AI Summary & Key Takeaways
AI InsightForth, geospatial analysis of the bathymetry shows that the deepest part of the trench is located in the south-west.
📌 3 Key Takeaways
Abstract
Gretl and R statistical libraries enables to perform data analysis using various algorithms, modules and functions. In this study, the geospatial analysis of example case study of Mariana Trench, a deep-sea hadal trench located in west Pacific Ocean, was performed using multi-functional combined approach of both Gretl and R libraries. The study aim was to model and visualize trends in variations of the trench’s properties: bathymetry (depths), geomorphology (steepness gradient), geology, volcanism (igneous rocks). The workflow included following statistical methods computed and visualized in Gretl and R libraries: 1) descriptive statistics; 2) box plots, normality analysis by quantile-quantile ( QQ ) plots; 3) local weighted polynomial regression model (loess), 4) linear regression by several methods: weighted least squares (WLS) regression , ordinary least squares (OLS) regression , maximal likelihood linear regression and heteroskedasticity regression model; 5) confidence ellipses and marginal intervals for data distribution; 6) robust estimation by Nadaraya–Watson kernel regression fit; 7) correlation analysis and matrix. The results include following ones. First, the geology of the trench has a correlation with a slope angle gradient and igneous rocks (volcanism effect). Second, the sedimentation is distributed unequally by tectonic plates. Third, there is a correlation between the slope gradient and aspect degree. Forth, geospatial analysis of the bathymetry shows that the deepest part of the trench is located in the south-west.