Generic Mapping Tools and Matplotlib Package of Python for Geospatial Data Analysis in Marine Geology

Abstract

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.

Keywords

• GMT,Mapping,Python,Statistics,Mariana Trench,Marine Geology

Supporting Institution

China Scholarship Council (CSC)

Project Number

2016SOA002

Thanks

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.

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