Published December 30, 2019 | Version v1
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Automatic Data Processing for Visualising Yap and Palau Trenches by Generic Mapping Tools

  • 1. Ocean University of China

Description

Current research focus on using Generic Mapping Tools (GMT) cartographic scripting toolset for geodata processing aimed to visualize and compare geomorphic cross-section profiles of two trenches. Study area: Yap Trench (YT) and Palau Trench (PT), two hadal trenches located in south-west Pacific Ocean. Study aim: to do automatic geomorphological modelling by digitizing profiles in a fast and precise way. This was possible using GMT based automatic data processing through modules: “grdtrack”, “psxy” and “convert”. The methodology consisted in extracting raster dataset from the SRTM15_PLUS, automated digitizing of the cross-sectional profiles on the selected segments, plotting profiles graphs for two trenches and statistical analysis on the bathymetry showing depth distribution. The results are the following. YT reaches -7,000 m, PT -6,200 m, that means that PT is shallower. YT has more abrupt slope and steep gradient of the profiles oceanward comparing to PT. For the diapason of -7000 m to -6000 m YT has 68 samples, while PT has 34 samples. PT has more samples below -6000 m, but YT has dominating depths at deeper values. In general, PT has more gentle geomorphic slope land and oceanwards, while YT has steeper slopes and deeper records. Topographic pro- files of the axis of the YT have sharp V-shape while PT has U-shape which indicates more flat bottom of the YT with thin sediment cover of the axis. Development of the machine learning explains the importance of the geodata automatization. Automated cross-sectioning by GMT is more accurate, fast and less error-prone comparing to the manual process of digitizing by hand in traditional GIS. This paper demonstrated a GMT based methodology for the geomorphic modelling that can be applied both for submarine and terrestrial areas in similar works. The paper contributes to the development of the cartographic methodologies.

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Cartographic Letters 2019 27(2) 72-89.pdf

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Journal article: 10.6084/m9.figshare.11544048 (DOI)