Application of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral imagery to mineral and lithologic mapping in southern West Greenland

Remote sensing data acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used for mineral and lithologic mapping at the Sarfartoq carbonatite complex area in southern West Greenland. The geology of the study area consists of carbonatites, fenites, hydrothermal alteration zones, gneisses, alluvial deposits etc. The Adaptive Coherence Estimator algorithm was used to analyze the remote sensing data. The reference spectra were selected from the imagery. The mapping results show the distribution of carbonatite, hydrothermally altered zones, fenite, and sericite. In addition, lichen and tundra green vegetation were also mapped. Due to the moderate spatial resolution of ASTER SWIR bands, it was not possible to detect and map the rock units in some parts of the study area. The study shows the possibilities and limitations of the use of the ASTER multispectral imagery for geological studies in the Arctic regions of West Greenland. The paper is the first reported study on the use of ASTER data for mineral and lithologic mapping in the Arctic regions of West Greenland.

Lithologic mapping using Random Forests applied to geophysical and remote-sensing data: A demonstration study from the Eastern Goldfields of Australia

The Eastern Goldfields of Western Australia is one of the world’s premier gold-producing regions; however, large areas of prospective bedrock are under cover and lack detailed lithologic mapping. Away from the near-mine environment, exploration for new gold prospects requires mapping geology using the limited data available with robust estimates of uncertainty. We used the machine learning algorithm Random Forests (RF) to classify the lithology of an underexplored area adjacent to the historically significant Junction gold mine, using geophysical and remote-sensing data, with no geochemical sampling available at this reconnaissance stage. Using a sparse training sample, 1.6% of the total ground area, we produce a refined lithologic map. The classification is stable, despite including parts of the study area with later intrusions and variable cover depth, and it preserves the stratigraphic units defined in the training data. We assess the uncertainty associated with this new RF classification using information entropy, identifying those areas of the refined map that are most likely to be incorrectly classified. We find that information entropy correlates well with inaccuracy, providing a mechanism for explorers to direct future expenditure toward areas most likely to be incorrectly mapped or geologically complex. We conclude that the method can be an effective additional tool available to geoscientists in a greenfield, orogenic gold setting when confronted with limited data. We determine that the method could be used either to substantially improve an existing map, or produce a new map, taking sparse observations as a starting point. It can be implemented in similar situations (with limited outcrop information and no geochemical data) as an objective, data-driven alternative to conventional interpretation with the additional value of quantifying uncertainty.