factors of ge-
ological origin affecting the trench. Location on various tectonic plates along with geological factors
(sediment thickness) and lithology contributes in geomorphic shaping of the actual form of the PT. Cross-
sectional transects were taken in the southern regions, through key features such as deep valley, slopes,
moraines, PSB basin, and adjacent shelf area. The datasets were combined with multi-source grids
explained in detailed 'Data and Materials'.
**4.13 Manila Trench**

The results of the geomorphological modelling and mapping of the MnT (Map 4.13.1) shown spatial variation in the different parts of the trench [406]. The characterization of the seafloor geology was performed by analyzing the trench and tectonic slabs location and areas of the submarine volcanoes. The profile transects were compared and differences in the geomorphic shape in southern and northern parts of the trench were revealed: southern part has steeper slope gradient on the oceanward part; northern part is steeper on the continental slope part. The submarine terraces are located on the northern segment of the trench at depths -2,000 m.

<figure><img src="image_184.png" /><figcaption>**Figure 4.13.1:** Manila Trench: transect of the digitized cross-section profilesh</figcaption></figure>

Depth and geomorphology of the slope vary for the range -3,500 to -4,500 m: minimal for the northern part of the trench with 526 samples (18,2%) for the depths -4,000 to -4,200 m. The histogram for the northern part has bimodal distribution with two peaks. Southern part shows 142 values for the minimal -3,500 to -3400 m [406]. The statistical comparison of the data proved northern part of the trench to be deeper. The GMT functionality shown in this paper enabled integration and interpretation of the multi-source data and approaches: automatically digitized profiles, geological mapping, 2D and 3D bathymetric modelling, statistical analysis, mathematical approximation for the trend modelling.

Two different trench segments were analyzed aimed at detecting their spatial changes for an area covering MnT and adjusting Luzon Island (northern Philippines). The south-east directed second segment (plotted as green line on the Map 4.13.1) shown steeper slopes on the oceanward side of the trench and deeper bathymetric records. On the contrary, northern segment (plotted as almost vertical yellow-colored line on Map 4.13.1) demonstrated softer geomorphic shapes yet steeper gradient from the Luzon Island side [406]. The fine-resolution data such as SRTM grid was used to map bathymetry and topography of the study area and 1-min resolution from the gravity data (Jason-1, CryoSat).

The statistical results (Fig. 4.13.2) show differences in the depth ranges of the MnT along the Luzon island. The depth and geomorphology of the slope vary significantly for the depths range -3,500 to -4,500: minimal values are notable for the northern part of the trench where 526 observation points (18,2%) are
recorded for the depths -4,000 to -4,200 m, as shown on Fig. 4.13.2 A [406].

<figure><img src="image_185.png" /><figcaption>Map 4.13.1: Manila Trench: two segments of the digitized cross-section profiles</figcaption></figure>

The histogram for the northern part has a clear bimodal distribution with two peaks. The second peak shows areas crossing the Luzon island. On the contrary, S part demonstrates 142 values for the minimal bathymetry with values -3,500 to -3400 m; the second peak also shows areas of the Luzon Island. The comparison of the statistical data analysis clearly shows that the N part of the trench is deeper.

The southern part of the trench has steeper slope gradient from the oceanward part and on the contrary, the northern part is steeper in the continental slope part (Fig. 4.13.1). Submarine terraces can be noted on the N segment of the trench (Fig. 4.13.1) at depths -2,000 m. Generally, model of the N part shows more steep degree of the oceanward slope (pairwise comparing on Fig. 4.13.1). However, a gradually slanting terrace on the S part of the trench can be seen on the fragment 50-100 of the cross-section with depths from -1,100 to -1,300 m [406]. Hence, the comparison between two segments of the trench located on the N and SE part of the trench show that N segment has shallower values of depths comparing to the S, but more steep slope on the Luzon Island side.

As for the geophysical modelling, based on the patterns of the free-air gravity map of the MnT (Fig. 2.12.3), high density materials are distributed continuously from the N the S Taiwan vertically southwards to the Luzon Island, the Philippines from -50 mGal to -200 mGal. The majority of the values in the South China Sea areas lie between the 10 to 30 mGal, while values >60 mGal can be noted in the island areas and southern Philippines [404]. The results of the geomorphological modelling and mapping of the MnT highlighted underlying tectonics conditions where the collision of two tectonic plates causes instability: repetitive earthquakes, submarine volcanism and seismicity.
<figure><img src="image_186.png" /><figcaption>**Figure 4.13.2:** Manila Trench: histograms of the bathymetry</figcaption></figure>

Strong topographical isolation and complex mix of water masses distinct trenches from other oceanic regions. Difficulties to study deep-sea life and habitats zone (6000-11,000 m) that presents the last great frontier in marine science are caused by remote location. Studies on marine biology are currently limited to the bathyal (200-3000 m) and abyssal depths (3000-6000 m), [269]. Specifics of the benthic biology consists in the geomorphic shape of the trench: despite representing 1-2% of the global benthic area, hadal zone constitute the deepest 45% area of the vertical depth gradient [287].

Consequently, the communities of the MnT represent spatially isolated environments, highly distinct from the shallower areas. It present a cluster of deep habitats that include features of the continental slopes, abyssal plains and unique properties of the hadal geomorphology affecting biological aspects. Hadal fauna survive on the fringes of two extremes caused by the hydrostatic pressure and remoteness from the surface-derived food supply [286]. The interconnectivity of the factors affecting hadal ecosystems is described by [751], who reviewed various aspects of the hadal biogeochemistry: the effect of food supply on hadal ecosystems, hadal carbon cycle in the sub-seafloor under high hydrostatic pressure and pollution in the hadal trenches.
**4.14 Ryukyu Trench**

The statistical results (Fig. 4.14.2) on the modelling RT show certain differences in the depth ranges
of the RT along the Ryukyu island arc. The depth and geometry of the slope vary considerably: maximal
values of -5,500 to -5,700 m are notable for the northern part of the trench where 334 observation points
are recorded, as shown on Fig. 4.14.2 (A) [392]. With a minor difference, southern part demonstrates
comparable values (329 samples) for the same depth range.

<figure><img src="image_187.png" /><figcaption>Map 4.14.1: Ryukyu trench: cross-section profiles of the southern and northern segments</figcaption></figure>

However, more significant differences can be seen for the depths >6,000 m. Thus, the southern part
of the trench (shown on Fig. 4.14.2 (B) has only few values (9 samples) with depths >6,800m while the northern part shows 41, 59 and 23 records, with a maximal values reaching -7,460 m. Moderate to shallow depths with ranges from -3,000 to -5,000 m are comparable for both parts of the trench with values not exceeding 68 samples for the northern part and 42 samples for the southern, respectively [392].

The forearc basin deeper than 2,000 m and shallower than 1,000 m BSL shows more samples data in the northern part of the trench than in the south. In contrast, a wide terrace with depths shallower than 25 m is generally larger (161 samples) in the southern part of the trench [392]. To conclude, the comparison between the two spatially different segments of the trench show that northern part of the trench has deeper depths comparing to the southern. This can also be noted on Fig. 2.13.1 where the deepest values of the central part are seen in the deep area extending seaward offshore from the Miyako Fault (Fig. 2.13.2).

<figure><img src="image_188.png" /><figcaption>**Figure 4.14.1:** Ryukyu Trench: models of the slope gradient trends for the profile curves</figcaption></figure>

As can be noticed on the comparison of Fig. 4.14.1 A and Fig. 4.14.1 B, the median stacked profile on the southern part (Fig. 4.14.1 A) demonstrates slightly more gentle slope shape with submarine terraces, while there are no clear on northern part of the forearc slope (Fig. 4.14.1 B). At the same time the depth of the forearc slope increases in the northern segment of the trench (Fig. 4.14.1 B). These changes of the slope geomorphology are also highlighted by the contour of the increased marine free-air gravity anomaly, as a triangle shaped island located at 126°E 24°N (Fig. 2.13.4) [392].

Geometric approximation of the trend curves (Fig. 4.14.1) shows similarity between the segments while applying coarse linear model (Fig. 4.14.1: E and J; Fig. 4.14.1: D and I) [392]. Specifically, it shows modelling trend curves for general geomorphological transects and slope gradients using various mathematical approximations. However, the differences in the geomorphic variations become more notable at the polynomial functions (Fig. 4.14.1: C and H; Fig. 4.14.1: B and G) [392].
<figure><img src="image_189.png" /><figcaption>**Figure 4.14.2:** Ryukyu Trench: histograms of frequency of the depth distribution</figcaption></figure>

The geoid model shows geoidal undulation in the south-western part of the area east off Taiwan Island (Fig. 2.13.3). Generally, the major part of the area has geoid values range of 25 to 35 mGal. The increased values (>35 to 85 mGal) can be seen on the south-eastern part in the Philippine Sea area, while lower values (15-25 mGal) bordering continental shelf stretch along the East China Sea and the lowest (<15 m Gal) mainly occupy land areas.

Various studies have been undertaking to analyze the complexity of the system of deep ocean trenches and reveal possible impact factors (recent studies [624], [522], [761], [175], [269], [101], [69], [225]. Current analysis of the trench geomorphology and variability of the steepness indicate the importance of geological factors. Complex tectonic and geomorphic features of the trench was reviewed using a set of cross-section profiles with local geologic settings located in four tectonic plates. The results demonstrated dependences between the geomorphic factors and geology (slope angles and sediment thickness, respectively) on the profile morphology.
**4.15 Palau Trench**

The results of the data analysis demonstrated spatial geomorphic unevenness of the PT. The information and conceptual framework for the sediment thickness of the World Ocean published by [134] was used in this research. Ranking geologic data has a step in geomorphic analysis where the levels of the slope degree are determined by the geospatial impact. Therefore, geomorphic variations typically necessitate an additional sub-task of the geological analysis of the study area.

<figure><img src="image_190.png" /><figcaption>**Figure 4.15.1:** Yap and Palau trenches: geomorphological modelling of the profiles [412]</figcaption></figure>

Data distribution for the PT has remarkable pattern: the amount of depths in range of -5000 m to -4000 m is large (737 samples at PT). The same situation can be seen for the depth range at -5000 m to -6000 m: PT has 152 samples. In general, PT has more gentle slope both landwards and oceanwards. Topographic profiles of the PT axis has U-shape. PT has the majority of values higher and/or below -6000 m. PT has more gentle geomorphic slope land- and oceanwards. Contours of the free-air gravity anomalies (Fig. 2.14.2) in general reflect bathymetric isolines (Fig. 2.15.1). Comparing to the neighboring Mariana Trench, gravity field of the Yap and PT are narrower (purple colored strip on the map).

Free-air gravity values reach positive above 100 to 500 mGal (red color) mostly over the area of Philippine Islands and small islands of the archipelago. Positive 70 to 100 mGal are notable over the shelf areas (orange color on the map). Gravity over the basin of the Philippine Sea generally fluctuates between -20 to 0 mGal range (light blue) to 020 mGal (cyan). The characteristics of the gravity field mirrors correlation between the gravity anomaly and geoid undulation as determined by [229] for gravity anomalies from a grid of geoid undulations. The geoid model (Fig. 2.15.2) visualizes subdued isolines in the Yap and PT.
<figure><img src="image_191.png" /><figcaption>**Figure 4.15.2:** Yap and Palau trenches: statistical histograms on bathymetry [412]</figcaption></figure>

Trench formation is a very complex process that is known to be influenced by a variety of geological and tectonic factors. These may not adequately explain the process of trench deep formation, its further development and what is the impact in a variety of factors affecting hadal structure of the ocean trenches. Studies of the oceanic trenches based on the profiles cross-section have been undertaken in the past, e.g. [562], yet based on technical methods of that time. Until now, little attempts has been available on application of shell scripting towards modelling ocean trenches in marine geology, and this research contributed to this question.
**4.16 Yap Trench**

According to the statistical analysis (Fig. 4.15.2), the highest values for **YT** are from -4000 m to -4200 m (278 samples) with 19% and 17% of the total data pool, respectively. For the depths over -5000 m, the model of two trenches significantly changes: decreases for the **PT** more clearly.

<figure><img src="image_192.png" /><figcaption>**Figure 4.16.1:** Yap trench: trend model of the slope gradient direction [412]</figcaption></figure>

Data distribution for the **YT** (605 samples): depth range at -5000 m to -6000 m: **YT** has in total only 53 samples. In general, **YT** has steeper slopes and generally deeper sample observation records. **YT** has more deep values within range of -7000 m – -6000 m: 68 samples, while **PT** has only 34
samples for the same depths diapason. That means that YT has dominating depths at deeper values. In general, YT has steeper slopes and generally deeper sample observation records. Bare bottom of YT may result from the thin sediment cover of the axis which proves correlation between the factors (geology, geomorphology, geophysics, tectonics, sedimentation) affecting trench structure and dynamics [761]; [433]; [316]. Topographic profiles of the YT axis have very sharp V-shape. V-shaped trench generally indicates bare bottom of the YT with very thin sediment cover of the axis.

<figure><img src="image_193.png" /><figcaption>Map 4.16.1: Yap and Palau trenches: digitized cross-section profiles [412]</figcaption></figure>

YT has steeper slopes and deeper records. Topographic profiles of the axis of the YT have sharp V-shape. YT reaches -7000 m in its median curve of the profiles, while the PT only -6200 m, that is the PT is shallower. Second, YT has distinctly more abrupt slope and steep gradient of the profiles oceanward (Fig. 4.15.1, B) comparing to the PT (Fig. 4.15.1, A). [332] earlier pointed that extensive slope failure is occurring on the landward slope of the PT from which great amount of mass is wasted to the trench axis. Hence, if surface sediments or rocks are collapsed into the trench axis, the V-shaped bottom will be gradually buried to form a flat basin, unless subduction or the tectonic erosion proceeds. Submarine sediment avalanches occur easily and frequently, when dipping angles of the lower landward slope partly exceed slope failure limit.
<figure><img src="image_194.png" /><figcaption>Map 4.16.2: Yap and Palau trenches: 3D mesh modelling [412]</figcaption></figure>

With this in view, the comparison of the graduate slopes is important (example for YT: Fig. 4.16.1). This can contribute to the explanation of the differences between two trenches in the geomorphic shape: as we can notice, inner slopes on the YT are more steep and abrupt (Fig. 4.15.1, B) while the PT (Fig. 4.15.1, A) has more gentle slope with sediment accumulation on the inner slope side. Fig. 4.15.1 shows modelled profiles of the bathymetry of the PT (A) and YT (B) trenches. The average dip of the landward slope is recognized as about 29°for the YT westwards 41°eastwards and while for the PT it is 17°westwards and 18°eastwards, respectively. The topography of the PT is more similar to an upside-down mound. Generally, YT has abrupt shape with no marine terraces identified.
**4.17 Mariana Trench**

Seafloor geomorphology of the MT and of the PSB were examined using high-resolution GEBCO and ETOPO1 topographic and gravity data grids in NetCDF format, geologic vector layers and modeled profiles.

<figure><img src="image_195.png" /><figcaption>**Map 4.17.1:** Cross-section transect profiles of the Mariana Trench</figcaption></figure>

Profiles are visualized on Fig. 4.17.1. The statistical histograms for transects bordering the PSB are visualized on Fig. 4.17.1. The shape of the histogram varies by two trenches reflecting their geomorphol-
ogy. The statistics on topography and interpolation approaches are shown on Fig. 4.17.1. Histograms for the MT demonstrated bimodal peak for depths -5,000–-3,000 m with samples <10%. In this interval, the 1st peak is covering -4,500 to -5,000 m BSL and the 2nd one is for the -3,800 to -3,000 m. Gradual decrease in data samples >5,000 m indicate a uniform way of data distribution for the depths range of -5,000–-6,500 frequency 4–2%, 285 samples. Depths at -6,200–-10,800 m have a frequency <2%. Shallower areas with depths >3000 m and <1,500 m BSL have frequency <4%, 400 samples.

<figure><img src="image_196.png" /><figcaption>**Figure 4.17.1:** Histograms on depths frequency: Mariana and Philippine trenches</figcaption></figure>

Gradient slopes of the trenches have markedly symmetric accurate 'V'-shape form in their deepest parts. Automatic digitizing of the perpendicular transects cross-sectioning MT and PT, demonstrated that MT has steeper gradient slope on the northern (Fig. 4.17.1, below) flank on the archipelago's side (Fig. 4.17.1). Slope steepness on the left flank has a 33,10° at 0-15 km from the center from the vertical plummet. A segment on 15-25 km has a steepness of 45°, segment of 25-40 km – 30,75°, a segment of 40-50 km – 28°. On the right flank of the trench its gradient steepness varies as follows. The segment at distance 0-28 km has a steepness of 40,5° from the vertical plummet, 28-37 km from the start point has a 43,33° of steepness. It then becomes gradually becomes gentles to 30,43° until 50th km. The segment at 50-90 km consists in the arc-shape gradual increase of the elevation which stabilizes after 100 km <-4000 BSL (Fig. 4.17.1).

The relief surrounding left flank slope of the trench presents uneven submarine terrain comparing to
its right flank with more complex submarine relief and to the PT which presents a more accurate and steep in its shape form. Results on geomorphic modelling of the MT profiles revealed that the major depth samples of the trench are between -3000 – -5000 m (Fig. 4.17.2). Maximal depths reach up to -10,000 in the current dataset: profiles 20, 21, 22 crossing mostly PSP (Fig. 4.17.2). Decrease of depth is in profiles 23, 24, 25, crossing Caroline Plate. Profiles Nr. 23 and 24 have the deepest depth data range, [427]. Conversely, bathymetric profiles from 1 to 16 have gradual decrease in depths, which can be noted in outliers sample location. A slight increase in depths of the profiles Nr. 4-8 was also observed of the Fig. 4.17.2.

<figure><img src="image_197.png" /><figcaption>**Figure 4.17.2:** Mariana Trench: graph of the 25 bathymetric profiles, QGIS [415]</figcaption></figure>

Changes in bathymetry may be explained by changed geological settings. Nevertheless, sediment thickness changes notably both within the trench by profiles and between tectonic plates. Since tectonic settings are not identical, the comparative analysis of data variations across tectonic plates was performed. The majority of samples of the profiles crosses Pacific Plate and Philippine Sea Plate, following my Mariana Plate, while Caroline Plate only covering a few points. Density distribution enables to compare overlapping and maximal aptitude of the density curves for the profiles and depth distribution by tectonic plates. Major trend of the trench gradient on the Pacific Plate has downward general line. The Philippine Plate, has a minimal peak by profiles #14-21, then moving upwards. The highest values are for the Caroline Plate. Consequently, Mariana Plate has the highest density of depth distribution values, followed by the Philippine plate, then Pacific and Caroline, respectively.

From two multiple panel by groups (Fig. 3.8.7 and 3.8.8) one can compare slope angles and depth distribution by tectonic plates, and patterns of these categories by plates. Summaries of variations of the local polynomial regression of the bathymetric depths of the measured samples are presented on Fig. 4 in [415]. Upon a close examination of these summaries it can be observed that, with a mean depth value ranging from 5000 to of 3000 on the profiles, the widths of the confidence intervals are expanding more rapidly by the profiles 12 to 15 and 19 to 22 thus indicating on the large amplitude of the depths variations in this part of the MT. The profile depth is being affected by local geographic settings caused by the location on the tectonic plates with varying geological conditions. On the other hand, profiles 22 to 25 crossing Caroline Plate [415] suggests that absolute depth in this region change to become shallower than profiles crossing Philippine and Pacific Plates. This phenomenon has adverse implications for deep inter-connections between these factors.

Marine free-air gravity anomaly in the PSP area shows (Fig. 2.11.4) higher values (> 80 mGal) of the gravity fields structure at the volcanic areas and Philippine archipelago. Geometric differences between trench shapes are explained by the context of the historic development and actual geomorphic
and sedimentary processes affected their formation and structure. Current study presented comparative
submarine geomorphic mapping and complex analysis of the geophysical and geological settings in the
Philippine Sea basin area.

**Bathymetric variations** Data distribution are presented on Fig. 4.17.3. By profiles they ranged from -10.600m (maximal depth in this data set) to -3150m (the shallowest point in the data set). The maximum depths in the profiles were found in profiles Nr. 20, 21, 19, 14 (Fig. 3, right, sorted bathymetry), and the minimal depths (i.e. the shallowest) consist the first group, that is profiles 24, 23, 22 and 10 (Fig. 3, right, sorted bathymetry). The deepest part of the trench is located in the SW and central segments where the trench crosses Philippine and Pacific Plates. Moderate depths correlate with Mariana Plate and the majority of the Philippine Sea Plate. The depths ranges detected across the profiles of present study are also higher in southern part of the trench than in northern segment (Fig. 4.17.3). Central part of the MT has variations in depth: the increasing depths towards S part were the depths are higher than in the N segments. By comparing the profiles Nr. 3, 4, 9, 12 from the Pandas array (Fig. 4.17.3, colored green) with profiles Nr. 18, 16, 15, 13 (orange on Fig. 4.17.3), the depths increase reaching its maximal depths in the following group (Profiles Nr. 20, 21, 19, 14, group blue, Fig. 4.17.3, right).

<figure><img src="image_198.png" /><figcaption>**Figure 4.17.3:** Mariana Trench. Data distribution analysis: maximal depths by profiles, Python [415]</figcaption></figure>

This step identified variations in the steepness in various segments of the trench. Slope steepness and depths were analyzed by libraries Matplotlib, Seaborn and Bokeh. The steepest part of the trench is located in NW, C and SW segments (profiles Nr. 3, 7, 9, 23, 24) where trench crosses Philippine and Pacific plates. This segment has the largest local depths and sharp abrupt changes. The highest depth gradient is at profile Nr. 21 at the distance of 10.600 m BSL. Middle moderate slope° (Profiles Nr. 1, 11, 4, 5, 10) correlate with areas where trench crosses Mariana Plate. Fig. 4.17.3 shows maximal depths by profiles, each profile has only one dot point on the plot, Fig. 4.17.2 illustrates the cross-section profiles. Profiles 19, 20, 21 and 22 had even geomorphic shape that decreased abruptly after sample 200. The unevenness of the profiles is explained by changed geologic conditions and tectonic plate (Philippine Sea to the Pacific, Mariana to the Philippine Plate). The groups of profiles 23, 24 and 25, (Fig. 9F, [415]), have similar geomorphic shapes: the first halve of the profiles is relatively even while the S part is particular for the increased depths. For better visualization, profiles are colored individually with the shared X axes (Fig. 9, [415]).

Geomorphic segments of the Mariana Trench have relatively balanced elevations from -7.600 m (profile Nr. 11), -7.750 m (profile Nr. 4), -7.800 m (profile Nr. 5), -7.750 m (profile Nr. 10), to -6.200 m (profile Nr. 1). Depths ranges are shown on Fig. 10 [415] (jitter plot). The profiles with the least slope degree (21, 22, 18, 20) are located in the S segments where trench crosses Caroline Plate. The depth
values >-9600 m BSL prevails on the bathymetric model for the deepest depth range, representing depths of the very steep slopes of the trench. Sediment sources are connected with trench tectonics where values of the sediment thickness are 140-120 decreasing to 50 mm. Sedimentation differs significantly by plates, due to the complex of the sediment processes and mechanisms. Among other are turbidity caused by gravity sliding, biochemical deposits, closeness of seismic zones, igneous volcanic areas. Besides, due to the funnelling effect, trench sediment deposits speed is faster and thickness is greater than abyssal basins.

<figure><img src="image_199.png" /><figcaption>**Figure 4.17.4:** Mariana Trench. Correlation, k-means Clustering and Data Grouping, R [422]</figcaption></figure>

**Correlation and k-means clustering** k-means algorithm shows groups across all profiles, with the number of groups represented by variable. Several clusters were tested (2 to 7 (Fig. 4.17.5), optical number is 5: cluster circle contained the number of samples and overlapping was minimal (Fig 4.17.5). Correlation matrix is presented on Fig. 16 ([422]) showing crossing in the combination of factors. Comparison of the bi-factor shows pairwise correlation (Fig. 17, [422]). Comparative analysis (Fig. 4.17.5) enabled to observe a marked influence on variables as bi-factors. In response to the decreasing sediment thickness the slope° goes in parallel; location of the volcanic igneous areas cause a cyclic repetition of the curve for the slope°. Igneous volcanic areas have correlation with the slope° and aspect°. Variable factors have distinct effect on the trench geomorphology: slope angle, tectonic plates, sediment thickness, aspect° and volcanic igneous areas, geographic location.

**Data partition and ranking** Analysis of the angle steepness of the cross-section profiles along the trench revealed (Fig. 3.8.13) that cross-section profiles form cluster groups with similar geomorphic
properties divided into 5 groups. 1st group: profiles 21, 22, 18 and 20, 'strong slope' tg°, ca. 0.05. 2nd group: profiles: 15, 19, 16, 17, 14, 2, class 'very strong slope', tg°angle of 0.057 to 0.058. 3rd group, 'extreme slope', profiles: 1, 11, 4, 5, 10 and 13, the average slope tg°, 0.060-0.070. 4th group, 'steep slope', profiles: 25, 12, 6, 8, 3, slope tg°, 0.070-0.075. 5th group, highest steepness, profiles: 9, 7, 23, 24, with average slope tg° up to 0.079. Although steepness is related to the slab subduction in this particular area, the nature of the slope expansion may be associated with other factors, e.g. topography, volcanism and location. The data grouping has been created using libraries {tidverse} and {ggsignif} for reordering and normalization of the steepness angle (Fig. 3.8.13). Additional Chinese fonts (Fig. 4.17.5) were activated through libraries {extrafont} and {extrafontdb}

<figure><img src="image_200.png" /><figcaption>**Figure 4.17.5:** Results of the *k*-means clustering of the Mariana Trench with different *k*-values, R [422]</figcaption></figure>

5 classes were grouped: strong slope, very strong slope, extreme slope, steep slope, very steep slope. They show trench steepness according to the results of the cluster analysis. General trend has a scope of 0.03 (ca. 0.05-0.08) of the profiles steepness. Re-classification of the slope° across the trench and statistical normalization have crucial role in the analysis of the slope. Profiles nr. 23 and 24 have the highest slope steepness, located on the Philippine Plate. 2 large classes of the normalized degree angles were observed: 1st below average° (profiles nr. 1, 2, 11, 14-19, 21-22) and 2nd above average° (all the rest). Steepness was shaped by the components of the igneous volcanic areas: profiles # 20, 22, 23, 24 with notable amount of igneous volcanic observation points (>180) correlate with steepness°. 5 classes of the slope steepness are selected due to the changed geomorphic properties. Circular bar plots enabled
to visualize depth distribution and slope angle values using R scripts, [422].

**Kernel density curves** Mariana Plate has the highest KDE of depth distribution values, followed by the Philippine Plate, then Pacific and Caroline, respectively. KDE shown in a combined plot enabling to compare the overlapping and maximal aptitude of the density curves for the profiles and depth distribution by tectonic plates. Major trend of the trench° located on the Pacific Plate has downward general line trend. Philippine Plate has a minimal peak by profiles #14-21 then moving upwards. The highest value for Caroline Plate has profile #23, while maximal level for the Mariana Plate has profile #7. Philippine Plate, has a minimal peak by profiles #14-21 then moving upwards. The highest value for the Caroline Plate has profile #23, while maximal level for Mariana Plate has profile #7.

Composition charts (Fig. 3.8.7), show comparison of the slope° by profiles. Basic concepts include interrelation of various factors (geological structure, bathymetric patterns, tectonic plates location and subduction, spreading, transform fault, depths) affecting trench geomorphology [403]. Comparison of the properties of the oceanic crust and ophiolite sections, sediment thickness and depth across profiles enabled to make a better understanding of the ties among geospatial factors influencing trench geomorphology. Tectonic plates, geometry (tg°), sediment thickness, geology (volcanic areas, earthquakes), tectonics play crucial role in the geomorphology of the trench. Other factors include slope steepness°, angle aspect, bathymetry (depth at basement), means, median and minimal values, geographic location. A hierarchical treemap of impact factors generated using R code: A.6.39 is presented by code [433].

Mariana Trench is important integral feature of active continental margins in west Pacific Ocean. Its mainland and oceanward slopes have complicated steps of various shapes and sizes, caused by active tectonic and sedimentation. The steepness of the trench slopes averages in 4-5°. Tectonic plates have more steep° with average slopes of 10 to 15°, but their individual parts can be limited to steeper slopes as subjects to the gravitational flow system of the submarine canyons and valleys. Complex distribution of various geologic material on the adjacent abyssal plains contributes to the formation of the geomorphic features of the ocean bottom in the trench. Slope degree and amplitude contribute to sediment thickness, aspect° has lesser effect.

**Spatial variations in trench morphology** Quantified similarities in data distribution were assessed by correlation matrices and cluster analysis. Large gradients in variability occur within and among N and S part of the trench and in the E curve of its shape directed towards transform lines near Magellan Seamounts and Marcus-Wake seamount. Geomorphology of the trench is dominated by following factors: closeness of the submarine volcanic areas, location of Philippine and Mariana tectonic plates, sediment thickness. Middle part of the trench (profiles: 14 up to 17), has very strong slopes, roughly equal proportions of sediment thickness. Variations in sediment thickness by profiles were defined as indirectly impacted from the changed slope° that gives conditions for sediment accumulations. If a certain segment of the trench is located near the active volcanic area, sedimentation rates increase. Profiles Nr. 1, 24, 25, 23 are notable for high sediment thickness on the Philippine Plate, profiles Nr. 2, 4, 6, 8 have the highest values for the Mariana Trench, profiles Nr. 3, 5, 8, 6 have the highest values of the sediment thickness for the Pacific Plate. Profiles located on the Caroline plate have the least values with only few samples. Five unique regions were compared, and impact factors affecting morphology analyzed by means of the statistical analysis.
**4.18 Izu-Bonin Trench**

Cross-sectioned profiles are visualized as two segments of the IBT (Fig. 3.3.14). The results of the modelling demonstrated that southern segment of the trench is deeper and has more accurate V-form with a steeper gradient slope on both slopes (Fig. 4.18.1, B), [405]. Northern part of the trench (Fig. 4.18.1, A) has asymmetric shape of the slope with notable submarine terrace on the western part and more straight shape on the oceanward side (Fig. 4.18.1, A), [405].

<figure><img src="image_201.png" /><figcaption>Map 4.18.1: Izu-Bonin Trench: modeled graph of the cross-section profiles in two segments</figcaption></figure>

Furthermore, southern segment of the IBT has Bonin Ridge distinguishable on the graph. Comparison of the oceanward flank of the both segments of the IBT show similar geomorphic shape but seamounts on the northern part (Fig. 4.18.1). The 3D model of the Izu-Bonin shows (Fig. 4.18.2) seamounts (exaggerated for the better visual effects) on the eastern part of the model. IBT is colored as deep-purple color with a general profile view on the grey draping border.

Statistical analysis of the two segments shows (Fig. 4.18.3) following results. For the N segment (Fig. 4.18.3, A) that for the most illustrative range of depths (that is from -5,000 to -6,000m), the N segments shows in total 1,037 recorded samples (304, 407, 248 and 78), while S segments shows (Fig. 4.18.3, B) shows in total 931 samples (523, 304, 77 and 27). Furthermore, the histogram for the S segment has clear bimodal distribution with two peaks: 523 samples taking 20% shows the most frequent depth values for the S segment that lie in the range of -5,800 to -6,000m. The 2^nd^ peak takes almost 10% of the data pool correlating with the Bonin Ridge location, [405].

These are the data on the range from -4,200 to -3,800 m. For the N segment, the most repetitive samples (407 values, 13,5%) are taken by the depths from -5,600 to -5,800 m, followed by depths in range -5,800 to -6,000 m (304 samples with 10%). In the N segment, the data distribution has more samples located on the shallow areas (146 recorded observations) pointing at the Izu-Bonin Arc, while
for the S segment there are no data recorded in this bathymetric range, [405]. That means that S segment
has more V-shape trench cross-section profile which is notable on Fig. 4.18.1.

Izu-Bonin Trench: 3D composite mesh model

Overlay of the topographic 3D on top of the 2D geoid contour plot

Data: ETOPO 5 arc min grid, World Geoid Image 9.2 5 min grid

<figure><img src="image_202.png" /><figcaption>**Map 4.18.2:** Izu-Bonin Trench: 3D mesh model map</figcaption></figure>

Before now, oceanic trenches were thought to have strongly restricted life activities, which is caused
by the extreme conditions that delimit vertical constrains in distinct bathymetric strata and define distribu-
tion of organisms. High hydrostatic pressure, temperature, salinity, oxygen, scarcity of food, submarine
volcanism, tectonic plate subduction, lack of UV are major factors limiting presence, functioning and
location of the species in the trenches. Continued studies of the biology discovered new phenomena: the
biomass and abundance of fauna increase with depth in some trenches, although their species richness
decrease downward [122]. Vast microbial and faunal community structure, abundance, biodiversity, level
of endemism and adaptation towards extreme condition in hadal species [504]; [505].
<figure><img src="image_203.png" /><figcaption>**Map 4.18.3:** Izu-Bonin Trench: statistical histograms of the depth frequency</figcaption></figure>

Life exists in the ocean's hadal zones at extreme depths of the Pacific Ocean, [223], [227]; [238]; [284]; [336]; [694]; [736]. The existence of the organisms at such depths strongly depends on the turbulence of the streams and motion of the submarine currents. Specific processes characteristic for the deep hadal communities were reported by [353] with a case study of *Dermacoccus abyssi* isolated from the deepest areas of the oceans for dye decolorization. Species are strongly influenced by oceanographic properties and geomorphic structure of the trench [636]. High sedimentation rates and biomass, intense microbial activity and chemosynthetic communities play crucial role in biogeochemical cycles of the trenches. Therefore, deep-sea biosphere deserves a special attention since this is a specific area of trenches where life conditions are created [510]; [361]; [303].

Deep-sea ecosystems arises interest due to their potential to serve a various range of the industrial needs [669]. Pressure within the trench increases by 1 MPa per 100 m, reaching 100 MPa in the deepest places [754]. Discovered new species of the deep-sea are *Spongicoloides* and *Corbitella* [336]. Life at such depths requires supply of nutrients in form of chemical species, transported by the bottom water through the deep currents ventilating trenches and upbringing nutrients. It was discovered [712] that microbe communities play certain role in global carbon sequestration and carbon cycle of the trench.
## 4.19 Japan Trench

Since fundamental bathymetric variable is the depth, or elevation records BSL geomorphic analysis of the GEBCO datasets in the form of DEMs, was performed for computation, comparison and descriptive statistical analysis of the two segments of the JT [396]. Hence, the comparative analysis of the two parts of the a series of cross-section profiles (Fig. 4.19.1) revealed more steep slope on the northern part of the trench on the continental slope part beneath the frontal wedge of the North American tectonic plate.

<figure><img src="image_204.png" /><figcaption>**Figure 4.19.1:** Japan Trench: modeling geomorphic cross-section profiles</figcaption></figure>

Southern part of the JT shows more gentle slope on the Honshu Island. The location of the swath profiles traversing the two segment of the JT South-West to North-East direction is shown in Fig. 4.19.1. 12 profiles were drawn for the southern segment and 14 profiles for the northern one. Highly rugged geomorphological structure of the median profiles, and altitude increase in the S-N direction can be seen on the northern part of the JT (Fig. 4.19.1). The results were interpreted based on the hypothesis that geological settings and seismicity of the area impact the trench roughness and general geomorphic shape when studying bathymetric patterns and variations by the cross-sectioning profiles of JT, which is shown on the geological map (Fig. 2.18.2) with high level of the seismicity, repetitive earthquakes and volcanism along the path of the trench. Consequently, W part of the trench (Honshu Island slope) has steeper slopes comparing to the oceanward slopes for both segments, which can also be seen on the 3D model shown on Fig. 2.18.5 [396].

The statistical analysis of the most frequent bathymetric value for the northern part of the JT (Fig. 4.19.2, A) shows a range of -5300- to -5500m (683 samples recorded). As for the southern part, the most frequent depths lie within interval of -5500 to -5800 meter (531 samples detected, Fig. 4.19.2, B). Furthermore, comparing the deepest values of the bathymetry, one can notice the southern part to be slightly deeper: the deepest records up to -8000 m (8 samples are recorded) [396].
<figure><img src="image_205.png" /><figcaption>Map 4.19.1: Japan Trench: map of the cross-sectional profiles along two segments</figcaption></figure>

In total, 139 samples (8+29+42+59) are located on the depths from -7000 to -8000 m in the S part of the trench (Fig. 4.19.2 B, left part of the graph). In the N part of the trench, the same depth range has 124 records (12, 59 and 53 in the deepest part of the profile, Fig. 4.19.2 A, left part of the graph). Furthermore, the values located on the depths from -4000 to -5000 meter are more frequent for the S part of the trench: 134 records in total (35+33+37+29 samples, Fig. 4.19.2, B), while N part of the trench do not overstep 100: 91 records (21+23+21+26 records, Fig. 4.19.2, A). To sum up, the S part is shallower than N part of the trench. It shows more deep values and the most frequent value is deeper that by the N segment (Fig. 4.19.2) [396].
<figure><img src="image_206.png" /><figcaption>**Figure 4.19.2:** Japan Trench: statistical analysis of the data distribution (histograms)</figcaption></figure>

Geophysical investigation of the presented modeling of geoid (a mean sea level model) and free-air gravity anomalies reflect the density variations of the Earth in the area across the JT and surroundings (Fig. 2.18.3 and 2.18.4). The recorded area of the JT has geoid values of 0-20 mGal (Fig. 2.18.3), [396] as can be clearly seen as a light green color with dense isolines. The highest values on the geoid elevations is recorded by the Honshu Island (above 40 mGal, shown as red color on the map). The rest of the area is covered by the values in the range of 20-40 mGal [396], which shows moderate geoid undulations.

Gravity map (Fig. 2.18.4) shows density variations of the Earths interior in the study area. Free-air model shows gravity reduction from the topographic surface to the geoid in the study area. Map of free-air gravity anomalies (Fig. 2.18.4) is defined with respect to the elevation showing values below -30 mGal in the JT area (visualized by deep purple color on the map). The marine areas of the Sea of Japan and the Pacific Ocean have values of up to 40 mGal of elevations. Certain areas on the Japan Islands (Honshu and Hokkaido) reach above 100 mGal (red color on the map) [396] showing high values in concern to gravity.