Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean
International audience The study area is focused on the Kuril-Kamchatka Trench, North Pacific Ocean. This region is geologically complex, notable for the lithosphere activity, tectonic plates subduction and active volcanism. The submarine geomorphology is complicated through terraces, slopes, seamou...
Published in: | Reports on Geodesy and Geoinformatics |
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Other Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
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HAL CCSD
2019
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Online Access: | https://hal.archives-ouvertes.fr/hal-02351014 https://hal.archives-ouvertes.fr/hal-02351014/document https://hal.archives-ouvertes.fr/hal-02351014/file/rgg-2019-0008.pdf https://doi.org/10.2478/rgg-2019-0008 |
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English |
topic |
Geography Generic Mapping Tools GMT cartography Kuril-Kamchatka Trench Raster Grid Modelling mapping data analysis Data modelling Geoid Gravity Bathymetry Topography Topography 3D Spatial analysis GIS GIS & Spatial Analyses Data processing Computer science Data visualization Shell scripts ACM: I.: Computing Methodologies ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] [SDE]Environmental Sciences [SDU.STU]Sciences of the Universe [physics]/Earth Sciences [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph] [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography |
spellingShingle |
Geography Generic Mapping Tools GMT cartography Kuril-Kamchatka Trench Raster Grid Modelling mapping data analysis Data modelling Geoid Gravity Bathymetry Topography Topography 3D Spatial analysis GIS GIS & Spatial Analyses Data processing Computer science Data visualization Shell scripts ACM: I.: Computing Methodologies ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] [SDE]Environmental Sciences [SDU.STU]Sciences of the Universe [physics]/Earth Sciences [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph] [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography Lemenkova, Polina Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean |
topic_facet |
Geography Generic Mapping Tools GMT cartography Kuril-Kamchatka Trench Raster Grid Modelling mapping data analysis Data modelling Geoid Gravity Bathymetry Topography Topography 3D Spatial analysis GIS GIS & Spatial Analyses Data processing Computer science Data visualization Shell scripts ACM: I.: Computing Methodologies ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] [SDE]Environmental Sciences [SDU.STU]Sciences of the Universe [physics]/Earth Sciences [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph] [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography |
description |
International audience The study area is focused on the Kuril-Kamchatka Trench, North Pacific Ocean. This region is geologically complex, notable for the lithosphere activity, tectonic plates subduction and active volcanism. The submarine geomorphology is complicated through terraces, slopes, seamounts and erosional processes. Understanding geomorphic features of such a region requires precise modelling and e effective visualization of the high-resolution data sets. Therefore, current research presents a Generic Mapping Tools (GMT) based algorithm proposing a solution for e effective data processing and precise mapping: iterative module-based scripting for the automated digitizing and modelling. Methodology consists of the following steps: topographic mapping of the raster grids, marine gravity and geoid; semi-automatic digitizing of the orthogonal cross-section pro les; modelling geomorphic trends of the gradient slopes; computing raster surfaces from the xyz data sets by modules nearneighbor and XYZ2grd. Several types of the cartographic projections were used: oblique Mercator, Mercator cylindrical, conic equal-area Albers, conic equidistant. The cross-section geomorphic profiles in a perpendicular direction across the two selected segments of the trench were automatically digitized. Developed algorithm of the semi-automated digitizing of the profiles enabled to visualize gradients of the slope steepness of the trench. The data were then modelled to show gradient variations in its two segments. The results of the comparative geomorphic analysis of northern and southern transects revealed variations in different parts of the trench. Presented research provided more quantitative insights into the structure and settings of the submarine landforms of the hadal trench that still remains a question for the marine geology. The research demonstrated the e effectiveness of the GMT: a variety of modules, approaches and tools that can be used to produce high-quality mapping and graphics. The GMT listings are provided ... |
author2 |
Ocean University of China (OUC) China Scholarship Council (CSC), State Oceanic Administration (SOA), Marine Scholarship of China, Grant Nr. 2016SOA002, Beijing, People’s Republic of China |
format |
Article in Journal/Newspaper |
author |
Lemenkova, Polina |
author_facet |
Lemenkova, Polina |
author_sort |
Lemenkova, Polina |
title |
Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean |
title_short |
Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean |
title_full |
Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean |
title_fullStr |
Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean |
title_full_unstemmed |
Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean |
title_sort |
topographic surface modelling using raster grid datasets by gmt: example of the kuril–kamchatka trench, pacific ocean |
publisher |
HAL CCSD |
publishDate |
2019 |
url |
https://hal.archives-ouvertes.fr/hal-02351014 https://hal.archives-ouvertes.fr/hal-02351014/document https://hal.archives-ouvertes.fr/hal-02351014/file/rgg-2019-0008.pdf https://doi.org/10.2478/rgg-2019-0008 |
geographic |
Pacific |
geographic_facet |
Pacific |
genre |
Kamchatka |
genre_facet |
Kamchatka |
op_source |
Reports on Geodesy and Geoinformatics https://hal.archives-ouvertes.fr/hal-02351014 Reports on Geodesy and Geoinformatics, 2019, 108 (1), pp.9-22. ⟨10.2478/rgg-2019-0008⟩ https://content.sciendo.com/view/journals/rgg/108/1/article-p9.xml |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.2478/rgg-2019-0008 hal-02351014 https://hal.archives-ouvertes.fr/hal-02351014 https://hal.archives-ouvertes.fr/hal-02351014/document https://hal.archives-ouvertes.fr/hal-02351014/file/rgg-2019-0008.pdf doi:10.2478/rgg-2019-0008 |
op_rights |
http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess |
op_rightsnorm |
CC-BY-NC |
op_doi |
https://doi.org/10.2478/rgg-2019-0008 |
container_title |
Reports on Geodesy and Geoinformatics |
container_volume |
108 |
container_issue |
1 |
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9 |
op_container_end_page |
22 |
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spelling |
ftccsdartic:oai:HAL:hal-02351014v1 2023-05-15T16:59:05+02:00 Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean Lemenkova, Polina Ocean University of China (OUC) China Scholarship Council (CSC), State Oceanic Administration (SOA), Marine Scholarship of China, Grant Nr. 2016SOA002, Beijing, People’s Republic of China 2019-11-06 https://hal.archives-ouvertes.fr/hal-02351014 https://hal.archives-ouvertes.fr/hal-02351014/document https://hal.archives-ouvertes.fr/hal-02351014/file/rgg-2019-0008.pdf https://doi.org/10.2478/rgg-2019-0008 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.2478/rgg-2019-0008 hal-02351014 https://hal.archives-ouvertes.fr/hal-02351014 https://hal.archives-ouvertes.fr/hal-02351014/document https://hal.archives-ouvertes.fr/hal-02351014/file/rgg-2019-0008.pdf doi:10.2478/rgg-2019-0008 http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess CC-BY-NC Reports on Geodesy and Geoinformatics https://hal.archives-ouvertes.fr/hal-02351014 Reports on Geodesy and Geoinformatics, 2019, 108 (1), pp.9-22. ⟨10.2478/rgg-2019-0008⟩ https://content.sciendo.com/view/journals/rgg/108/1/article-p9.xml Geography Generic Mapping Tools GMT cartography Kuril-Kamchatka Trench Raster Grid Modelling mapping data analysis Data modelling Geoid Gravity Bathymetry Topography Topography 3D Spatial analysis GIS GIS & Spatial Analyses Data processing Computer science Data visualization Shell scripts ACM: I.: Computing Methodologies ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS ACM: I.: Computing Methodologies/I.4: IMAGE PROCESSING AND COMPUTER VISION ACM: I.: Computing Methodologies/I.5: PATTERN RECOGNITION ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] [SDE]Environmental Sciences [SDU.STU]Sciences of the Universe [physics]/Earth Sciences [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] [SDU.OCEAN]Sciences of the Universe [physics]/Ocean Atmosphere [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph] [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography info:eu-repo/semantics/article Journal articles 2019 ftccsdartic https://doi.org/10.2478/rgg-2019-0008 2020-12-24T12:43:27Z International audience The study area is focused on the Kuril-Kamchatka Trench, North Pacific Ocean. This region is geologically complex, notable for the lithosphere activity, tectonic plates subduction and active volcanism. The submarine geomorphology is complicated through terraces, slopes, seamounts and erosional processes. Understanding geomorphic features of such a region requires precise modelling and e effective visualization of the high-resolution data sets. Therefore, current research presents a Generic Mapping Tools (GMT) based algorithm proposing a solution for e effective data processing and precise mapping: iterative module-based scripting for the automated digitizing and modelling. Methodology consists of the following steps: topographic mapping of the raster grids, marine gravity and geoid; semi-automatic digitizing of the orthogonal cross-section pro les; modelling geomorphic trends of the gradient slopes; computing raster surfaces from the xyz data sets by modules nearneighbor and XYZ2grd. Several types of the cartographic projections were used: oblique Mercator, Mercator cylindrical, conic equal-area Albers, conic equidistant. The cross-section geomorphic profiles in a perpendicular direction across the two selected segments of the trench were automatically digitized. Developed algorithm of the semi-automated digitizing of the profiles enabled to visualize gradients of the slope steepness of the trench. The data were then modelled to show gradient variations in its two segments. The results of the comparative geomorphic analysis of northern and southern transects revealed variations in different parts of the trench. Presented research provided more quantitative insights into the structure and settings of the submarine landforms of the hadal trench that still remains a question for the marine geology. The research demonstrated the e effectiveness of the GMT: a variety of modules, approaches and tools that can be used to produce high-quality mapping and graphics. The GMT listings are provided ... Article in Journal/Newspaper Kamchatka Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Pacific Reports on Geodesy and Geoinformatics 108 1 9 22 |