Understanding
patterns of the correlation between the geomorphology and geology of
the seafloor of the hadal trenches is important for the proper ocean
modelling. Current paper focuses on the west Pacific Ocean region
with a special case of Mariana Trench, the deepest hadal trench on
the planet. Methodology of the research include combination of
Generic Mapping Tools (GMT) and Quantum GIS based mapping of the
geographic location, bathymetry, geodesy, sediment thickness,
geomorphic shape, tectonic and geologic structure of the Mariana
Trench area, and statistical analysis by means of Python. A GMT was
selected for GIS visualization due to its powerful functionality and
effective cartographic solutions. An object-oriented high-level
programming language, Python was chosen for the data analysis and
scientific plotting. The statistical analysis includes following
steps: 1) Data distribution by the box plots; 2) Data
sorting and grouping by stem plots; 3) Correlation analysis by 3D
comparative plots referred to four tectonic plates; 4) Principal
Component Analysis; 5) Analysis of Variance. The statistical analysis
of the data set was performed in Matplotlib library and its
dependencies: NumPy, SciPy and Pandas. A combination of the powerful
methods by GMT with data analysis supported by Python programming
language is an important method in geosciences aimed to increase the
effectiveness of the data analysis by cartographic mapping,
statistical computations and graph plotting. This paper illustrated
usage of GMT, QGIS and Python for combined data analysis scheme. The
results demonstrated correlation between the sediment thickness,
slope steepness, depths and location of the bathymetric profiles
crossing adjacent tectonic plates: Philippine, Pacific, Caroline and
Mariana.
China Scholarship Council (CSC)
2016SOA002
This research was funded by the China Scholarship Council (CSC) State Oceanic Administration (SOA) Marine Scholarship of China, Grant Nr. 2016SOA002, Beijing, P.R.C.
2016SOA002
Primary Language | English |
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Subjects | Engineering |
Journal Section | Research Articles |
Authors | |
Project Number | 2016SOA002 |
Publication Date | December 8, 2019 |
Published in Issue | Year 2019 Volume: 6 Issue: 3 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.