Daniel David Gregory

Mineral exploration methods are expensive and time-consuming, especially in recent times, where many near-surface deposits have been found and exploited. To overcome these challenges, new strategies must be developed. Here, we test whether the trace element chemistry of pyrrhotite changes systematically with distance from mineralization at the Sullivan deposit, British Columbia. If so, this could provide an additional tool to search for new ore bodies. Forty samples of the hanging wall, footwall, and mineralization hosting stratigraphy (host horizon) were collected from seven drill holes, both proximal and distal to the Sullivan deposit. These samples were analyzed using reflected light microscopy, an electron microprobe, and LA-ICPMS (laser ablation, inductively coupled plasma mass spectrometry). A total of three hundred and ninety LA-ICPMS analyses were used to build machine learning classifiers (cluster analysis and random forests) to determine whether an unknown pyrrhotite sample was from the mineralized horizon and, if so, whether it was proximal or distal to the mineralization. Our study found that the trace element abundance in pyrrhotite was higher in the footwall and hanging wall compared to the host horizon, and within the host horizon, was higher distal to the mineralization.

Shales and mudstones are fine-grained rocks formed in sedimentary basins throughout Earth’s history. These lithologies are increasingly important targets for mineral deposit exploration since they can have economic resources of critical minerals, including vanadium (V), an essential component of redox-flux batteries in solar cells. However, many Paleozoic, shale-hosted V deposits are metamorphosed and deformed. This commonly obscures primary features, including V-bearing host phases and the original composition of organic material. In this study, we present geochemical and mineralogical data from the Paleozoic Van Property deposit, Northwest Territories, Canada, to show that V can be released from organic matter during metamorphism and incorporated in clay phases such as illite. The siliceous argillites at the Van Property host up to 0.69% V2O5 and were metamorphosed to (sub-)greenschist facies. Their mineralogy is dominated by quartz with minor graphite, illite, muscovite, pyrite, sphalerite, rutile, and carbonates. Although some illite (i.e., high-V illite) can have up to 13 wt% V2O3 and rutile can have up to 4.4 wt% V2O3, mass-balance calculations are insufficient to explain V enrichment at the Van Property utilizing only illite and rutile. The third V host is inferred to be carbonaceous matter in which V accumulated syn-deposition on the seafloor. Subsequent metamorphism led to the demetallation of V-bearing geoporphyrins and the release of vanadyl ions (VO2+), some of which were then incorporated into high-V illite and rutile. This process of V enrichment highlights the role of organic matter in scavenging V from superficial reservoirs and the importance of metamorphism on subsequent V release and its incorporation into inorganic phases. The geochemistry of siliceous, V-rich argillites at the Van Property is also compared to other V-enriched shale and mudstone deposits, highlighting the diversity of shale-hosted V deposits and emphasizing the need for further research to close the knowledge gaps related to variations in composition, mineralogy, and V enrichment processes.

Anomalous high grade Au mineralization has been found in float material at the Drogo Prospect, Nunavut, Canada. However, traditional exploration methods have thus far been unable to find the bedrock source of the mineralization. Recent studies have shown that pyrite trace element chemistry, as determined using LA-ICP-MS, varies between Au-forming and barren fluids. To test whether this also occurred at the Drogo Prospect, we analyzed pyrite in rock samples, including Au-rich samples, moderate Au grade samples, and barren samples. However, little variation in pyrite trace element chemistry was observed, suggesting that pyrite trace element chemistry may not be an effective exploration tool at the Drogo Prospect. To confirm these observations, cluster analysis was used to determine if natural clustering of the data could highlight which samples were more likely to be Au rich; again, no statistical differences could be found between the samples. In contrast to our working hypothesis, this suggests that pyrite trace element chemistry is not an effective exploration tool to find the bedrock source of the Drogo boulder trend.

Chlorite has long been considered a mineral group likely to have different trace element chemistry with proximity to mineralization, and therefore can be used to vector towards ore bodies. However, due to their geochemical complexity, it has proven challenging to develop a simple vectoring method based on the variation in abundance of one or a few chemical elements or isotopes. Machine learning, specifically cluster analysis, provides a potential mathematical tool for characterizing multidimensional geochemical correlations with proximity to mineralization. In this contribution we conducted a cluster analysis on 23 elements from 1,679 distinct chlorite sample analyses. The combination of this clustering technique with classification by proximity to the ore body, 1) explores and characterizes the nature of chlorite composition and proximity to ore bodies and 2) tests the efficacy of clustering-classification methods to predict whether a chlorite sample is near to an ore body. We found that chlorite chemistry is more strongly controlled by deposit type than proximity to mineralization and that cluster analysis of chlorite trace element content is likely not a viable way to develop vectors towards porphyry mineralization.

When laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analyses of pyrite plot below the gold solubility line on a gold vs. arsenic plot and have relatively flat counts on laser ablation time-resolved output graphs, it is often interpreted that the gold is held within the pyrite structure. The study by Ehrig et al. (2023, this issue) shows, using a combination of LA-ICP-MS spot analyses of gold in pyrite, transmission electron microscopy, and electron backscatter diffraction that this is not necessarily the case. Furthermore, they use these same techniques to identify how trace elements, including gold, are remobilized in pyrite during deformation and metamorphism.

The rare earth elements and yttrium (REY)-enriched phosphorites in the Zhijin region, southwest China, have attracted much attention, yet its origin has not been sufficiently addressed. The geology, mineralogy and geochemistry of samples from four sections of the early Cambrian age from the Zhijin region were studied to attain the redox condition and origin of REY. Data from in situ analysis show that REY are mainly contained in francolite, and the two types of francolite (bioclastic and granular) have no distinct difference in REY content (ΣREY). A Ce anomaly indicates an oxic condition in primary seawater. An Eu anomaly and ratios of redox sensitive elements (RSEs) indicate a fluctuating redox condition during phosphorite deposition. Shale-normalized REY patterns of whole rock and francolite show a hat-shaped pattern, indicating the effect of diagenetic processes. The Y/Ho ratio of the selected sections ranges from 38.13 to 61.93, and together with the Y anomaly, La_N/Nd_N ratio, La_N/Sm_N ratio, and La_N/Yb_N ratio, this indicates a seawater origin. This is supported by the Ce/Ce* of the phosphorite that ranges from 0.32 to 0.52 and the Eu/Eu* that ranges from 0.88 to 1.82, which is similar to the characteristics of seawater and deep-sea mud. We also propose a minor contribution of terrigenous debris and influence of diagenesis and hydrothermal processes.

Porphyry Cu-Mo deposits are among the world’s largest source of Cu, Mo, and Re, and are also an important source of other trace elements, such as Au and Ag. Despite the fact that chalcopyrite, bornite, and pyrite are the most common sulfides in this deposit type, their trace element content remains poorly constrained. In particular, little is known about minor and trace elements partitioning into Cu-(Fe) sulfides as a function of temperature and pH of the hydrothermal fluid. In this study, we report a comprehensive geochemical database of chalcopyrite, bornite, and pyrite in the super-giant Chuquicamata porphyry Cu-Mo deposit in northern Chile. The aim of our study, focused on the new Chuquicamata Underground mine, was to evaluate the trace element composition of each sulfide from the different hydrothermal alteration assemblages in the deposit. Our approach combines the electron microprobe analysis (EMPA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) of sulfide minerals obtained from six representative drill cores that crosscut the chloritic (propylitic), background potassic, intense potassic, and quartz-sericite (phyllic) alteration zones. Microanalytical results show that chalcopyrite, bornite, and pyrite contain several trace elements, and the concentration varies significantly between hydrothermal alteration assemblages. Chalcopyrite, for example, is a host of Se (≤22,000 ppm), Pb (≤83.00 ppm), Sn (≤68.20 ppm), Ag (≤45.1 ppm), Bi (≤25.9 ppm), and In (≤22.8 ppm). Higher concentrations of Se, In, Pb, and Sn in chalcopyrite are related to the high temperature background potassic alteration, whereas lower concentrations of these elements are associated with the lower temperature alteration types: quartz-sericite and chloritic. Bornite, on the other hand, is only observed in the intense and background potassic alteration zones and is a significant host of Ag (≤752 ppm) and Bi (≤2960 ppm). Higher concentrations of Ag and Sn in bornite are associated with the intense potassic alteration, whereas lower concentrations of those two elements are observed in the background potassic alteration. Among all of the sulfide minerals analyzed, pyrite is the most significant host of trace elements, with significant concentrations of Co (≤1530 ppm), Ni (≤960 ppm), Cu (≤9700 ppm), and Ag (≤450 ppm). Co, Ni, Ag, and Cu concentration in pyrite vary with alteration: higher Ag and Cu concentrations are related to the high temperature background potassic alteration. The highest Co contents are associated with lower temperature alteration types (e.g., chloritic). These data indicate that the trace element concentration of chalcopyrite, bornite, and pyrite changed as a function of hydrothermal alteration is controlled by several factors, including temperature, pH, fO₂, fS₂, and the presence of co-crystallizing phases. Overall, our results provide new information on how trace element partitioning into sulfides relates to the main hydrothermal and mineralization events controlling the elemental budget at Chuquicamata. In particular, our data show that elemental ratios in chalcopyrite (e.g., Se/In) and, most importantly, pyrite (e.g., Ag/Co and Co/Cu) bear the potential for vectoring towards porphyry mineralization and higher Cu resources.

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). The Random Forests classification models for pyrite trace element data can be used as a predictive modeling tool in greenfield terrains by providing an accurate indication of ore deposit type. This advance will assist mineral explorers by allowing early implementation of predictive ore deposit models when prospecting for ore deposits. Furthermore, the ability of the classifier to accurately identify pyrite of sedimentary origin will allow researchers interested in paleoenvironmental conditions of ancient oceans to effectively screen prospective samples that are affected by a hydrothermal overprint.