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For this study, we generated thallium (Tl) isotope records from two anoxic basins to track the earliest changes in global bottom water oxygen contents over the Toarcian Oceanic Anoxic Event (T-OAE; ∼183 Ma) of the Early Jurassic. The T-OAE, like other Mesozoic OAEs, has been interpreted as an expansion of marine oxygen depletion based on indirect methods such as organic-rich facies, carbon isotope excursions, and biological turnover. Our Tl isotope data, however, reveal explicit evidence for earlier global marine deoxygenation of ocean water, some 600 ka before the classically defined T-OAE. This antecedent deoxygenation occurs at the Pliensbachian/Toarcian boundary and is coeval with the onset of initial large igneous province (LIP) volcanism and the initiation of a marine mass extinction. Thallium isotopes are also perturbed during the T-OAE interval, as defined by carbon isotopes, reflecting a second deoxygenation event that coincides with the acme of elevated marine mass extinctions and the main phase of LIP volcanism. This suggests that the duration of widespread anoxic bottom waters was at least 1 million years in duration and spanned early to middle Toarcian time. Thus, the Tl data reveal a more nuanced record of marine oxygen depletion and its links to biological change during a period of climatic warming in Earth’s past and highlight the role of oxygen depletion on past biological evolution.

Simultaneous comparison of multiple trace elements through statistical analysis has been carried out using data from LA-ICP-MS pyrite trace element analysis 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 sulphide (VHMS) deposits. The preferred classifier utilises 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% while testing using data from deposits and sedimentary basins that did not have analyses in the training data set yielded an overall accuracy of 88.0%. The performance of the classifier was further improved by instituting criteria to remove uncertain or inconclusive classifications, increasing the classifier’s accuracy to 94.5% for the test data and 93.9% for the blind test data. It is concluded that the Random Forests classification models for pyrite trace element data can be used for predictive modelling in greenfield terrains by providing an accurate indication of ore deposit type. The classifier will also be useful for palaeoenvironmental studies as it can accurately identify pyrite of sedimentary origin and so screen prospective samples that are affected by a hydrothermal overprint., Simultaneous comparison of multiple trace elements through statistical analysis has been carried out using data from LA-ICP-MS pyrite trace element analysis 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 sulphide (VHMS) deposits. The preferred classifier utilises 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% while testing using data from deposits and sedimentary basins that did not have analyses in the training data set yielded an overall accuracy of 88.0%. The performance of the classifier was further improved by instituting criteria to remove uncertain or inconclusive classifications, increasing the classifier’s accuracy to 94.5% for the test data and 93.9% for the blind test data. It is concluded that the Random Forests classification models for pyrite trace element data can be used for predictive modelling in greenfield terrains by providing an accurate indication of ore deposit type. The classifier will also be useful for palaeoenvironmental studies as it can accurately identify pyrite of sedimentary origin and so screen prospective samples that are affected by a hydrothermal overprint.

Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis of 1 407 sedimentary (diagenetic and syngenetic) pyrites from 45 carbonaceous shale and unconsolidated sulphidic sediment samples, ranging in age from Palaeoarchaean to present day, show a considerable range of trace element compositions. As, Ni, Pb, Cu, and Co are among the most abundant trace elements, with medians ranging from 100 to 1 000 ppm. Less abundant elements Mo, Sb, Zn, and Se have median ranges of 10 to 100 ppm, and Ag, Bi, Te, Cd, and Au have median ranges of 0.01 to 10 ppm. The dataset reveals three main groups of trace elements that are incorporated into pyrite in different ways. Group 1 elements (As, Ni, Co, Sb, Se, and Mo) are contained uniformly throughout the pyrite and may be held within the pyrite crystal structure or as nanoinclusions evenly distributed within pyrite. Group 2 elements (Bi, Pb, Ag, Au, Te, and Cu) generally occur uniformly at low concentrations and may be incorporated into the pyrite structure but are highly variable at high concentrations, where they may also occur as microinclusions. Group 3 elements (Zn and Cd) tend to have highly variable abundances and generally occur in pyrite as microinclusions of sphalerite. Factor analysis of the dataset identified five factors that account for 65.4% of the variance in pyrite trace element concentrations. Variations in pyrite texture do not show consistent compositional patterns between different samples, though within the same sample later formed pyrite tends to have lower trace element abundance., Laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) analysis of 1 407 sedimentary (diagenetic and syngenetic) pyrites from 45 carbonaceous shale and unconsolidated sulphidic sediment samples, ranging in age from Palaeoarchaean to present day, show a considerable range of trace element compositions. As, Ni, Pb, Cu, and Co are among the most abundant trace elements, with medians ranging from 100 to 1 000 ppm. Less abundant elements Mo, Sb, Zn, and Se have median ranges of 10 to 100 ppm, and Ag, Bi, Te, Cd, and Au have median ranges of 0.01 to 10 ppm. The dataset reveals three main groups of trace elements that are incorporated into pyrite in different ways. Group 1 elements (As, Ni, Co, Sb, Se, and Mo) are contained uniformly throughout the pyrite and may be held within the pyrite crystal structure or as nanoinclusions evenly distributed within pyrite. Group 2 elements (Bi, Pb, Ag, Au, Te, and Cu) generally occur uniformly at low concentrations and may be incorporated into the pyrite structure but are highly variable at high concentrations, where they may also occur as microinclusions. Group 3 elements (Zn and Cd) tend to have highly variable abundances and generally occur in pyrite as microinclusions of sphalerite. Factor analysis of the dataset identified five factors that account for 65.4% of the variance in pyrite trace element concentrations. Variations in pyrite texture do not show consistent compositional patterns between different samples, though within the same sample later formed pyrite tends to have lower trace element abundance.