BIG EARTH DATA PROCESSING USING MACHINE LEARNING FOR INTEGRATED MAPPING OF THE DEAD SEA FAULT, JORDAN

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.