Unravelling the Deformation of Paleoproterozoic Marbles and Zn-Pb Ore Bodies by Combining 3D-Photogeology and Hyperspectral Data (Black Angel Mine, Central West Greenland)

Abstract

The Black Angel Zn-Pb ore deposit is hosted in folded Paleoproterozoic marbles of the Mârmorilik Formation. It is exposed in the southern part of the steep and inaccessible alpine terrain of the Rinkian Orogen, in central West Greenland. Drill-core data integrated with 3D-photogeology and hyperspectral imagery of the rock face allow us to identify stratigraphic units and extract structural information that contains the geological setting of this important deposit. The integrated stratigraphy distinguishes chemical/mineralogical contrast within lithologies dominated by minerals that are difficult to distinguish with the naked eye, with a similar color of dolomitic and scapolite-rich marbles and calcitic, graphite-rich marbles. These results strengthen our understanding of the deformation style in the marbles and allow a subdivision between evaporite-carbonate platform facies and carbonate slope facies. Ore formation appears to have been mainly controlled by stratigraphy, with mineralizing fluids accumulating within permeable carbonate platform facies underneath carbonate slope facies and shales as cap rock. Later, folding and shearing were responsible for the remobilization and improvement of ore grades along the axial planes of shear folds. The contact between dolomitic scapolite-rich and calcitic graphite-rich marbles probably represents a direct stratigraphic marker, recognizable in the drill-cores, to be addressed for further 3D-modeling and exploration in this area.

Three-Dimensional, Km-Scale Hyperspectral Data of Well-Exposed Zn–Pb Mineralization at Black Angel Mountain, Greenland

“…With this dataset we aim to (1) showcase good practices in oblique hyperspectral data processing, (2) demonstrate the added value of fully corrected 3D hyperspectral data to mineral exploration, (3) motivate further workflow development, and (4) provide a relevant geological test case for spectral analysis and machine learning, e.g., the development of deep spectral unmixing methods that could help to make more specific predictions of mineral abundances. In an accompanying paper [5], we extend on the actual geological interpretation and validation of the dataset. There, we demonstrate the dataset's value in increasing the understanding of the deformation style in the Mârmorilik marbles and the subdivision between evaporite-carbonate platform facies and carbonate slope facies.…”

“…The geometries outlined by contacts between the calcitic and dolomitic marbles (Figure 7) highlight a structural complexity that is not apparent using only RGB data from photogrammetric overflights. An interpretation of these geometries is beyond the scope of this data descriptor, but is the subject of an accompanying paper [5].…”

The Black Angel deposit, Greenland: a Paleoproterozoic evaporite-related Mississippi Valley-type Zn–Pb deposit

“…Wave ripples and crossbedding in quartzites of the basal quartzite-semipelite member suggest that deposition of the Mârmorilik Formation started in a shallow-water, highenergy environment, followed by fine-grained sediments, consistent with deepening and finally, as the supply of detrital material decreased, an extensive carbonate platform developed (Henderson and Pulvertaft 1987). Subsequently, the platform was rapidly drowned, with the establishment of a deep and sediment-starved basin which was buried by turbidites of the Nûkavsak Formation (Guarnieri et al 2022b;2022e).…”

Underground hyperspectral outcrop scanning for automated mine‐face mapping: The lithium deposit of Zinnwald/Cínovec

“…These datasets have significant potential for the mining industry as they allow for the integrated representation, analysis, and interpretation of geometric and spectral features. As recently shown in several studies, the hypercloud approach can effectively support exploration and mining activities (e.g., Booysen et al, 2022;Guarnieri et al, 2022;Kirsch et al, 2018;Lorenz et al, 2018;Murphy et al, 2015;Thiele et al, 2021).…”