Faced with ongoing depletion of near-surface ore deposits, geologists are increasingly required to explore for deep deposits or those lying beneath surface cover. The result is increased drilling costs and a need to maximize the value of the drill hole samples collected. Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis of pyrite is one tool that is showing promise in deep exploration. Since the trace element content of pyrite approximates the composition of the fluid from which it precipitated and the crystallization mechanism, the trace element characteristics can be used to predict the type of deposit with which a pyritic sample is associated. This possibility, however, is complicated by overlapping trace element abundances for many deposit types. The solution lies with simultaneous comparison of multiple trace elements through rigorous statistical analysis. Specifically, we used LA-ICP-MS pyrite trace element data and Random Forests, an ensemble machine learning supervised classifier, to distinguish barren sedimentary pyrite and five ore deposit categories: iron oxide copper-gold (IOCG), orogenic Au, porphyry Cu, sedimentary exhalative (SEDEX), and volcanic-hosted massive sulfide (VHMS) deposits. The preferred classifier utilizes in situ Co, Ni, Cu, Zn, As, Mo, Ag, Sb, Te, Tl, and Pb measurements to train the Random Forests. Testing of the Random Forests classifier using additional data from the same deposits and sedimentary basins (test data set) yielded an overall accuracy of 91.4% (94.9% for IOCG, 78.8% for orogenic Au, 81.1% for porphyry Cu, 93.6% for SEDEX, 97.2% for sedimentary pyrite, 91.8% for VHMS). Similarly, testing of the Random Forests classifier using data from deposits and sedimentary basins that did not have analyses in the training data set yielded an overall accuracy of 88.0% (81.4% for orogenic Au, 95.5% for SEDEX, 90.0% for sedimentary pyrite, 73.9% for VHMS; insufficient data was available to perform a blind test on porphyry Cu and IOCG). The performance of the classifier was further improved by instituting criteria (at least 40% of total votes from the Random Forests needed for a conclusive identification) to remove uncertain or inconclusive classifications, increasing the classifier’s accuracy to 94.5% for the test data (94.6% for IOCG, 85.8% for orogenic Au, 87.8% for porphyry Cu, 95.4% for SEDEX, 98.5% for sedimentary pyrite, 94.6% for VHMS) and 93.9% for the blind test data (85.5% for orogenic Au, 96.9% for SEDEX, 96.7% for sedimentary pyrite, 84.6% for VHMS).
Detrital zircon grains preserved within clasts and the matrix of a basal diamictite sequence directly overlying the Carrapateena IOCG deposit in the Gawler Craton, South Australia are shown here to preserve U–Pb ages and geochemical signatures that can be related to underlying mineralisation. The zircon geochemical signature is characterised by elevated heavy rare-earth element fractionation values (GdN/YbN ≥ 0.15) and high Eu ratios (Eu/Eu* ≥ 0.6). This geochemical signature has previously been recognised within zircon derived from within the Carrapateena orebody and can be used to distinguish zircon associated with IOCG mineralisation from background zircon preserved within stratigraphically equivalent regionally unaltered and altered samples. The results demonstrate that zircon chemistry is preserved through processes of weathering, erosion, transport, and incorporation into cover sequence materials and, therefore, may be dispersed within the cover sequence, effectively increasing the geochemical footprint of the IOCG mineralisation. The zircon geochemical criteria have potential to be applied to whole-rock geochemical data for the cover sequence diamictite in the Carrapateena area; however, this requires understanding of the presence of minerals that may influence the HREE fractionation (GdN/YbN) and/or Eu/Eu* results (e.g., xenotime, feldspar).
Geochemical data are frequently collected from mineral exploration drill-hole samples to more accurately define and characterise the geological units intersected by the drill hole. However, large multi-element data sets are slow and challenging to interpret without using some form of automated analysis, such as mathematical, statistical or machine learning techniques. Automated analysis techniques also have the advantage in that they are repeatable and can provide consistent results, even for very large data sets. In this paper, an automated litho-geochemical interpretation workflow is demonstrated, which includes data exploration and data preparation using appropriate compositional data-analysis techniques. Multiscale analysis using a modified wavelet tessellation has been applied to the data to provide coherent geological domains. Unsupervised machine learning (clustering) has been used to provide a first-pass classification. The results are compared with the detailed geologist's logs. The comparison shows how the integration of automated analysis of geochemical data can be used to enhance traditional geological logging and demonstrates the identification of new geological units from the automated litho-geochemical logging that were not apparent from visual logging but are geochemically distinct.KEY POINTSTo reduce computational complexity and facilitate interpretation, a subset of geochemical elements is selected, and then a centred log-ratio transform is applied.The wavelet tessellation method is used to domain the drill holes into rock units at a range of scales.Several clustering methods were tested to identify distinct rock units in the samples and multiscale domains for classification.Results are compared with geologist's logs to assess how geochemical data analysis can inform and improve traditional geology logs.
Abstract Porphyry-style hydrothermal alteration has long been recognized in the Delamerian Orogen, Southeastern Australia. However, the fertility of porphyry prospects in this belt, including the Anabama Hill, remains elusive, due to intermittent exploration activities and sparse exposure. Recent significant discoveries of porphyry-epithermal Cu-Au deposits in the adjacent Stavely Arc have led to renewed exploration interest. Reinvestigation of the Anabama Hill drill cores highlights that K-feldspar-rich and epidote-chlorite-dominated alterations are superimposed by extensive quartz-pyrite ± chalcopyrite ± molybdenite veins with white mica-quartz selvedges, related to early-middle Ordovician granitic stocks. Granodiorite and diorite hosts have diagnostic geochemical characteristics, including high Sr/Y, V/Sc ratios, and listric-shaped REE trends, implying amphibole-leading fractionation due to high water contents in primitive melts. LA-ICP-MS analyses show that characteristic element compositions, e.g., high Fe, Sr, Pb, U and Bi and low Mg and REEs in the Anabama Hill epidote, and high Mn, Zn, Zr and U and low Ca, Ba and Pb in the chlorite, suggest the two minerals resulting from propylitic alteration rather than metamorphism. Compared to well-mineralized porphyry deposits, the epidote shows high Bi, Cu, Sr, Ti, Zr and U, and the chlorite is high in Ti/Sr and Al/Si ratios, implying that they are most likely deposit-proximal or near a heat center. This is supported by intermediate to high temperatures of 200—420°C calculated by chlorite geothermometer. Propylitic epidote and chlorite outside pyrite halos typically define geochemical shoulders by anomalous As-Sb and Mn-Zn highs, 1—1.5 km away from the mineralized centers. Given most of the epidote and chlorite intergrown with sulfides, their close proximity to a likely mineralized center accounts for low to moderate concentrations of distal pathfinder elements and subdued performances on the As-Sb and Mn-Zn fertility plots. Combined with bulk-rock results, proximal-fertility indicators recorded in epidote and chlorite provide encouraging implications for porphyry exploration in the Delamerian belt.
AbstractSedimentary uranium mineralisation in South Australia is mostly in Cainozoic basinal palaeochannels developed on or proximal to Precambrian cratons. Precambrian basement of the Gawler and Curnamona cratons have uranium contents in the range 10-100 ppm, well above the crustal average of 2.8 ppm U. Deeply weathered basement rocks in these regions were incised during early Tertiary times and the sediments in these palaeodrainage networks now form several significant uranium provinces. The palaeochannel uranium deposits are typically hosted by medium to coarse-grained sandstone deposited in a continental fluvial, latchstring, alluvial, or marginal marine sedimentary environment. Impermeable clay units are intercalated in the sedimentary sequence and often occur immediately above and below the mineralised sandstone. Uranium mineralisation is associated with reduced conditions, and similar to elsewhere in the world, the host sediments to the Tertiary uranium mineralisation often contain pyrite and organic (plant) matter that is either disseminated or forms lignite seams. Tabular or roll-front shaped mineralised bodies formed along the contact with clay horizons and also along the palaeochannel margins. Tertiary continental sediments in SA are important favourable hosts because of the high organic content in channel sediments due to widespread colonisation by land plants during this time.The precise geometric definition of a palaeochannel is important in the selection of exploration targets for sandstone uranium. This often requires the integration of various geoscientific data sets in order to define targets and to improve the effectiveness of drilling. Refinements in remote sensing and geophysical techniques, data processing, sedimentology and computer-aided interpretations provide an effective, economic and efficient method of outlining the principal drainage patterns and channel dimensions. An improved understanding of sedimentary models and their relationship to uranium distribution will assist the effectiveness of exploration industry to explore for uranium in buried channel systems.Keywordspalaeochanneluranium.
Abstract To evaluate the fertility of porphyry mineralization in the Delamerian Orogen (South Australia), zircon and apatite from four prospects, including Anabama Hill, Netley Hill, Bendigo, and Colebatch, have been analyzed by LA-ICP-MS and electron microprobe. The zircon is characterized by heavy REEs enrichment relative to light REEs, high (Ce/Nd) N (1.3–45), and weak to moderate negative Eu/Eu* (0.2–0.78). The apatite has right-sloped REE patterns with variably negative to positive Eu anomalies. Low Mg (< 670 ppm) and Sr/Y ratios (< 5) in apatite likely illustrate fractional crystallization trends for the granitic melts in shallow crust. The Yb/Gb and Eu/Eu* in zircon reveal that intrusions at Anabama Hill, Netley Hill, and Bendigo underwent fractional crystallization controlled by amphibole (< 50–60%), garnet (< 15%), apatite (< 0.6%), and/or titanite (< 0.3%). These stocks have average f O 2 values reported relative to fayalite-magnetite-quartz buffer (ΔFMQ), from 0.7 ± 0.9 to 2.1 ± 0.4, ascribed to prolonged magmatic evolution or sulfur degassing during post-subduction processes. Our data imply that both Anabama and Bendigo complexes experienced prevalent (garnet-) amphibole crystallization from hydrous melts that have moderately high oxidation (ΔFMQ + 1 to + 3) and elevated sulfur-chlorine components (Anabama, 37 ± 9 to 134 ± 83 ppm S and 0.30 ± 0.24 to 0.64 ± 0.89 wt% Cl; Bendigo, 281 ± 178 to 909 ± 474 ppm S and 0.45 ± 0.47 to 3.01 ± 1.54 wt% Cl). These are crucial ingredients to form porphyry Cu–Mo ± Au ores with economic significance, which provides encouragement for mineral exploration in this orogen.
ABSTRACT An increasing requirement for the discovery of new mineral deposits is the ability to detect mineralization through thick cover. Large areas of the southern Australian Curnamona Province are prospective for Pb–Zn and Cu–Au mineralization; however, much of the prospective basement is covered by 10–150 m of Cenozoic fluvial, alluvial and lacustrine sediments. Soil surveys were conducted at the Kalkaroo Cu–Au–Mo deposit and Polygonum multi-element prospect. The major challenge to exploration at both sites is the 40–150 m thickness of transported cover. The objective was to determine whether any surface geochemical method could be used to detect mineralization under such conditions. The range of methods examined included those where some success was indicated in previous surveys in the region. Soil samples were collected from 10–25 cm depth and from the zone of high soil moisture loss as indicated by the presence of secondary calcium carbonate and sulphate (60–300 cm). Samples were treated using aqua regia and the partial extractants weak cyanide, sodium hydroxide, magnesium chloride and the proprietary method MMI-M. Conductivity and pH measurements were made on all samples. A Chinese variant of the electrochemical CHIM technique was also tested over the Kalkaroo deposit. A consistent response to the mineralized zone at the Kalkaroo deposit is double-peak anomalies for Mo. Less regular double-peak anomalies are also present for U and Au, and for soil conductivity. The two survey lines with electrochemical CHIM results show mainly high-contrast, one-point anomalies over mineralization. The significance of these is still to be established. Soils collected over the Kalkaroo and Polygonum prospects contain Ag concentrations that vary coincidently with changes in underlying basement lithology. Relatively high Ag concentrations in soils over the Polygonum prospect show a spatial relationship to underlying mineralized zones. Although no single technique used in this study was identified as a reliable exploration tool, survey results add support to the contention that partial extraction geochemistry of soils in the region may reflect mineralization and/or underlying bedrock trace metal content through as much as 150 m of transported cover.
When building 3D models of the subsurface, reconciling several geological and geophysical data of diverse nature, resolutions, coverage, or sensitivity, is challenging, both numerically and petrophysically. In this work, we propose a workflow for mapping selected geological features and characterise their uncertainty using a Bayesian Estimate Fusion algorithm. Different datasets such as 1D probabilistic models derived from geophysical data, drillholes and geological data are combined to produce probabilistic maps of selected geological boundaries, relying on petrophysical and geological assumptions. Leveraging large, high-quality geophysical datasets acquired in the eastern Gawler Craton in South Australia, we demonstrate the applicability of our approach with two examples: (1) we map in 3D the top of a stratigraphic unit in the cover, the Tregolana Shale, using 1D magnetotelluric (MT) and 1D Airborne Electromagnetic (AEM) probabilistic models, drill holes and surface geology; (2) we map the depth to basement using 1D probabilistic MT models, drill holes and interpreted structural information. Our results show that the different resolution, data sampling, depth of investigation and reliability of the utilised datasets can be combined in a complementary fashion, overcoming their respective limitations, to find solutions/models that satisfy all the datasets. We show that probabilistic workflows permit characterisation and reduce uncertainty when mapping the location of features of interest, but also permit the testing of geological hypotheses against other geophysical and geological data. These types of models are valuable to better characterise, interpret, and conceptualise the subsurface, enabling better exploration targeting and supporting efforts to discover new mineral deposits.
SummaryThis contribution presents a method for efficiently classifying geophysical anomalies and identifying regions and features that share characteristics of many known iron-oxide-copper-gold (IOCG) deposits of the Gawler Craton, and can therefore be used in drill target prioritization. Residual Bouguer gravity and reduced-to-pole total magnetic intensity grids over the Gawler Craton were transformed, generating polygon datasets representing populations of locally anomalous gravity and magnetic intensity. Taken as simple anomaly polygons, there are a very large number of features across the Gawler Craton (>39,000 TMI and >10,000 gravity). Superimposing mineral deposits over these features shows a clear spatial correlation between IOCG deposits and occurrences, and anomalies (>90% of deposits within 1,000 m of an anomaly), but leaves thousands of anomalies of varying magnitudes that cannot all be related to IOCG mineralization. Eliminating TMI and gravity anomalies with a separation of more than 1,000 m reduced the search space to ~20,000 TMI features and ~8,500 gravity features. Limiting the search to a statistically derived gravity threshold ≥0.4 mGal gravity anomalies, the exploration space is reduced to 798 gravity features with coincident TMI features within the Olympic Copper-Gold Province. The Anselin Local Morans I method was used to delineate geographic regions based upon spatial clustering of high magnitude anomalies. The spatial distribution and clustering characteristics of the gravity anomalies provide additional information and can be related to differing basement geology and deposit style. Terranes where lithologies and Cu-Au occurrences are commonly magnetite-rich show clustered high-magnitude gravity anomalies, and correlated spatially with the Mount Woods and Moonta domains within the eastern Gawler Craton. Importantly, it was found that the central, and currently most endowed, the Olympic Domain, was distinct in that it was dominated by spatial outliers (discrete high-magnitude density features).These results could be used as a starting point in developing IOCG exploration strategies, due to the high number of additional untested, spatially coincident gravity and magnetic anomalies that warrant further investigation.
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