An Examination of Transformation Techniques to Investigate and Interpret Multivariate Geochemical Data Analysis: Tellus Case Study

This research aims to use the multivariate geochemical dataset, generated by the Tellus project, to investigate the appropriate use of transformation methods to maintain the integrity of geochemical data and inherent constrained behaviour in multivariate relationships. The widely used normal score transform is compared with the use of a stepwise conditional transform technique. The Tellus Project, managed by GSNI and funded by the Department of Enterprise Trade and Development and the EU’s Building Sustainable Prosperity Fund, involves the most comprehensive geological mapping project ever undertaken in Northern Ireland. Previous study has demonstrated spatial variability in the Tellus data but geostatistical analysis and interpretation of the datasets requires use of an appropriate methodology that reproduces the inherently complex multivariate relations. Previous investigation of the Tellus geochemical data has included use of Gaussian-based techniques. However, earth science variables are rarely Gaussian, hence transformation of data is integral to the approach. The multivariate geochemical dataset generated by the Tellus project provides an opportunity to investigate the appropriate use of transformation methods, as required for Gaussian-based geostatistical analysis. In particular, the stepwise conditional transform is investigated and developed for the geochemical datasets obtained as part of the Tellus project. The transform is applied to four variables in a bivariate nested fashion due to the limited availability of data. Simulation of these transformed variables is then carried out, along with a corresponding back transformation to original units. Results show that the stepwise transform is successful in reproducing both univariate statistics and the complex bivariate relations exhibited by the data. Greater fidelity to multivariate relationships will improve uncertainty models, which are required for consequent geological, environmental and economic inferences.

'Trusting the numbers': mineral prospecting, raising finance and the governance of knowledge

Mineral prospecting and raising finance for ‘junior’ mining firms has historically been regarded as a speculative activity. For the regulators of securities markets upon which ‘junior’ mining companies seek to raise capital, a perennial problem has been handling not only the indeterminacy of scientific claims, but also the social basis of epistemic practices. This paper examines the production of a system of public warrant and associated knowledge practices intended to enable investors to differentiate between ‘destructive’ and ‘productive’ varieties of financial speculation. It traces the use of the notion of ‘disclosure’ in constructing and legitimizing the ‘juniors’ market in Canada. It argues that though the work of ‘economics’ may be necessary in the construction of markets, it is by no means sufficient. Attention must also be given to the ways in which legal models of ‘the free-market’ can be translated and constantly re-worked across the sites and spaces of regulatory practice, animating the geographies of markets.

Geochemical records of palaeoenvironmental controls on peat forming processes in the Mfabeni peatland, Kwazulu Natal, South Africa since the Late Pleistocene

The Mfabeni peatland is the only known sub-tropical coastal fen that transcends the Last Glacial Maximum (LGM). This ca. 10m thick peat sequence provides a continuous sedimentation record spanning from the late Pleistocene to present (basal age c. 47kcalyr BP). We investigated the paleaeoenvironmental controls on peat formation and organic matter source input at the Mfabeni fen by: 1) exploring geochemical records (mass accumulation rate, total organic carbon, carbon accumulation rate, δC, δN and C/N ratio) to delineate primary production, organic matter source input, preservation and diagenetic processes, and 2) employ these geochemical signatures to reconstruct the palaeoenvironmental conditions and prevailing climate that drove carbon accumulation in the peatland. We established that the Mfabeni peat sediments have undergone minimal diagenetic alteration. The peat sequence was divided into 5 linear sedimentation rate (LSR) stages indicating distinct changes in climate and hydrological conditions: LSR stage 1 (c. 47 to c. 32.2kcalyr BP): predominantly cool and wet climate with C4 plant assemblages, interrupted by two short warming events. LSR stage 2 (c. 32.2 to c. 27.6kcalyr BP): dry and windy climate followed by a brief warm and wet period with increased C4 sedge swamp vegetation. LSR stage 3 (c. 27.6 to c. 20.3kcalyr BP): initial cool and wet period with prevailing C4 sedge plant assemblage until c. 23kcalyr BP; then an abrupt change to dry and cool glacial conditions and steady increases in C3 grasses. LSR stage 4 (c. 20.3 to c. 10.4kcalyr BP): continuation of cool and dry conditions and strong C3 grassland signature until c. 15kcalyr BP, after which precipitation increases. LSR stage 5 (c. 10.4kcalyr BP to present): characterised by extreme fluctuations between pervasive wet and warm to cool interglacial conditions with intermittent abrupt millennial-scale cooling/drying events and oscillations between C3 and C4 plant assemblages. In this study we reconstructed a high-resolution record of local hydrology, bulk plant assemblage and inferred climate since the Late Pleistocene, which suggest an anti-phase link between Southern African and the Northern Hemisphere, most notably during Heinrich (5 to 2) and Younger Dryas events. © 2013 Elsevier B.V.

Using geostatistical Bayesian Updating to integrate airborne radiometrics and soil geochemistry to improve mapping for mineral exploration

Mineral exploration programmes around the world use data from remote sensing, geophysics and direct sampling. On a regional scale, the combination of airborne geophysics and ground-based geochemical sampling can aid geological mapping and economic minerals exploration. The fact that airborne geophysical and traditional soil-sampling data are generated at different spatial resolutions means that they are not immediately comparable due to their different sampling density. Several geostatistical techniques, including indicator cokriging and collocated cokriging, can be used to integrate different types of data into a geostatistical model. With increasing numbers of variables the inference of the cross-covariance model required for cokriging can be demanding in terms of effort and computational time. In this paper a Gaussian-based Bayesian updating approach is applied to integrate airborne radiometric data and ground-sampled geochemical soil data to maximise information generated from the soil survey, to enable more accurate geological interpretation for the exploration and development of natural resources. The Bayesian updating technique decomposes the collocated estimate into a production of two models: prior and likelihood models. The prior model is built from primary information and the likelihood model is built from secondary information. The prior model is then updated with the likelihood model to build the final model. The approach allows multiple secondary variables to be simultaneously integrated into the mapping of the primary variable. The Bayesian updating approach is demonstrated using a case study from Northern Ireland where the history of mineral prospecting for precious and base metals dates from the 18th century. Vein-hosted, strata-bound and volcanogenic occurrences of mineralisation are found. The geostatistical technique was used to improve the resolution of soil geochemistry, collected one sample per 2 km2, by integrating more closely measured airborne geophysical data from the GSNI Tellus Survey, measured over a footprint of 65 x 200 m. The directly measured geochemistry data were considered as primary data in the Bayesian approach and the airborne radiometric data were used as secondary data. The approach produced more detailed updated maps and in particular maximized information on mapped estimates of zinc, copper and lead. Greater delineation of an elongated northwest/southeast trending zone in the updated maps strengthened the potential to investigate stratabound base metal deposits.

Soil-Geochemical Factors controlling the Distribution and Oral Bioaccessibility of Nickel, Vanadium and Chromium in Northern Ireland Soils

Geogenic nickel (Ni), vanadium (V) and chromium (Cr) are present at elevated levels in soils in Northern Ireland. Whilst Ni, V and Cr total soil concentrations share common geological origins, their respective levels of oral bioaccessibility are influenced by different soil-geochemical factors. Oral bioaccessibility extractions were carried out on 145 soil samples overlying 9 different bedrock types to measure the bioaccessible portions of Ni, V and Cr. Principal component analysis identified two components (PC1 and PC2) accounting for 69% of variance across 13 variables from the Northern Ireland Tellus Survey geochemical data. PC1 was associated with underlying basalt bedrock, higher bioaccessible Cr concentrations and lower Ni bioaccessibility. PC2 was associated with regional variance in soil chemistry and hosted factors accounting for higher Ni and V bioaccessibility. Eight per cent of total V was solubilised by gastric extraction on average across the study area. High median proportions of bioaccessible Ni were observed in soils overlying sedimentary rock types. Whilst Cr bioaccessible fractions were low (max = 5.4%), the highest measured bioaccessible Cr concentration reached 10.0 mg kg-1, explained by factors linked to PC1 including high total Cr concentrations in soils overlying basalt bedrock.

When are total concentrations not total? Factors affecting geochemical analytical techniques for measuring element concentrations in soil

Inductively coupled plasma (ICP) following aqua regia digestion and X-ray fluorescence (XRF) are both geochemical techniques used to determine ‘total’ concentrations of elements in soil. The aim of this study is to compare these techniques, identify elements for which inconsistencies occur and investigate why they arise. A study area (∼14,000 km2) with a variety of total concentration controls and a large geochemical dataset (n = 7950) was selected. Principal component analysis determined underlying variance in a dataset composed of both geogenic and anthropogenic elements. Where inconsistencies between the techniques were identified, further numerical and spatial analysis was completed. The techniques are more consistent for elements of geogenic sources and lead, whereas other elements of anthropogenic sources show less consistency within rural samples. XRF is affected by sample matrix, while the form of element affects ICP concentrations. Depending on their use in environmental studies, different outcomes would be expected from the techniques employed, suggesting the choice of analytical technique for geochemical analyses may be more critical than realised.

Tephra from andesitic Shiveluch volcano, Kamchatka, NW Pacific: chronology of explosive eruptions and geochemical fingerprinting of volcanic glass

The ~16-ka-long record of explosive eruptions from Shiveluch volcano (Kamchatka, NW Pacific) is refined using geochemical fingerprinting of tephra and radiocarbon ages. Volcanic glass from 77 prominent Holocene tephras and four Late Glacial tephra packages was analyzed by electron microprobe. Eruption ages were estimated using 113 radiocarbon dates for proximal tephra sequence. These radiocarbon dates were combined with 76 dates for regional Kamchatka marker tephra layers into a single Bayesian framework taking into account the stratigraphic ordering within and between the sites. As a result, we report ~1,700 high-quality glass analyses from Late Glacial–Holocene Shiveluch eruptions of known ages. These define the magmatic evolution of the volcano and provide a reference for correlations with distal fall deposits. Shiveluch tephras represent two major types of magmas, which have been feeding the volcano during the Late Glacial–Holocene time: Baidarny basaltic andesites and Young Shiveluch andesites. Baidarny tephras erupted mostly during the Late Glacial time (~16–12.8 ka BP) but persisted into the Holocene as subordinate admixture to the prevailing Young Shiveluch andesitic tephras (~12.7 ka BP–present). Baidarny basaltic andesite tephras have trachyandesite and trachydacite (SiO₂ < 71.5 wt%) glasses. The Young Shiveluch andesite tephras have rhyolitic glasses (SiO₂ > 71.5 wt%). Strongly calc-alkaline medium-K characteristics of Shiveluch volcanic glasses along with moderate Cl, CaO and low P₂O₅ contents permit reliable discrimination of Shiveluch tephras from the majority of other large Holocene tephras of Kamchatka. The Young Shiveluch glasses exhibit wave-like variations in SiO₂ contents through time that may reflect alternating periods of high and low frequency/volume of magma supply to deep magma reservoirs beneath the volcano. The compositional variability of Shiveluch glass allows geochemical fingerprinting of individual Shiveluch tephra layers which along with age estimates facilitates their use as a dating tool in paleovolcanological, paleoseismological, paleoenvironmental and archeological studies. Electronic tables accompanying this work offer a tool for statistical correlation of unknown tephras with proximal Shiveluch units taking into account sectors of actual tephra dispersal, eruption size and expected age. Several examples illustrate the effectiveness of the new database. The data are used to assign a few previously enigmatic wide-spread tephras to particular Shiveluch eruptions. Our finding of Shiveluch tephras in sediment cores in the Bering Sea at a distance of ~600 km from the source permits re-assessment of the maximum dispersal distances for Shiveluch tephras and provides links between terrestrial and marine paleoenvironmental records.

The single component geochemical map: Fact or fiction?

Single component geochemical maps are the most basic representation of spatial elemental distributions and commonly used in environmental and exploration geochemistry. However, the compositional nature of geochemical data imposes several limitations on how the data should be presented. The problems relate to the constant sum problem (closure), and the inherently multivariate relative information conveyed by compositional data. Well known is, for instance, the tendency of all heavy metals to show lower values in soils with significant contributions of diluting elements (e.g., the quartz dilution effect); or the contrary effect, apparent enrichment in many elements due to removal of potassium during weathering. The validity of classical single component maps is thus investigated, and reasonable alternatives that honour the compositional character of geochemical concentrations are presented. The first recommended such method relies on knowledge-driven log-ratios, chosen to highlight certain geochemical relations or to filter known artefacts (e.g. dilution with SiO2 or volatiles). This is similar to the classical normalisation approach to a single element. The second approach uses the (so called) log-contrasts, that employ suitable statistical methods (such as classification techniques, regression analysis, principal component analysis, clustering of variables, etc.) to extract potentially interesting geochemical summaries. The caution from this work is that if a compositional approach is not used, it becomes difficult to guarantee that any identified pattern, trend or anomaly is not an artefact of the constant sum constraint. In summary the authors recommend a chain of enquiry that involves searching for the appropriate statistical method that can answer the required geological or geochemical question whilst maintaining the integrity of the compositional nature of the data. The required log-ratio transformations should be applied followed by the chosen statistical method. Interpreting the results may require a closer working relationship between statisticians, data analysts and geochemists.

Compositional data-driven alternatives to single geochemical component maps

Conventional practice in Regional Geochemistry includes as a final step of any geochemical campaign the generation of a series of maps, to show the spatial distribution of each of the components considered. Such maps, though necessary, do not comply with the compositional, relative nature of the data, which unfortunately make any conclusion based on them sensitive
to spurious correlation problems. This is one of the reasons why these maps are never interpreted isolated. This contribution aims at gathering a series of statistical methods to produce individual maps of multiplicative combinations of components (logcontrasts), much in the flavor of equilibrium constants, which are designed on purpose to capture certain aspects of the data.
We distinguish between supervised and unsupervised methods, where the first require an external, non-compositional variable (besides the compositional geochemical information) available in an analogous training set. This external variable can be a quantity (soil density, collocated magnetics, collocated ratio of Th/U spectral gamma counts, proportion of clay particle fraction, etc) or a category (rock type, land use type, etc). In the supervised methods, a regression-like model between the external variable and the geochemical composition is derived in the training set, and then this model is mapped on the whole region. This case is illustrated with the Tellus dataset, covering Northern Ireland at a density of 1 soil sample per 2 square km, where we map the presence of blanket peat and the underlying geology. The unsupervised methods considered include principal components and principal balances
(Pawlowsky-Glahn et al., CoDaWork2013), i.e. logcontrasts of the data that are devised to capture very large variability or else be quasi-constant. Using the Tellus dataset again, it is found that geological features are highlighted by the quasi-constant ratios Hf/Nb and their ratio against SiO2; Rb/K2O and Zr/Na2O and the balance between these two groups of two variables; the balance of Al2O3 and TiO2 vs. MgO; or the balance of Cr, Ni and Co vs. V and Fe2O3. The largest variability appears to be related to the presence/absence of peat.