Application of 3D geochemistry to mineral exploration

Abstract

ABSTRACT The development of sophisticated 3D visualization software has made it possible to fully integrate geological, geochemical, and geophysical data in three dimensional space creating new opportunities to explore data relationships. The advent of inexpensive, multi-element ICP-MS (inductively coupled plasma-mass spectrometry) analysis techniques with low detection limits has led to the identification of zoned element associations and their spatial relations to ore with great efficiency and clarity. Geochemical modelling of down-hole data has advantages in pattern recognition by facilitating the creation of 3D volumes. This is achieved by producing individual element block models using a gridding algorithm to interpolate concentrations between drill-holes. The block models provide an effective means of exploring relationships in the down-hole data and integrating this information with other subsurface data (geological lithology logs, geophysical inversions) and surface data (geochemistry, geology and geophysics). A key prerequisite for geochemical modelling is the acquisition of down-hole data distributed in a generally systematic fashion along each hole. Some inconsistencies in a geochemical database that need to be addressed prior to modelling include: (1) relative accuracy shifts over time in data reported from a single laboratory or between different laboratories; (2) variable detection limits amongst datasets; (3) mixed partial and total extraction data; (4) special values or zeros representing data below detection limit or missing data; and (5) mixed reporting units. Some applications of 3D geochemistry include: (1) stratigraphic correlation using elements not introduced or significantly redistributed during the mineralizing event; (2) development of a conceptual zonation model of a mineral system; (3) identification of vector criteria for locating high grade mineralization based on zonation relationships; (4) distinguishing proximal from distal signatures; (5) improving vectoring by integrating surface and sub-surface data; (6) improving interpretation of surface data by understanding the effects of surface weathering; (7) locating the bedrock source of anomalies in 3D overburden data; and (8) increasing the understanding of mineral systems and dispersion phenomena. Most of these applications are illustrated using a schematic zonation model for Carlin-type sediment-hosted gold systems in Nevada, USA, a 3D model of the Sleeper low sulphidation gold system in Nevada, and a 3D model of mobile ion dispersion in glacial overburden over the Shoot gold zone, Ontario, Canada.