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Journals / Electronic Letters on Science and Engineering / 2019 / Cilt: 15 - Sayı: 3

Geospatial Analysis by Python and R: Geomorphology of the Philippine Trench, Pacific Ocean

Polina LEMENKOVA
Pages
81–94
Publication Date
2019-12-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Application of the statistical methods of the data analysis by Python and R packages has a broad applicability to similar research aimed at modelling landform variations in the submarine geomorphology of the hadal trenches.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The study area is focused on the Philippine Trench, a hadal trench located in the axe of the collision of the Philippine Sea Plate and Sunda Plate, west Pacific Ocean.
📊
Key Findings & Data:Algorithms of the advanced machine learning and graph-based analysis applied for the marine geological data set demonstrated in this research enabled to gain insights into the seafloor geomorphology that can only be accessible by remote sensing methods and modelling.
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Conclusion & Impact:Application of the statistical methods of the data analysis by Python and R packages has a broad applicability to similar research aimed at modelling landform variations in the submarine geomorphology of the hadal trenches.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Philippine Trench · Python · computing

Abstract

The study area is focused on the Philippine Trench, a hadal trench located in the axe of the collision of the Philippine Sea Plate and Sunda Plate, west Pacific Ocean. The research is aimed at the analysis of the trench geomorphology by correlation between changes in slope steepness and environmental variables. The methodology consists in modelling data by statistical libraries of Python and R programming languages. The results revealed that variations in the slope steepness correlate with the sediment thickness across the Philippine Trench. Variations in the landform are caused by a combination of various factors that include geology, tectonic slab dynamics and increasing depths in bathymetry. Algorithms of the advanced machine learning and graph-based analysis applied for the marine geological data set demonstrated in this research enabled to gain insights into the seafloor geomorphology that can only be accessible by remote sensing methods and modelling. Application of the statistical methods of the data analysis by Python and R packages has a broad applicability to similar research aimed at modelling landform variations in the submarine geomorphology of the hadal trenches.

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Cite This Article

LEMENKOVA, P. (2019). Geospatial Analysis by Python and R: Geomorphology of the Philippine Trench, Pacific Ocean. Electronic Letters on Science and Engineering, Cilt: 15 - Sayı: 3, 81–94. https://ajindex.com/articles/133645

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