U ovom istraživanju razvijen je integrisani okvir za analizu podataka o Zemlji (“Big Earth data”) u kon- tekstu geomorfologije Jordana. Istraživanje se bavi korelacijom između nekoliko tematskih skupova podataka, uključujući mašinsko učenje i multidisciplinarne geoprostorne podatke. GIS kartiranje se široko koristi u geološkom mapiranju kao najadekvatnije tehničko sredstvo za vizualizaciju i anal- izu podataka. GIS aplikacije podstiču geološko prospektivno modeliranje vizualizacijom podataka usmjerenih na prognozu mineralnih resursa. Međutim, automatizacija pomoću mašinskog učenja za obradu velikih podataka o Zemlji pruža brzinu i tačnu obradu masivnih skupova podataka iz više izvora. To je moguće primjenom skriptiranja i programiranja u kartografskim tehnikama. Ova stu- dija predstavlja kombinovane metode mašinskog učenja kartografske analize i modeliranja velikih podataka o Zemlji. Cilj studije je da se analizira povezanost faktora koji utiču na geomorfološki oblik Jordana u odnosu na rasjed Mrtvog mora i geološku evoluciju. Tehnička metodologija uključuje tri nezavisna alata: 1) Generic Mapping Tools (GMT); 2) odabrane biblioteke programskog jezika R; 3) QGIS. Konkretno, GMT skriptni program korišćen je za topografsko, seizmičko i geofizičko mapiranje; QGIS - za geološko mapiranje; Programski jezik R - za geomorfometrijsko modeliranje. Shodno tome, tok rada je logično strukturisan kroz ova tri tehnička alata, predstavljajući različite kartografske pri- stupe za obradu podataka. Podaci i materijali uključuju skupove podataka iz više izvora različite rezo- lucije, prostornog opsega, porijekla i formata. Rezultati su predstavili kartografske rasporede kvalita- tivnih i kvantitativnih karata sa statističkim rezimeom (histogrami). Novost ovog pristupa objašnjava se potrebom da se smanji tehnički jaz između tradicionalnog GIS-a i skripti za kartiranje, koje je bit- no za mapiranje velikih podataka, gdje su presudni faktori brzina i preciznost rukovanja podacima i efikasna vizuelizacija postignuta mašinskom grafikom. U radu se analiziraju osnovni geološki procesi koji utiču na formiranje geomorfoloških oblika terena u Jordanu sa 3D vizualizacijom izabranog frag- menta zone rasjeda Mrtvog mora. Istraživanje predstavlja prošireni opis metodologije, uključujući objašnjenja isječaka koda iz modula GMT i primjere upotrebe R-biblioteka ‚raster‘ i ‚tmap‘. Rezultati su otkrili snažnu korelaciju između geoloških i geofizičkih karakteristika terena i geomorfoloških obrazaca. Integrisano proučavanje geomorfologije Jordana zasnovano je na skupovima podataka koji obrađuju više scenarija. Temeljnom analizom predstavljene su regionalne korelacije između geomorfološkog, geološkog i tektonskog okruženja u Jordanu. Rad je doprinio razvoju kartografskog inženjerstva uvođenjem skriptnih tehnika i regionalnim studijama Jordana, uključujući Mrtvo more kao posebnu regiju Jordana. Rezultati uključuju 12 novih tematskih mapa, uključujući 3D model.
In this research, an integrated framework on the big Earth data analysis has been developed in the context of the geomorphology of Jordan. The research explores the correlation between several thematic datasets, including machine learning and multidisciplinary geospatial data. GIS mapping is widely used in geological mapping as the most adequate technical tool for data visualization and analysis. GIS applications encourage geological prospective modeling by visualizing data aimed at the prognosis of mineral resources. However, automatization using machine learning for big Earth data processing provides the speed and accurate processing of multisource massive datasets. This is enabled by the application of scripting and programming in cartographic techniques. This study presents the combined machine learning methods of cartographic analysis and big Earth data modeling. The objective is to analyze a correlation between the factors affecting the geomorphological shape of Jordan with respects to the Dead Sea Fault and geological evolution. The technical methodology includes the following three independent tools: 1) Generic Mapping Tools (GMT); 2) Selected libraries of R programming language; 3) QGIS. Specifically, the GMT scripting program was used for topographic, seismic and geophysical mapping, while QGIS was used for geologic mapping and R language for geomorphometric modeling. Accordingly, the workflow is logically structured through these three technical tools, representing different cartographic approaches for data processing. Data and materials include multisource datasets of the various resolution, spatial extent, origin and formats. The results presented cartographic layouts of qualitative and quantitative maps with statistical summaries (histograms). The novelty of this approach is explained by the need to close a technical gap between the traditional GIS and scripting mapping, which is wider for big data mapping and where the crucial factors are speed and precision of data handling, as well as effective visualization achieved by the machine graphics. The paper analyzes the underlying geologic processes affecting the formation of geomorphological landforms in Jordan with a 3D visualization of the selected fragment of the Dead Sea Fault zone. The research presents an extended description in methodology, including the explanations of code snippets from the GMT modules and examples of the use of R libraries 'raster' and 'tmap'. The results revealed strong correlation between the geological and geophysical settings which affect geomorphological patterns. Integrated study of the geomorphology of Jordan was based on multisource datasets processed by scripting. A thorough analysis presented regional correlations between the geomorphological, geological and tectonic settings in Jordan. The paper contributed both to the development of cartographic engineering by introducing scripting techniques and to the regional studies of Jordan including the Dead Sea Fault as a special region of Jordan. The results include 12 new thematic maps including a 3D model.
Lemenkova, P. (2021). Big Earth data processing using machine learning for integrated mapping of the Dead Sea Fault, Jordan. GLASNIK SUMARSKOG FAKULTETA UNIVERZITETA U BANJOJ LUCI, 1(31), 79-103 [10.7251/gsf2131079l].
Big Earth data processing using machine learning for integrated mapping of the Dead Sea Fault, Jordan
Lemenkova, Polina
Primo
2021
Abstract
In this research, an integrated framework on the big Earth data analysis has been developed in the context of the geomorphology of Jordan. The research explores the correlation between several thematic datasets, including machine learning and multidisciplinary geospatial data. GIS mapping is widely used in geological mapping as the most adequate technical tool for data visualization and analysis. GIS applications encourage geological prospective modeling by visualizing data aimed at the prognosis of mineral resources. However, automatization using machine learning for big Earth data processing provides the speed and accurate processing of multisource massive datasets. This is enabled by the application of scripting and programming in cartographic techniques. This study presents the combined machine learning methods of cartographic analysis and big Earth data modeling. The objective is to analyze a correlation between the factors affecting the geomorphological shape of Jordan with respects to the Dead Sea Fault and geological evolution. The technical methodology includes the following three independent tools: 1) Generic Mapping Tools (GMT); 2) Selected libraries of R programming language; 3) QGIS. Specifically, the GMT scripting program was used for topographic, seismic and geophysical mapping, while QGIS was used for geologic mapping and R language for geomorphometric modeling. Accordingly, the workflow is logically structured through these three technical tools, representing different cartographic approaches for data processing. Data and materials include multisource datasets of the various resolution, spatial extent, origin and formats. The results presented cartographic layouts of qualitative and quantitative maps with statistical summaries (histograms). The novelty of this approach is explained by the need to close a technical gap between the traditional GIS and scripting mapping, which is wider for big data mapping and where the crucial factors are speed and precision of data handling, as well as effective visualization achieved by the machine graphics. The paper analyzes the underlying geologic processes affecting the formation of geomorphological landforms in Jordan with a 3D visualization of the selected fragment of the Dead Sea Fault zone. The research presents an extended description in methodology, including the explanations of code snippets from the GMT modules and examples of the use of R libraries 'raster' and 'tmap'. The results revealed strong correlation between the geological and geophysical settings which affect geomorphological patterns. Integrated study of the geomorphology of Jordan was based on multisource datasets processed by scripting. A thorough analysis presented regional correlations between the geomorphological, geological and tectonic settings in Jordan. The paper contributed both to the development of cartographic engineering by introducing scripting techniques and to the regional studies of Jordan including the Dead Sea Fault as a special region of Jordan. The results include 12 new thematic maps including a 3D model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
