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Deep-Sea Trenches of the Pacific Ocean: a Comparative Analysis of the Submarine Geomorphology by Data Modeling Using GMT, QGIS, Python and R Mid-Term PhD Thesis Presentation: Current Research Progress

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posted on 2019-11-09, 08:24 authored by Polina LemenkovaPolina Lemenkova
Current presentation reports Mid-Term progress of the PhD research. Research Goals: Investigating the geology and submarine geomorphology of the Pacific trenches.Technical improving and testing of the advanced algorithms of geodata analysis. Applying innovative methods in cartographic data visualization and mapping. Developing techniques of the automatic digitizing of the cross-section profiles. Sequential data processing & modelling by QGIS, Python, R, GMT, AWK, Octave. Automatization in geological data analysis aims at: precision and reliability of the results, increased speed of the data processing, accuracy and precision of the data modelling, crucial for the big data processing common for geological field marine observations. Geospatial analysis to identify variations and to highlights correlations between the geomorphic shape of the trenches (slope steepness gradient, depth ranges). Research Object: Deep-sea trenches of the Pacific Ocean. Research Focus. Submarine geomorphology of the trenches: comparative analysis of their structure. Seafloor bathymetry of the trenches: modelling spatial variations of their patterns. Impact factors affecting trench formation: highlighting their variability. Research Techniques. Methods: data analysis, processing, visualization, statistical modelling, cartographic mapping, 3D and 2D simulation models, graphical plotting. Tools: Generic Mapping Tools (GMT); QGIS plugins; statistical libraries of the programming languages: Python, R, Matlab/Octave and AWK. Presentation includes viculaized maps, preliminary statistical results and discussions.

Funding

China Scholarship Council (CSC), State Ocean Administration (SOA), Marine Scholarship of China, People’s Republic of China (P. R. C.), Beijing, Grant #2016SOA002, 2016-2020.

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