Neuro-fuzzy modelling in mining geochemistry: Identification of geochemical anomalies

Abstract Local and mine scale exploration models for anomaly recognition within known ore fields are discussed. Traditional geochemical exploration methods are based on multivariate statistical analysis, metallometry, vertical geochemical zonality and criteria of natural field geochemical associations, which suffer several shortcomings, including lack of a geostatistical generalised approach for separating anomalies from background. These shortcomings make the interpretation process time consuming and costly. Fuzzy set theory, fuzzy logic and neural network techniques seem very well suited for typical mining geochemistry applications. The results, obtained from applying the proposed technique to a real scenario, reveals significant improvements, comparing the results obtained from applying multivariate statistical analysis. Computationally, the introduced technique makes possible, without exploration drilling, the distinction between blind mineralisation and zone of dispersed ore mineralisation. The methodology developed in this research study has been verified by testing it on various real-world mining geochemical projects.