State-of-the-art analysis of geochemical data for mineral exploration

E C Grunsky,P De Caritat

doi:10.1144/geochem2019-031

E C Grunsky, P De Caritat

Open Access

https://doi.org/10.1144/geochem2019-031

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Abstract

Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments and regolith are typically carried out at continental, regional and local scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. Modern geochemical datasets represent a multi-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflecting geochemical/geological processes are described (process discovery). These patterns form the basis from which probabilistic predictive maps are created (process validation). Processing geochemical survey data requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. Problems that are typically associated with geochemical data include closure, missing values, censoring, merging, levelling different datasets and adequate spatial sample design. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyse and interpret geochemical datasets. Geochemical and geological processes can often be recognized through the use of data discovery procedures such as the application of principal component analysis. Classification and predictive procedures can be used to confirm lithological variability, alteration and mineralization. Geochemical survey data of lake/till sediments from Canada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can be generated. Upscaling a multivariate statistics-based prospectivity analysis for arc-related Cu–Au mineralization from a regional survey in the southern Thomson Orogen in Australia to the continental scale, reveals a number of regions with a similar (or stronger) multivariate response and hence potentially similar (or higher) mineral potential throughout Australia. Thematic collection: This article is part of the Exploration 17 collection available at: https://www.lyellcollection.org/cc/exploration-17

Highlights

IntroductionGeochemical datasets can be defined as geochemical data derived from a range of media (e.g. soil, till, regolith, lake sediments, stream sediments, bedrock) collected at a spatial scale consistent with the geological/geochemical processes being investigated
Geochemical datasets can be defined as geochemical data derived from a range of media collected at a spatial scale consistent with the geological/geochemical processes being investigated
Regional scale geochemical surveys can vary from one site per km2 to one site per 500 km2, and continental scale surveys can vary from one site per 500 km2 to one site per 5000 km2 (Geological Survey of Northern Ireland 2007; Reimann et al 2009, 2010, 2014; Caritat & Cooper 2011; Smith et al 2011)

Summary

Introduction

Geochemical datasets can be defined as geochemical data derived from a range of media (e.g. soil, till, regolith, lake sediments, stream sediments, bedrock) collected at a spatial scale consistent with the geological/geochemical processes being investigated. Continental, regional and local scale surveys reveal increasingly detailed processes ranging from the tectonic assemblage of continents to hydrothermal veining, for instance. The intent of geochemical surveys is to provide a spatial geochemical description of the general geology or dominant geochemical processes as manifested in the medium being sampled. Generally described in terms of the average area, or volume (in 3D) that each sample site represents, will have an influence on the detection and discovery of geochemical/geological processes that have acted at a specific spatial scale. It is evident that highdensity surveys are able to detect local scale processes, which can be associated with mineral deposits. As the density of a survey decreases, the likelihood of randomly sampling a site that is associated with alteration or mineralization decreases. The following is a brief summary of the primary considerations that must be taken into account when obtaining, compiling and synthesizing geochemical data prior to statistical treatment and

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