Geological mapping in Melville Peninsula, Northwest Territories, Canada using multi-source remote sensing and geophysical data

Author(s):  
C.-J.F. Chung ◽  
Peng Gong ◽  
A.N. Rencz ◽  
M. Schau
2020 ◽  
Vol 9 (2) ◽  
pp. 34
Author(s):  
Wei Yang

<p>Remote sensing technology is widely used in various industries in China, and plays its own role. In geological surveying and mapping, its remote sensing technology can optimize the process of geological surveying and mapping, change the traditional working methods, and make its geological surveying and mapping results more accurate. Therefore, it is necessary to understand the applications of remote sensing technology in geological mapping. In this paper, we need to understand the content of remote sensing technology first, and then explain the specific application of remote sensing technology in geological surveying and mapping, explain the development prospect of remote sensing technology, and provide reference for the corresponding researchers.</p>


2020 ◽  
Author(s):  
Philipp Brandl ◽  
Anna Kraetschell ◽  
Justin Emberley ◽  
Mark Hannington ◽  
Margaret Stewart ◽  
...  

&lt;p&gt;The offshore regions of Eastern Papua New Guinea and the Solomon Islands include several active and remnant arc and backarc systems that formed in response to complex plate tectonic adjustments following subduction initiation in the Eocene. Although there has been extensive exploration for offshore petroleum resources, and more than 54 research cruises have investigated or transited the region since 1993, a comprehensive regional geological map, including the deep marine areas, has not been available at a scale that permits quantitative analysis of the basin history. We present the first map that depicts interpreted assemblage- and formation-level lithostratigraphic units correlated across the marine basins and adjacent land masses. The mapped assemblages and large-scale formations are based on a compilation of land-based geological maps, marine geophysical data (hydroacoustics, magnetics, and gravity) integrated with the results of geological sampling, ocean drilling, seismic surveys, and seabed observations.&lt;/p&gt;&lt;p&gt;More than 400,000 km&lt;sup&gt;2 &lt;/sup&gt;of the map area covered by ship-based multibeam and other geophysical data were inspected to derive the offshore geological units. In areas with limited data, the units were extrapolated from well-documented formations in adjacent regions with more complete information, including on land. This approach follows closely the techniques used for remote predictive mapping in other regions of the Earth where geological information is sparse. Geological boundaries were constrained by ship-based multibeam data reprocessed at 35-m to 50-m resolution and integrated with the Global Multi-Resolution Topography (GMRT) gridded at 100 m. Lithotectonic assemblages were assigned on the basis of plate structure, crustal type and thickness, age, composition, and sedimentary cover and further refined by bathymetric and geophysical data from the literature and cruise reports. The final compilation is generalized and presented here at 1:1 &amp;#1052;. Our new approach integrates conventional mapping on land with remote predictive mapping of the ocean floor.&lt;/p&gt;&lt;p&gt;The newly compiled geological map illustrates the diversity of assemblages in the region and its complex geodynamic evolution. The resolution of our map allows to perform quantitative analyses of area-age relationships and thus crustal growth. Further geoscientific analyses may allow to estimate the regional mineral potential and to delineate permissive areas as future exploration targets.&lt;/p&gt;


Author(s):  
Ying Wang ◽  
Anna K Ksienzyk ◽  
Ming Liu ◽  
Marco Brönner

Summary Modern geophysical data acquisition technology makes it possible to measure multiple geophysical properties with high spatial density over large areas with great efficiency. Instead of presenting these co-located multi-geophysical datasets in separate maps, we take advantage of cluster analysis and its pattern exploration power to generate a cluster map with objectively integrated information. Each cluster in the resulting cluster map is characterised by multi-geophysical properties and can be associated with certain geological attributes or rock types based on existing geological maps, field data and rock sample analysis. Such a cluster map is usually high in resolution and proven to be more helpful than single-attribute maps in terms of assisting geological mapping and interpretation. In this paper, we present the workflow and technical details of applying cluster analysis to multi-geophysical data of a study area in the Trøndelag region in Mid-Norway. We address the importance of carefully designed pre-processing procedures regarding the input datasets to ensure an unbiased data integration using cluster analysis. Random Forest as a supervised machine learning method for classification/regression is strategically employed post-clustering for quality evaluation of the results. The multi-geophysical data used for this study include airborne magnetic, frequency electromagnetic and radiometric measurements, together with ground gravity measurements. Due to the nature of these input data, the resulting cluster map carries multi-depth information. When associated with available geological information, the cluster map can help interpret not only bedrock outcrops, but also rocks underneath the sediment cover.


2014 ◽  
Vol 1010-1012 ◽  
pp. 1380-1386
Author(s):  
Wei Feng Wang ◽  
Chuan Hua Zhu ◽  
Yan Bin Qing ◽  
Xin Jian Shan

The Longmenshan fault zone has been a research hotspot, but fewer scholars have paid attention to its transverse faults. According to the analysis of regional tectonic, seismic activities, geomorphic features, remote sensing images, and deep geophysical data, combined with field studies, the existence, distribution and type of the transverse faults in the Longmenshan fault zone were demonstrated. Research shows that there are 9 transverse faults that lie parallel to each other approximately at ~50km intervals in the Longmenshan fault zone. And transverse faults can be divided into regional transverse faults and localized transverse faults with NW strike, nearly EW strike and nearly SN strike.


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