Gold deposits can be successfully located using geographic map analysis
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Peer-reviewed scientific literature demonstrates that geospatial analysis, remote sensing, and spatial predictive modeling using map data and satellite imagery can be successfully used to locate and map gold mineral deposits.
Previous research on Carlin-type gold deposits in North America and China has revealed peculiarities in their genesis, distribution, and prospectivity. However, pinpointing these deposits within known ore districts and prospective areas is a complex and resource-demanding task. Studying the structural and geological characteristics of Carlin-type deposits in areas with a longer history of exploration using machine learning techniques is crucial, especially considering the potential for discovering Carlin-type deposits in Russia. Crustal fracturing fields detected in space imagery and digital relief models can serve as a foundation for prospectivity mapping of Carlin-type deposits, even without evidence of magmatic sources of ore matter. The detection of disjunctive features, observed as linear elements (lineaments) in remote sensing images of the Earth, allows for a quantitative description of the Earth’s crust permeability to ore-bearing magmas and fluids. This can be accomplished using open source pyLEFA software. Optical detection methods facilitate this process, while the assessment of heterogeneity in the distribution of fracture field parameters is achieved using unsupervised learning and classification. Machine learning based on datasets produced with pyLEFA enables the assessment of the contribution of predictor variables to the result. The knowledge acquired can be applied to areas with the potential for discovering Carlin-type deposits.
In this study, predictive models that characterize gold potential zones within the Josephine Prospecting Licence (PL) Area of Northwestern Ghana have been created by data-driven methods comprising frequency ratio and information value. These predictive models were evaluated using known locations of gold (Au) occurrence datasets and compared to each other. The mineral prospectivity models (MPMs) of gold occurrence areas within the Josephine PL Area were constructed by determining the spatial correlation between known locations of Au occurrences and eight mineralization related factors. The locations of these known Au occurrences, which characterize regions of anomalously high Au geochemical concentration and regions of previous or ongoing artisanal mining operations were identified by using geographic positioning systems (GPS). Eight mineralization related factors (geoscientific thematic layers) over the entire study area composed of analytic signal, lineament density, uranium-thorium ratio, uranium, potassium-thorium ratio, potassium, reduction-to-equator and geology were used to generate the MPMs. The predictive capacity of each of the MPMs generated was determined by employing the area under the receiver operating characteristics curve (AUC). The AUC score obtained for the predictive models produced based on the information value and the frequency ratio approaches were respectively 0.794 and 0.815. The AUC scores generated indicate that the MPMs produced are good predictive models (with an AUC greater than 0.7) and can therefore assist in narrowing down the highly prospective zones of mineral occurrences within the study area. However, the overall predictive potential of the frequency ratio approach was better than the model produced by the information value approach.
Mineral resources play a critical role in the sustainable economic development of countries. In Eritrea, conventional mineral exploration and geological mapping methods are expensive, time-consuming, and in some inaccessible areas, difficult to implement. Advances in remote sensing and data-driven analytical techniques now provide efficient alternatives for mineral exploration. This research applies a remote sensing and data-driven approach, combining multispectral Sentinel-2 imagery with field geological data, to identify volcanogenic massive sulfide (VMS) deposits and map lithology in the ar
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