Calculating Slope Gradient Variations in the Submarine Landforms by R and Python Statistical Libraries

This research focuses on the analysis of the submarine geomorphology in the Mariana Trench located in west Pacific Ocean. The research question is to identify variations in the geomorphic form and bathymetry in different segments of the trench. Technically, the paper applies Python and R programming statistical libraries for geospatial modelling of the data sets. The methodological approach of the statistical data analysis by scripting libraries aimed to visualize geomorphic variations in the 25 transect profiles of the trench. Multiple factors affect submarine geomorphology causing variations in the gradient slope: geological settings (rock composition, structure, permeability, erodibility of the materials), submarine erosion, gravity flows of water streams, tectonics, sediments from the volcanic arcs, transported by transverse submarine canyons. Understanding changes in geomorphic variations is important for the correct geospatial analysis. However, modelling such a complex structure as hadal trench requires numerical computation and advanced statistical analysis of the data set. Such methods are proposed by R and Python programming languages. Current research presented usage of statistical libraries for the data processing: Matplotlib, NumPy, SciPy, Pandas, Seaborn, StatsModels by Python. The research workflow includes following steps: Partial least squares regression analysis; Ordinary Least Square (OLS); Violin plots and Bar plots for analysis of ranges of the bathymetric data; Isotonic Regression by StatsModels library; Data distribution analysis by Bokeh and Matplotlib libraries; Circular bar plots for sorting data by R; Euler-Venn diagrams for visualizing overlapping of attributes and factors by Python. As a result of the data analysis, the geomorphology of the trench slopes in 25 transecting profiles was modelled. The results achieved by the statistical data modelling show differences in the gradient slope in various segments of the trench depending on its spatial location. This shows complex geological structure of the trench. The paper contributes towards the methodological development of the data analysis in marine geology through the stepwise workflow explanations with a case study of Python and R applications