Geochemical exploration data is popular in mineral exploration in that it plays a notable role
in discovering unknown mineral deposits. In this study, we review the state-of-the-art popular …

GIS-based mineral prospectivity mapping (MPM) is a computer-aided methodology for
delineating and better constraining target areas deemed prospective for mineral deposits of …

In this paper, we train an autoencoder network to encode and reconstruct a geochemical
sample population with unknown complex multivariate probability distributions. During the …

Complexities of geological processes portrayed as certain feature in a map (eg, faults) are
natural sources of uncertainties in decision-making for exploration of mineral deposits …

Deep neural networks perform very well in learning high-level representations in support of
multivariate geochemical anomaly recognition. Geochemical exploration data typically …

Integration of stream sediment geochemical data with other types of mineral exploration
data, especially in knowledge-driven mineral potential mapping (MPM), is a challenging …

Abstract The Zhangbaling–Guandian area is located in the eastern part of Anhui Province,
China, and contains several small Au-Cu deposits and occurrences that highlight the …

Index overlay and Boolean logic are two techniques customarily applied for knowledge-
driven modeling of prospectivity for mineral deposits, whereby weights of values in …

Abstract Three-dimensional (3D) geological models are typical data sources in 3D mineral
prospectivity modeling. However, identifying prospectivity-informative predictor variables …

Stream sediment geochemical data are usually subjected to methods of multivariate
analysis (eg factor analysis) in order to extract an anomalous geochemical signature (factor) …