Stochastic Modelling of Mineral Exploration Targets

• Published in: Mathematical geosciences Vol. 54; no. 3; pp. 593 - 621
• Main Authors: Talebi, Hassan, Mueller, Ute, Peeters, Luk J. M., Otto, Alex, de Caritat, Patrice, Tolosana-Delgado, Raimon, van den Boogaart, K. Gerald
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2022; Springer Nature B.V
• Subjects: Algorithms; Anomalies; Chemistry and Earth Sciences; Computer Science; Cross correlation; Earth and Environmental Science; Earth Sciences; Geological mapping; Geological processes; Geotechnical Engineering & Applied Earth Sciences; Heterogeneity; Hydrogeology; Learning algorithms; Lithology; Machine learning; Metals; Mineral deposits; Mineral exploration; Mineralization; Modelling; Multivariate analysis; Physics; Prediction models; Spatial dependencies; Special Issue; Statistical analysis; Statistical methods; Statistics for Engineering; Stochastic models; Uncertainty; Uncertainty quantification; Mineral prospectivity mapping; Geostatistical learning; Spatial data; Spatial predictive model; Machine learning

Mineral deposits are metal enrichment anomalies, occurring as local manifestations of the interplay between various geological processes that operate at a wide range of temporal and spatial scales. Mineral prospectivity maps are generated by integrating several proxy maps that represent critical geo...