GMT Based Comparative Analysis and Geomorphological Mapping of the Kermadec and Tonga Trenches, Southwest Pacific Ocean

Lemenkova, Polina

doi:10.21163/gt_2019.142.04

Cited by 36 publications

(28 citation statements)

References 28 publications

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“…The GMT cross-section stacking methodology [51][52][53] proved to be a successful means of visualizing and plotting geomorphological models applied for the submarine bathymetry by effectively minimizing the hand-made cartographic routine. Using GMT enables to automatically digitize profiles, perform statistical analysis by plotting histograms and model trends for the general shape of the profiles using mathematical models of the lines approximation.…”

Section: Resultsmentioning

confidence: 99%

Cartographic Interpretation of the Seafloor Geomorphology Using GMT: a Case Study of the Manila Trench, South China

Lemenkova

2020

Aksaray University Journal of Science and Engineering

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The study is geographically focused on the Manila Trench, located in the west Pacific Ocean, South China Sea, west Philippines. The research aims at the geological mapping, analysis and visualizing variations in the submarine geomorphology of the Manila Trench. Technically, the work was done using Generic Mapping Tools scripting toolset (GMT). A combination of various GMT modules was applied for geospatial modelling. Methodology includes cartographic data integration and interpretation through approaches of data analysis: topographic plotting, geophysical modelling, geological mapping and statistical analysis. The data included SRTM, ETOPO1, geoid and gravity grids (CryoSat-2, Jason-1). Two sets of the cross-section profiles of the trench were automatically digitized. The profile transects were compared and differences in the geomorphic shape in southern and northern parts revealed. Southern part has steeper slope on the western part. Northern part is steeper on the continental slope part. The submarine terraces are located on the northern segment at depths-2,000 m. The depth and geomorphology of the slope vary for the range-3,500 to-4,500 m: minimals for the northern part with 526 samples (18.2%) for the depths-4,000 to-4,200 m. The histogram for the northern part has bimodal distribution with two peaks. The southern part shows 142 values for the minimals-3,500 to-3400 m. The statistical analyses revealed that northern part of the trench is deeper. The GMT functionality shown in this paper enabled integration and interpretation of the multi-source data: automatically digitized profiles, geological mapping, 2D and 3D bathymetric modelling, statistical analysis, mathematical approximation of the trend modelling. The GMT proved to be capable of visualizing geodata that can significantly improve Earth studies and interpretation of submarine geomorphology of the oceanic trenches through the advanced data analysis.

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Section: Resultsmentioning

confidence: 99%

Cartographic Interpretation of the Seafloor Geomorphology Using GMT: a Case Study of the Manila Trench, South China

Lemenkova

2020

Aksaray University Journal of Science and Engineering

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“…Many studies reported methods, approaches and algorithm to better process and model data aimed at highlighting phenomena. Examples of various methods of statistical data analysis include data visualization, regressions, correlation, inference (Roberts et al, 2019;Lemenkova, 2019d), velocity modeling using grid based travel-time calculation method (Fujie et al, 2006), regression analysis of variables (Lemenkova, 2019c), factor analysis (Tucker, 1964;Lemenkova, 2018c), clustering and data grouping (Dumont et al, 2018;Lemenkova, 2019h). As for data instruments, examples of application include R programming language (Kotov & Pälike, 2017Lemenkova, 2018a), Python programming language (Yu et al, 2019;Lemenkova, 2019b), SPSS Statistics (Lemenkova, 2019e), Gretl (Lemenkova, 2019d).…”

Section: Discussionmentioning

confidence: 99%

Gebco Gridded Bathymetric Datasets for Mapping Japan Trench Geomorphology by Means of GMT Scripting Toolset

Lemenkova¹

2020

Geodesy and Cartography

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The study investigated geomorphology of the Japan Trench located east of Japan, Pacific Ocean. A high-resolution GEBCO Gridded Bathymetric Dataset was used for modeling, mapping and visualization. The study aimed to compare and analyse variations in the geomorphic structures of the two parts of the trench and to visualize variations in the geological, geophysical and bathymetric settings. Technically, the cartographic work was performed using scripting based on the Generic Mapping Toolset (GMT). Modelled cross-sectioning orthogonal profiles transecting the trench in a perpendicular direction were automatically digitized and graphed in the two segments. The results of the bathymetric analysis shown that the southern part is shallower: with deeper values in absolute (139 samples between –7000 to –8000 m) and statistical records (the most frequent values are within –5500 to –5800 m) comparing to the northern segment (–5300 to –5500 m). The geomorphological analysis shows a more complicated relief in the northern part of the trench, which has a higher seismic activity. The southern part has a gentler slope on the Honshu island side. The geoid modeling along the trench ranges in 0–20 mGal. The highest values are recorded by the Honshu Island (>40 mGal). The rest of the area has rather moderate undulations (20–40 mGal). The free-air marine gravity of the Sea of Japan is <40 mGal. The results include 2D and 3D graphical models, thematic cartographic maps, spatial and statistical analysis of the Japan Trench geomorphology. Tested GMT functionality can be applied to future regional bathymetric modeling of the ocean trenches. All presented maps and graphs are made using GMT scripting toolset.

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“…Besides programming tools, analysis of the geological datasets can be performed by shell scripts as plugins embedded in GIS, or as scripting method of the geodata analysis and visualization using Generic Mapping Tools (GMT). Despite being a specifically cartographic toolset primarily designed for mapping, GMT also includes available modules for the statistical data analysis: histograms, plotting median and mean in a datasets [48][49][50][51][52][53][54][55].…”

Section: Discussionmentioning

confidence: 99%

R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees

Lemenkova

2020

Carpathian Journal of Electronic and Computer Engineering

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The paper presents a comparison of the two languages Python and R related to the classification tools and demonstrates the differences in their syntax and graphical output. It indicates the functionality of R and Python packages {dendextend} and scipy.cluster as effective tools for the dendrogram modelling by the algorithms of sorting and ranking datasets. R and Python programming languages have been tested on a sample dataset including marine geological measurements. The work aims to detect how bathymetric data change along the 25 bathymetric profiles digitized across the Mariana Trench. The methodology includes performed hierarchical cluster analysis with dendrograms and plotted clustermap with marginal dendrograms. The statistical libraries include Matplotlib, SciPy, NumPy, Pandas by Python and {dendextend}, {pvclust}, {magrittr} by R. The dendrograms were compared by the model-simulated clusters of the bathymetric ranges. The results show three distinct groups of the profiles sorted by the elevation ranges with maximal depths detected in a group of profiles 19-21. The dendrogram visualization in a cluster analysis demonstrates the effective representation of the data sorting, grouping and classifying by the machine learning algorithms. The programming codes presented in this study enable to sort a dataset in a similar research aimed to group data based on the similarity of attributes. Effective visualization by dendrograms is a useful modelling tool for the geospatial management where data ranking is required. Plotting dendrograms by R, comparing to Python, presented functional and sophisticated algorithms, refined design control and fine graphical data output. The interdisciplinary nature of this work consists in application of the coding algorithms for spatial data analysis.

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GMT Based Comparative Analysis and Geomorphological Mapping of the Kermadec and Tonga Trenches, Southwest Pacific Ocean

Cited by 36 publications

References 28 publications

Cartographic Interpretation of the Seafloor Geomorphology Using GMT: a Case Study of the Manila Trench, South China

Cartographic Interpretation of the Seafloor Geomorphology Using GMT: a Case Study of the Manila Trench, South China

Gebco Gridded Bathymetric Datasets for Mapping Japan Trench Geomorphology by Means of GMT Scripting Toolset

R Libraries {dendextend} and {magrittr} and Clustering Package scipy.cluster of Python For Modelling Diagrams of Dendrogram Trees

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