Research on processing geochemical data and identifying geochemical anomalies has
made important progress in recent decades. Fractal/multi-fractal models, compositional data …

Geochemical Anomaly and Mineral Prospectivity Mapping in GIS documents and explains,
in three parts, geochemical anomaly and mineral prospectivity mapping by using a …

In its 26 years of existence, the journal of Natural Resources Research (NRR) has published
and continues to publish papers on geochemical anomaly and mineral potential mapping …

Abstract The Random Forests (RF) algorithm is a machine learning method that has recently
been demonstrated as a viable technique for data-driven predictive modeling of mineral …

With the increasing use of machine learning for big data analytics, several methods have
been implemented for the purpose of exploration targeting using mineral potential mapping …

Determining indicator element association for mineralization can not only improve mineral
exploration efficiency but also reduce the cost of unnecessary element analysis during …

Among the more popular spatial modeling techniques, artificial neural networks (ANN) are
tools that can deal with non-linear relationships, can classify unknown data into categories …

Large amounts of digital data must be analyzed and integrated to generate mineral potential
maps, which can be used for exploration targeting. The quality of the mineral potential maps …

Mineral exploration endeavors to find ore deposits (ie, economically viable concentrations of
minerals or metals) for mining purposes. Delineation of targets for mineral exploration is the …

A major objective of the ProMine project was to develop a Pan-EU GIS data management
and visualization system for natural and man-made mineral endowment and the …