Information value-based geochemical anomaly modeling: A statistical index to generate enhanced geochemical signatures for mineral exploration targeting

Abstract

Converting geochemical exploration data into information relevant to descriptive statistics of indicator elements facilitates gaining the knowledge of spatial and genetic relationships between mineralization and elemental dispersion patterns. This allows obtaining insights into alternatives to exploration strategies aiming at vectoring toward undiscovered mineral deposit sites. In this regard, shaping geochemical data for better understanding of their underlying patterns and extraction of information about ore deposition-related anomalies are challenging issues. To better extraction of the information from the geochemical data, we adapted the “information value” concept to quantify the significance of geochemical anomaly classes as spatial proxies of mineral deposits. To illustrate and evaluate the procedure proposed, a dataset of porphyry-Cu exploration was used to delineate exploration targets in the Baft district, Kerman province, Iran. Through this, geochemical models of information values were created for ore deposition-related elements. Then, the geochemical models were combined to produce an information value-based multi-element geochemical signature aiming at utilizing the prediction ability of every indicator element in a single model. Through the combination, spatial patterns of geochemical signals around mineral deposits are revealed that makes it easier to explore undiscovered deposit sites. We also applied fuzzy operators, as common and alternative practices, to integrate values of the same indicator elements for comparison purpose. The results demonstrated that the model of information value-based multi-element geochemical signature is an efficient geochemical proxy, over those geochemical models generated by the application of fuzzy operators, for exploration targeting.