ioGAS Case Studies & White Papers
Exploratory data analysis (EDA) is the analysis of geochemical data with the aim of recognising trends and anomalies that provide insight into geochemical/geological processes. With ever expanding volumes of data confronting geologists, efficient EDA software has become a critical tool for enhancing exploration productivity.
Exploratory Data Analysis for Target Generation
Multielement assay data can be turned into valuable quantitative information through the use of simple plots, projections of mineral compositions as points and lines onto diagrams, projections of assay data into a known mineralogy ‘space’ and deriving calculated mineralogy . This information can be applied directly to logging, mapping, stratigraphic correlation and the identification and quantification of hydrothermal alteration. It is also possible to derive more inferential estimates of key metallurgical performance parameters such as hardness, acid consumption, and the distribution of deleterious components, both as discrete phases, or as substitutions into specific minerals from such data.
Deriving Quantitative Geological Information from Assay Data
Prior to resource modelling, sample scale data is normally grouped into spatial domains that reflect zones of homogenous properties. These domains are usually based on a combination of grade and other variables that reflect, for example, geology and alteration, or variables known to correlate with properties of the samples that affect downstream processing.
Unsupervised Methods for Assessing Domain Homogeneity
Lithogeochemistry has been successfully applied to gold exploration in deeply weathered terrains. This PDF describes a case history of the application of lithological discriminators determined in fresh material to weathered (in the saprolite) and soil material at an advanced gold project in SW Mali.