Prediction of economic potential of deep blind mineralization by Fourier transform of a geochemical dataset

Abstract

The identification of blind and disseminated mineral deposits is an important challenge in mining exploration. This research presents a quantitative approach to predict a deep, hidden disseminated mineralization using a novel approach named frequency coefficient method (FCM) in mining geochemistry. In deep mineral deposits, the processes of ore formation generate geochemical anomalies at the surface, which have distinct wavelengths and frequencies. The mineralization processes especially deep mineral deposits create geochemical anomalies at the surface with different wavelengths and frequency signals. These deposits can be detected using geochemical signal processing by Fourier transform and interpretation of these frequency signals. In particular, analysis of the geochemical dataset in the frequency domain (FD) proves to be an effective tool for identifying deep ore-formation processes, and hence potentially discovering a hidden mineralization at depth before borehole drilling is carried out. In this study, we apply the FCM to a geochemical dataset made of 104 soil and whole rock elemental analyses from the Au-bearing vein system of Tanurcheh (Khaf-Daruneh belt, north-east Iran) to predict the uneconomic nature of that Au mineralization at depth. Such conclusion has been tested and validated by the Au grade distribution of six exploration boreholes.