![]() ![]() ![]() 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. ![]() Current research presented usage of statistical libraries for the data processing: Matplotlib, NumPy, SciPy, Pandas, Seaborn, StatsModels by Python. Such methods are proposed by R and Python programming languages. However, modelling such a complex structure as hadal trench requires numerical computation and advanced statistical analysis of the data set. Understanding changes in geomorphic variations is important for the correct geospatial analysis. 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. The methodological approach of the statistical data analysis by scripting libraries aimed to visualize geomorphic variations in the 25 transect profiles of the trench. Technically, the paper applies Python and R programming statistical libraries for geospatial modelling of the data sets. The research question is to identify variations in the geomorphic form and bathymetry in different segments of the trench. This research focuses on the analysis of the submarine geomorphology in the Mariana Trench located in west Pacific Ocean. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |