Topographic surface modelling using raster grid datasets by GMT: example of the Kuril–Kamchatka Trench, Pacific Ocean

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  Reports on Geodesy and Geoinformatics , 2019, Vol. 108, pp. 9–22 DOI: 10.2478/rgg-2019-0008 Received: 20 July 2019 / Accepted: 4 October 2019Published online: 4 November 2019 ORIGINAL ARTICLE Topographic surface modelling using raster griddatasets by GMT: example of the Kuril–KamchatkaTrench, Pacific Ocean Polina Lemenkova 1 * 1 College of Marine Geo-sciences, Ocean University of China, 238 Songling Rd., Laoshan, 266100, Qingdao,Shandong Province, People’s Republic of China * pauline.lemenkova@gmail.com  Abstract The study area is focused on the Kuril–Kamchatka Trench, North Pacific Ocean. This region is geologically complex, notablefor the lithosphere activity, tectonic plates subduction and active volcanism. The submarine geomorphology is complicatedthrough terraces, slopes, seamounts and erosional processes. Understanding geomorphic features of such a region requires precise modelling and effective visualization of the high-resolution data sets. Therefore, current research presents aGeneric Mapping Tools (GMT) based algorithm proposing a solution for 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 orthogonalcross-section profiles; modelling geomorphic trends of the gradient slopes; computing raster surfaces from the xyz datasets 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 morequantitative 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 effectiveness of the GMT: a variety of modules, approachesand tools that can be used to produce high-quality mapping and graphics. The GMT listings are provided for repeatability. Key words : GMT, cartography, Kuril–Kamchatka Trench, Raster Grid Modelling, mapping, data analysis 1 Introduction Current paper introduces the use of Generic Mapping Tools(GMT) for the cartographic workflow aimed at the geologicaldata visualization. Among the wide range of GIS software usedfor mapping and geospatial data modelling, GMT stands apartfrom the traditional tools such as ArcGIS (ESRI Team, 2010), QGIS (QGIS, 2019) and MapINFO. The particularity of the GMT comparing to the standard GIS consists in its fundamentallydifferentapproachtowardsgeodataprocessing. TheGMT,src-inallydevelopedby WesselandSmith(1998)isnotaclassicGIS  but a toolset of the shell/bash scripts that should be written bya cartographer for mapping rather than operating with stan-dard GUI and panel tools. Each line of the code in GMT scriptpresents a command used for specific purposes: visualizingspecific elements, modelling grid, generating colour palette,drawing lines on the map, adding logo, scale bar, setting upparameters (fonts, offset, map layout), adding directional roseand so on. The sequence of the several GMT commands to- This work is available in Open Access model and licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.Publisher: De Gruyter 9  10  |  Reports on Geodesy and Geoinformatics , 2019, Vol. 108, pp. 9–22 gether combine a script that can be run from the command lineof the GMT console. Running a GMT script results in an out-put map. The resulted map is generated as a PostScript imagefile (ps) that can be further converted into standard graphicalformat (e.g., tiff, jpeg).Marine geological data analysis include two major ap-proaches: GIS based data modelling and statistical analysis.Thefirstapproach,thestatisticalanalysisofthemarinegeolog-ical observations can be found in various works (Brown et al.,2012; Gallo et al., 2015; Harris and Whiteway, 2011; Lemenkova, 2019c,e; Meibom and Anderson, 2003; Ravisankar et al., 2015; Strick et al., 2018), in which the authors test various statistical approaches, software and libraries by introducing the latest al-gorithm technologies specifically for oceanographic data anal-ysis. The second approach, a GIS mapping for the geospatialdata mapping, consists of the multiple reports with mostly us-age of ArcGIS. A brief summary of the up to date applicationsof GIS in the marine studies can be read in the following works:Ichino et al. (2015); Metz et al. (2014); Rovere et al. (2019); Sen et al. (2016); Yesson et al. (2011). Examples of the photogram- metric approaches of image analysis include, for example, Li-DAR and processing digital elevation model (DEM) ( Altuntas, 2019), GNSS and GPS data geodetic modelling (Banasik and Bu- jakowski, 2017; Tomaszewski et al., 2017). The traditional GIS, for example, ArcGIS, is used in the thematic mapping: envi-ronmental analysis (Klaučo et al., 2013, 2017); geomorpholog- ical assessment (Lemenkova et al., 2012); marine cartography (Suetova et al., 2005). Advanced data analysis with program- ming and scripting based approaches may be used for the ur- ban mapping, for example, CityGML XML with 3D modelling( Janečka, 2019), marine geological mapping, for example, R (Lemenkova, 2019d) or Python (Lemenkova, 2019c), combina- tion of GMT and ArcGIS (Gauger et al., 2007). Current paper presents the advantages of GMT to increaseits popularity. The fundamental difference of the GMT fromtraditional GIS lies in its scripting approach which enablessemi-automated processing of the data in large volumes (e.g., big data in geosciences). In total, the open source GMT col-lection consists of approximately 80 command-line modulesfor the processing of geographic and Cartesian data sets. Sev-eral grid and vector layers are embedded in the GMT, such asthe world coastline ( Wessel and Smith, 1996) that can be vi- sualized directly using coordinates and choosing projection. A variety of cartographic projections and transformations (over30) are presented in the GMT enabling cartographer to selectcoordinates, suitable map orientation and additional elements(e.g., standard parallels, meridian). The effectiveness of theGMT consists in flexibility and variety of the modules that en-ables to perform high-quality mapping. The flexibility of theusage of the GMT modules for data modelling proves it to bean excellent tool set for the geomatics and cartographic datavisualization.The GMT compatibility with Unix commands enables a car-tographer to insert  echo ,  rm  Unix commands directly in theGMT code and to integrate GMT data modelling with furtherdata processing to Matlab. Unix  echo  is a command availablein shell Unix scripts. Its meaning is as follows. It puts thestring texts passed as arguments to the visualization on thecomputer screen or printed output. In other words, it enablesto put the text to the screen or a computer file as part of a se-quential pipeline of Unix commands. For example, using  echo ,one can write directly a piece of text on any location of themap, as illustrated in Fig. 14. The text on graphs A and B inFig. 14 in red, blue and brown colours (’Pacific Ocean, PacificPlate, ’Median stacked profiles’, ...) were written using the echo  Unix command. The same can be seen in Fig. 15, subplotsB–E and G–J, where formulae (placed in lower left corner onthe light yellow background) were also visualized using  echo Figure 1.  Study region: Sea of Okhotsk and Kamchatka area (Mer-cator oblique projection) command. The placing of   echo  command can be seen in thefragment in Listing 10 (GMT code for trend modelling). The rm  Unix command is an abbreviation meaning remove files. Itremoves the auxiliary files after the execution of the script toavoid the overstock of the computer memory. It is commonlyused in the programming environments aimed to delete un-necessary files automatically, after the batch processing is fin-ished. Besides, GMT enables both 2D and 3D data modellingand high-quality data visualization, which is explained in thefollowing sub-chapters stepwise with code snippets. 2 General Situation 2.1 Geographic Location The study are is focused on the Kuril–Kamchatka Trench, Kam-chatka Peninsula and the Sea of Okhotsk located in the FarEast, North Pacific Ocean (Figure 1). The Sea of Okhotsk is asemi-enclosed sea, connected in terms of bathymetry with theopen Pacific Ocean through the Bussol and Krusenstern straits(Maiorova and Adrianov, 2018). The Sea of Okhotsk is not iso- lated, has maximal depths of 3374 m. It has deep-sea straits tothe NW Pacific Ocean, the deepest of which are the Bussol withdepths reaching 2300 m ( Avdeiko et al., 2007; Brandt et al., 2018) (Figure 2). The Sea of Okhotsk is connected with the Pacific Ocean hydrologically 14through the Bussol Strait. Theupper water masses of the Kuril–Kamchatka Trench region arestrongly influenced by the currents in opposite directions: theOyashio coming from the Arctic southwards into the PacificOceanandtheKuroshiocomingfromtheSouthChinaandflow-ing in the northeast direction (Belkin et al., 2009; Tyler, 2002). 2.2 Geology The region of the Far East region is notable for particularlyhigh seismicity, repeated earthquakes and unstable geologicsettings. The particular seismic features of the area are sub-marine earthquakes generated within the island-arc slope of the Kuril-–Kamchatka Trench (Figure 3) seismic belt that canreach  M  = 9.0 and trigger tsunami waves. Comparing to othernatural hazards, such as earthquakes, floods, and typhoons,tsunami is ranked fourth in terms of damage to human lifeand infrastructure losses. Among the most recent such events, there is 2006–2007  Lemenkova |  11 Figure 2.  Topographic map of the study area: Sea of Okhotsk,Kamchatka Peninsula, Greater Kuril Chain and Kuril–Kamchatka Trench. Numerical data source: ETOPO 1Global Relief Model 1 minute raster grid. Great Kuril Earthquake Sequence that involved coupled underthrusting and extensional faulting on a large scale in this area(Lay et al., 2009). Most of the earthquakes within the Kuril– Kamchatka Trench are located in an area between the trenchaxis and the edge of the Kuril islands and land shelf, wherethe Pacific plate is subducting beneath the island-arc zoneof Eurasian continental lithosphere (Lobkovsky and Sorokhtin,1979). 2.3 Tectonics During the Late Eocene, the Kuril–Kamchatka subduction zoneand volcanic front was located in the Sredinny Mountains of the Kamchatka Peninsula, and migrated ca. 150 km eastwardsthereafter, towards the eastern shore of the Kamchatka Penin-sula. This migration resulted in the formation of the SredinnyMountains on the Kamchatka Peninsula, a series of volcanoeson the Central Kamchatka Depression as well as Eastern Vol-canic Plateau (Barr and Spagnolo, 2013). The seismic situation is reflected by the bedrock in theKuril–KamchatkaTrench, mostlydominatedbytheQuaternaryand Miocene–Plocene volcanic complexes (Persits et al., 1997).  As a consequence, the tsunamis in the Far East region in gen-eral and in Kuril–Kamchatka area in particular, are frequent,extensive, and devastating compared to the other regions of the Pacific Ocean (Hatori, 1971; Soloviev, 1968, 1972). 2.4 Marine Biology The uniqueness of the Sea of Okhotsk consists in its richnessof the marine deep-sea biota. In particular, the high levelof biodiversity is shown in the eutrophic area of the Kuril–Kamchatka Trench, which is explained by the turbulence of the water masses. The abyssal echiurans fauna in the Kuril–Kamchatka Trench and the deep-sea basin of the Sea of Japanshow an intermediate level of species biodiversity and speciesrichness (Maiorova and Adrianov, 2018). However, in other ge- ographic places of the Kuril–Kamchatka basin, a very rich ma- Figure 3.  ContourbathymetricareaoftheKuril–KamchatkaTrench.Conic projection. rinefaunaisrecorded(Fischeretal.,2015;Zenkevich,1963). In general, thegeographiclocationofthetrenchandabyssalplainaffects its biodiversity (Schmidt et al., 2019); despite a vertical separation of the seafloor of the trench from the euphotic zone,it underlies a richly productive boreal region, because it is lo-cated near the coasts of the Kamchatka Peninsula and GreaterKuril Chain. Steep slopes of the trench serve as depositionalcentres of the organic carbon accreted on the seafloor via thelateral transport from the continental margins forming deep-sea biological hotspots (Danovaro et al., 2003; Itoh et al., 2011). 3 Methodology Methodology includes application of the GMT scripting toolsetfor the raster thematic maps visualization and automated digi-tizingoftheprofilesacrosstheKuril–KamchatkaTrenchcross-ing the trench in a perpendicular direction. A sequence of theGMT codes is explained below in the relevant subsections. TheGMT coding was used to visualize raster and vector data, per-form geomorphological modelling, data analysis and compar-ison of the two segments of the Kuril–Kamchatka Trench, asdescribed below. Several cartographic projections were testedformapsvisualizationinGMT:theObliqueMercatorprojection(showninFigure 1)wasusedtoshowKuril–KamchatkaTrencharea along a great circle other than the Equator or a TransverseMercator. Figures 7 to 14 are presented in Mercator projection. Twomapsareplottedintheconicprojection: equal-areaAlbersprojection (Figure 3) and equidistant conic projection (Figure6). Figure 4 and 5 are presenting 3D modelling visualizing ge- omorphology of the study area. 3.1 Research questions and purpose The main purpose of the research presented here is to pro-vide a technique for the spatial modelling and mapping of theoceanic trench by remote sensing methods: visualizing rasterdata grids using GMT without having a fieldwork in the studyarea. To achieve this, the role of the geological and tectonicsetting that may affect the geomorphology of the trench wereinvestigated and reviewed in the Section 2 (General Situation).  12  |  Reports on Geodesy and Geoinformatics , 2019, Vol. 108, pp. 9–22 The cartographic means and GMT modules taken to investigateKuril–Kamchatka geospatial setting is described below. There-fore,theobjectiveofthisresearchwastoderivenewGMTbasedtechniques applied for cartographic mapping, visualizing andgeomorphic modelling of the geospatial data with a case studyof the Kuril–Kamchatka Trench region, Far East, North PacificOcean.The general questions of this research are: first, how differ-ent the topographic bathymetric settings are in northern andsouthern parts of the trench, and how to perform modellingtechnically by means of GMT scripting using the available datasets. With this aim, Figure 14 and Figure 15 visualize the varia- tions in the gradient slope of the trench. Second question wasto understand, if the topography varies across the length of thetrench. With this aim, the topographic contours were mappedto visualize the whole study area of the Sea of Okhotsk andNorth-West Pacific Ocean (Figure 3) and modelled through theraster grid specifically for the Kuril–Kamchatka Trench (Fig-ure 10). The geophysical settings of the study area are vi-sualized through modelled geoid (Figure 6 and Figure 7) and gravimetry (Figure 8 and Figure 9). The enlarged fragment of  the gravity along the Kuril–Kamchatka Trench is presented inFigure 11. 3.2 Data In this research, high-resolution geodata collection was per-formed from the website USGS and available embedded layersof the GMT from the SOEST, University of Hawaii, and NOAA Laboratory for Satellite Altimetry ( Wessel and Smith, 1996; Smith and Sandwell, 1997b): i. The main data include ETOPO1 1 arc-minute global relief modelofEarth’ssurfacefromNOAA,usedforvisualizationof the bathymetry taken from the official website: ETOPO1 thesrcinal grid raster file ( earth_relief_01m.grd ) in NetCDF for-mat was produced for modelling and mapping in GMT, thatis, Figure 2, Figure 3, Figure 14 (modelled profiles are based on the ETOPO1), Figure 15 as a visualization of the modelledprofiles based on ETOPO1.ii. The ETOPO5 5 arc-minute global relief model of theEarth’s surface ( earth_relief_05m.grd ) was used for mod-elling Figure 3 (contour map), Figure 4, Figure 5 (3D maps). iii. Gridding bathymetric contours from the xyz data were based on the data taken from the public official website:Global seafloor topography from satellite altimetry. Fromthere, the necessary square was selected by the coordinatesfollowingWest-East-South-Northusualsystem(inthiscase,the square was the following: 144 ◦  W–162 ◦  W; 40 ◦ N–51 ◦ N).The derived xyz data were used as a table and then processedand visualized on Figure 12, Figure 13. iv. The raster data on gravity and geoid were used from theScripps Institution of Oceanography and visualized on Fig-ure 7, Figure 8, Figure 9. v. Coastal borders on all the maps were based on the NOAA:Global Self-consistent, Hierarchical, High-resolution Geog-raphy Database (GSHHG). 3.3 3D-topographical mesh modelling The 3D-modelling of the trench highlighting the bathymetricpatterns of the study area is presented in Figure 4 and Figure 5 plottedusingthemainmodule grdview , whichreadsa2Drastergrid on Kuril–Kamchatka area and produces a 3D perspectiveplot.Numerical data source for the grid: Smith and Sandwell(1997a). Visualizing ETOPO data set with 5 minute resolution Figure 4.  Composite overlay of the 3D-topographical mesh modelon top of the 2D grid contour plot. Contour bathymet-ric map source: ETOPO 5 min grid resolution. Azimuthrotation: 165/30. Figure 5.  Composite overlay of the 3D-topographical mesh modelon top of the 2D grid contour plot. Contour bathymetricmap source: ETOPO 5 min grid resolution. Azimuth rota-tion: 135/30.  Lemenkova |  13 Figure 6.  Colour geoid image of the Kuril–Kamchatka Trench.Conic projection.  was performed through the sequence of main GMT modules: img2grd  and  grdimage . Because processing of the 1-min ETOPOpresented a too detailed and large 3D map, it was questionableif applying higher resolution data continuously over the wholeregion of the Kamchatka area is beneficial, since the gain ininformation was too small for the price. Therefore, the 5-minresolutionforthe3Dmodellingwaschosenfortheselectedseg-ment presented of Figure 4 and Figure 5 (rotated). The  makecpt module was used for generating colour palette. Further tech-niques include drawing a mesh painted by a coloured surface.Inthiscasea -Qsm commandinthepresentedcodebelowstandsfor added mesh lines plotted as contours on top of the surface,and added a plane at the z-level equal to -7500 (that is a bathy-metric depth in this case). It is worth noting that two maps arecombined together on one layout, presented with a set up dis-tance between them: a 2D oblique contour and a 3D map. Thechosen colour palette is artificial ’rainbow’. As can be seenfrom the code below (Listing 1), all commands generate a partof the map that is read into the ’ps’ file defined initially. Atthe next steps, the elements of the 2D map are added stepwise.The 3D grid was added on the map through the following code(Listing1). Herethe -J -R standfortheprojectionderivedfromthe basic 2D map,  R  stands for the region,  -p165/30  shows theazimuth angle of the 3D grid,  -Qsm -N-7500  shows the baselineofthelevelofthe3D.Figure4isshowingthatthemesh3Dgridis the rotation azimuth angle 165 ◦ versus 135 ◦ in the Figure 5. 3.4 Geoid image modelling The geoid data were taken from the Satellite Geodesy researchgroupatScrippsInstitutionofOceanography, UniversityofCal-ifornia San Diego, Global Geoid Online Data Set. The geodeticsituation in the study area was visualized by the GMT modules(Figure 6) using the following GMT code snippet (Listing 2): stepwise. The output image showing modelled geoid is pre-sented in Figure 6. The scale colours used for the data visu-alization are artificial ’rainbow’ to highlight the differences intheelevationoftheshapethattheoceansurfacewouldtakeun-der the influence of the gravity and rotation of the Earth. Thegeodetic situation in the study area with shaded overlapped re-lief of the adjacent study area was visualized by the sequenceGMT modules (Figure 7) using the following code (Listing 3). Figure 7.  ModellinggravityregionalsettingintheOkhotskSeaarea.Mercator projection. 3.5 Gravitational regional modelling Gravity model reflects the deformation of the Earth’s surfaceand is connected with the tectonic settings of the study area,such buoyancy stress of the tectonic slabs, vertical gravitysignal in the forearc (Boutelier and Oncken, 2011; Ramberg, 1967). The key GMT modules  grdimage  and  grdcontour  wereused to generate gravity image using available numerical data(Sandwell et al., 2014) with shading using geoid data. Ma- rine free-air gravity modelling is a useful approach for com-puting non-isostatic bathymetry. In this case, the additionaleffects of the sedimentation on the seafloor, and lithosphericsubsidence related to age are removed from the bathymetry. As a result, a non-isostatic topographic map presents an im-proved approximation to the deformed shape of the Earth’sseafloor and tectonic plate (Zhang et al., 2014). The selected colour palette is ’GMT_haxby’ which is a GMT embedded BillHaxby’s colour scheme specially designed for geoid and grav-ity. The data on the marine free-air gravity anomalies (FAGA) were derived and estimated from the available satellite altime-try missions from the CryoSat-2 and Jason-1 radar altimeterdata. Modelling the marine free-air gravity anomaly along theKuril–Kamchatka Trench (Figure 8) was performed using GMTmodules by the following code snippet (Listing 4) using avail-able data set (Smith and Sandwell, 1997b). Figure 9 shows a marine free-air gravity modelling along the study area of theKuril–Kamchatka Trench on the junction between the Pacificand Okhotsk tectonic plates. Finally, the base map includinggrid, title, coastline, direction rose and scales were added viathe GMT  pscoast  module. 3.6 Surface modelling In addition to the main study area including the trench area(140 ◦ E/170 ◦ E/40 ◦ N/60 ◦ N), the area of the trench was minia-turized for surface modelling compared to that in the majormap series with the enlarged scale of the square with coordi-nates 144 ◦ E/162 ◦ E/40 ◦ N/51 ◦ N, as can be noticed on the respec-tive maps (Figure 10 and Figure 11). Surface modelling by the curvature splines from the ASCII data was performed and anal-ysed with a given data of topography and gravity. The geoiddata were taken from the Satellite Geodesy research group at
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