Quantitative analysis of geoscientific data to determine the areas most likely to contain mineral deposits is becoming increasingly common in the mining industry. The approach is based on characterising areas known to contain deposits and seeking similar areas elsewhere. This paper presents an automatic image processing technique for the prospectivity analysis of Archaean lode gold deposits, wh...

The Bonaparte project is a new multi-year bedrock mapping program of the British Columbia Geological Survey (BCGS) concentrating on Mesozoic arc volcanic and plutonic rocks of the Quesnel Terrane in the northeastern part of the Bonaparte Lake (92P) map sheet (Figure 1). The area encompasses a northwest-trending belt of high mineral potential that includes a number of interesting mineral occurre...

the method of weights of evidence is one of the most important data driven methods for mineral potential mapping in gis. in this method, considering the characteristics of known mineralized locations, we can prospect new mineralized areas. in this research work, the method of weights of evidence has been used for hydrothermal gold potential mapping in torbat-e-heydarieh area, east of iran. as a...

Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts rely on the interpretation of Terrestrial Laser Scanning and oblique photogrammetry, which have ina...

Biofortification of foods, achieved by increasing the concentrations of minerals such as iron (Fe) and zinc (Zn), is a goal of plant scientists. Understanding genes that influence seed mineral concentration in a model plant such as Arabidopsis could help in the development of nutritionally enhanced crop cultivars. Quantitative trait locus (QTL) mapping for seed concentrations of calcium (Ca), c...

We briefly review the state-of-the-art machine learning (ML) algorithms for mineral exploration, which mainly include random forest (RF), convolutional neural network (CNN), and graph (GCN). In recent years, RF, a representative shallow algorithm, CNN, deep approach, have been proved to be powerful tools ML-based mapping exploration. future, GCN deserves more attention exploration because of it...

Hyperspectral imaging technology has been used for geological analysis for many years wherein mineral mapping is the dominant application for hyperspectral images (HSIs). The very high spectral resolution of HSIs enables the identification and the diagnosis of different minerals with detection accuracy far beyond that offered by multispectral images. However, HSIs are inevitably corrupted by no...