Continental-scale geochemical surveys and mineral prospectivity: Comparison of a trivariate and a multivariate approach

The National Geochemical Survey of Australia (NGSA) provides an internally consistent, state-of-the-art, continental-scale geochemical dataset that can be used to assess areas of Australia more elevated in commodity metals and/or pathfinder elements than others. But do regions elevated in such elements correspond to known mineralized provinces, and what is the best method for detecting and thus potentially predicting those? Here, using base metal associations as an example, I compare a trivariate rank-based index and a multivariate-based Principal Component Analysis method. The analysis suggests that the simpler rank-based index better discriminates catchments endowed with known base metal mineralization from barren ones and could be used as a first-pass prospectivity tool

An improved method for assessing the degree of geochemical similarity (DOGS2) between samples from multi-element geochemical datasets

The multi-element aqua regia National Geochemical Survey of Australia (NGSA) database is used to demonstrate an improved method for quantifying the degree of geochemical similarity (DOGS2) between soil samples. The improvements introduced here address issues relating to compositional data (closure, relative scale). After removing the elements with excessive censored (below detection) values, the rank-based Spearman correlation coefficient (rs) between samples is calculated for the remaining 51 elements. Each element is given equal weight through the rank-based correlation. The rs values for pairs of samples of known similar origin (e.g. granitoid-derived) are significantly positive, whereas they are significantly negative for pairs of samples of known dissimilar origin (e.g. granitoid- v. greenstone-derived). Maps of rs for all samples in the database against various reference samples are used to obtain correlation maps for lithological derivations. Likewise, the distribution of soils having a geochemical fingerprint similar to established mineralized provinces can be mapped, providing a simple, first order mineral prospectivity tool. Sensitivity of results to the removal of up to a dozen elements from the correlation indicates the method to be extremely robust. The new method is compliant with contemporary compositional data analysis principles and is applicable to various digestion methods.The NGSA project was part of the Australian Government’s Onshore Energy Security Program 2006–2011, from which funding support is gratefully acknowledged

Establishing geochemical background variation and threshold values for 59 elements in Australian surface soil

During the National Geochemical Survey of Australia over 1300 top (0–10 cm depth) and bottom (~ 60–80 cm depth) sediment samples (including ~ 10% field duplicates) were collected from the outlet of 1186 catchments covering 81% of the continent at an average sample density of 1 site/5200 km2. The < 2 mm fraction of these samples was analysed for 59 elements by ICP-MS following an aqua regia digestion. Results are used here to establish the geochemical background variation of these elements, including potentially toxic elements (PTEs), in Australian surface soil. Different methods of obtaining geochemical threshold values, which differentiate between background and those samples with unusually high element concentrations and requiring attention, are presented and compared to Western Australia's ‘ecological investigation levels’ (EILs) established for 14 PTEs. For Mn and V these EILs are so low that an unrealistically large proportion (~ 24%) of the sampled sites would need investigation in Australia. For the 12 remaining elements (As, Ba, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Sn and Zn) few sample sites require investigation and as most of these are located far from human activity centres, they potentially suggest either minor local contamination or mineral exploration potential rather than pollution. No major diffuse source of contamination by PTEs affects Australian soil at the continental scale. Of the statistical methods used to establish geochemical threshold values, the most pertinent results come from identifying breaks in cumulative probability distributions, the Tukey inner fence and the 98th percentile. Geochemical threshold values for 59 elements, including emerging ‘high-tech’ critical elements such as lanthanides, Be, Ga or Ge, for which no EILs currently exist, are presented

Publicly available datasets on thallium (Tl) in the environment-a comment on "Presence of thallium in the environment: sources of contaminations, distribution and monitoring methods" by Bozena Karbowska, Environ Monit Assess (2016) 188: 640 (DOI 10.1007/s10661-016-5647-y)

This comment highlights a whole series of datasets on thallium concentrations in the environment that were overlooked in the recent review by Karbowska, Environmental Monitoring and Assessment, 188, 640, 2016 in this journal. Geochemical surveys carried out over the last few decades all over the world at various scales and using different sampling media have reported the concentration of thallium (and dozens more elements) in tens of thousands of samples. These datasets provide a `real-world' foundation upon which source apportionment investigations can be based, monitoring programs devised and modelling studies designed. Furthermore, this comment also draws attention to two global geochemical mapping initiatives that should be of interest to environmental scientists

Catchment-based gold prospectivity analysis combining geochemical, geophysical and geological data across northern Australia

The results of a pilot study into the application of an unsupervised clustering approach to the analysis of catchment-based National Geochemical Survey of Australia (NGSA) geochemical data combined with geophysical and geological data across northern Australia are documented. NGSA Mobile Metal Ion (R) (MMI) element concentrations and first and second order statistical summaries across catchments of geophysical data and geological data are integrated and analysed using Self-Organizing Maps (SOM). Input features that contribute significantly to the separation of catchment clusters are objectively identified and assessed. A case study of the application of SOM for assessing the spatial relationships between Au mines and mineral occurrences in catchment clusters is presented. Catchments with high mean Au code-vector concentrations are found downstream of areas known to host Au mineralization. This knowledge is used to identify upstream catchments exhibiting geophysical and geological features that indicate likely Au mineralization. The approach documented here suggests that catchment-based geochemical data and summaries of geophysical and geological data can be combined to highlight areas that potentially host previously unrecognised Au mineralization.The NGSA project was part of the Australian Government’s Onshore Energy Security Program 2006 – 2011, from which funding support is gratefully acknowledged

Quantifying Diffuse Contamination: Method and Application to Pb in Soil

A new method for detecting and quantifying diffuse contamination at the continental to regional scale is based on the, analysis of cumulative,distribution functions (CDFs). It uses cumulative probability (CP) plots for spatially representative data sets, preferably containing >1000 determinations. Simulations demonstrate how different types of contamination influence elemental CDFs of different sample media. It is found that diffuse contamination is characterized by a distinctive shift of the low-concentration end of the distribution of the studied element in its CP plot. Diffuse contamination can be detected and quantified via either (1) comparing the distribution of the contaminating element to that of an element with a geochemically comparable behavior but no contamination source (e.g., Pb vs Rb), or (2) comparing the top soil distribution of an element to the distribution of the same element in subsoil samples from the same area, taking soil forming processes into consideration. Both procedures are demonstrated for geochemical soil data sets from Europe, Australia, and the U.S.A. Several different data sets from Europe deliver comparable results at different scales. Diffuse Pb contamination in surface soil is estimated to be <0.5 mg/kg for Australia, 1-3 mg/kg for Europe, and 1-2 mg/kg, or at least <5 mg/kg, for the U.S.A. The analysis presented here also allows recognition of local contamination sources and can be used to efficiently monitor diffuse contamination at the continental to regional scale

Chemical elements in the environment: Multi-element geochemical datasets from continental- to national-scale surveys on four continents

During the last 10–20 years, Geological Surveys around the world have undertaken a major effort towards delivering fully harmonised and tightly quality controlled low-density multi-element soil geochemical maps and datasets of vast regions including up to whole continents. Concentrations of between 45 and 60 elements commonly have been determined in a variety of different regolith types (e.g., sediment, soil). The multi-element datasets are published as complete geochemical atlases and made available to the general public. Several other geochemical datasets covering smaller areas, but generally at a higher spatial density, are also available. These datasets may, however, not be found by superficial internet-based searches because the elements are not mentioned individually either in the title or in the keyword lists of the original references. This publication attempts to increase the visibility and discoverability of these fundamental background datasets covering large areas up to whole continents.We thank the governments and other sponsors for funding geochemical surveys, field and laboratory support teams and collaborators for their support, and land owners for granting access to field sites around the globe

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

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.The National Geochemical Survey of Australia project was supported by Commonwealth funding through the Onshore Energy Security Program, and Geoscience Australia appropriation (http://www.ga.gov. au/ngsa)

Recognition of geochemical footprints of mineral systems in the regolith at regional to continental scales

Understanding the character of Australia's extensive regolith cover is crucial to the continuing success of mineral exploration. We hypothesise that the regolith contains geochemical fingerprints of processes related to the development and preservation of mineral systems at a range of scales. We test this hypothesis by analysing the composition of surface sediments within greenfield regional-scale (southern Thomson Orogen) and continental-scale (Australia) study areas. In the southern Thomson Orogen area, the first principal component (PC1) derived in our study [Ca, Sr, Cu, Mg, Au and Mo at one end; rare earth elements (REEs) and Th at the other] is very similar to the empirical vector used by a local company (enrichment in Sr, Ca and Au concomitant with depletion in REEs) to successfully site exploration drill holes for Cu-Au mineralisation. Mapping of the spatial distribution of PC1 in the region reveals several areas of elevated values and possible mineralisation potential. One of the strongest targets in the PC1 map is located between Brewarrina and Bourke in northern New South Wales. Here, exploration drilling has intersected porphyry Cu-Au mineralisation with up to 1 wt% Cu, 0.1 g/t Au, and 717ppm Zn. The analysis of a comparable geochemical dataset at the continental scale yields a compositionally similar PC1 (Ca, Sr, Mg, Cu, Au and Mo at one end; REEs and Th at the other) to that of the regional study. Mapping PC1 at the continental scale shows patterns that (1) are spatially compatible with the regional study and (2) reveal several geological regions of elevated values, possibly suggesting an enhanced potential for porphyry Cu-Au mineralisation. These include well-endowed mineral provinces such as the Curnamona and Capricorn regions, but also some greenfield regions such as the Albany-Fraser/western Eucla, western Murray and Eromanga geological regions. We conclude that the geochemical composition of Australia's regolith may hold critical information pertaining to mineralisation within/beneath it.The studies reported here would not have been possible without Commonwealth funding through the Cooperative Research Centre Program, the Onshore Energy Security Program, and Geoscience Australia appropriation

Surficial and deep earth material prediction from geochemical compositions

Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery