Mineral Exploration Research Articles

Research on numerical simulation method for 3D marine direct current resistivity

As one of the most important methods in marine electromagnetic exploration, the marine direct current (DC) resistivity method has been widely used in the exploration of marine minerals, oil and gas, as well as in large-scale engineered geological exploration and other fields. Efficient and high-precision forward algorithms are the foundation for an accurate quantitative interpretation of the marine DC resistivity method. We have implemented an efficient and high-precision forward modeling approach for 3D marine DC resistivity. First, the wave number domain anomalous potential control equations are obtained by performing a 2D Fourier transform along the horizontal direction based on the differential control equations satisfied by the marine DC anomalous potential. Instead of directly solving the 3D numerical simulation problem, we transform the 3D numerical simulation problem into multiple 1D numerical simulation problems in the wavenumber domain by dimensionality reduction for computational efficiency. Second, the boundary conditions of the governing equations are given to obtain the corresponding boundary value problems, and the anomalous potential is solved using the 1D finite element method in the wavenumber domain. Next, we perform a 2D inverse Fourier transform on the wave-number domain anomalous potential to obtain the spatial domain anomalous potential. Furthermore, compact operators are used to iteratively modify the potential and obtain high-precision numerical solutions. Finally, we demonstrate the correctness of the proposed algorithm’s solution strategy by using a hierarchical ocean model, and the efficiency of the proposed algorithm by using a spherical model.

Unsupervised Learning-Based Exploration of Urban Rail Transit Passenger Flow and Travel Pattern Mining

This study delves into the realm of urban rail transit systems, leveraging unsupervised learning techniques to analyze passenger flow characteristics and unearth travel patterns. Focused on the dynamic and complex nature of urban rail networks, the research utilizes extensive datasets, primarily derived from Automated Fare Collection (AFC) systems, to provide a comprehensive analysis of passenger behaviors and movement trends. Employing advanced algorithms like DBSCAN, the study categorizes passengers into distinct groups, including tourists, shoppers, thieves, commuters, and station staff. These classifications reveal intricate patterns in travel behaviors, significantly contributing to a deeper understanding of urban transit dynamics. The findings offer valuable insights into peak travel times, popular routes, and station congestion, highlighting potential areas for operational improvements and infrastructure development. The study’s application of unsupervised learning in analyzing vast, unstructured data sets a precedent in urban transportation research, showcasing the potential of artificial intelligence in enhancing the efficiency and sustainability of urban transit systems. The insights garnered are pivotal not only for optimizing current operations but also for shaping future expansion and adaptation strategies, ensuring urban rail systems continue to meet the evolving needs of growing urban populations.

Research on slim-hole drilling technology for shale gas geological survey in China

Over the last 10 years, the China Geological Survey has deployed 137 slim-hole shale gas geological exploration wells for coring entire wellbores. These wells are primarily located in new blocks and geological formations where neighboring well data are insufficient, beyond the scope of developed oil fields in China, or outside of oil and gas company mining-right areas. The drilling rig equipment, coring tools, and core drill bits of slim-hole shale gas drilling technology are different from those associated with traditional petroleum drilling. Many studies have been conducted on non-coring slim-hole drilling technology. This paper focuses on coring technology and drilling safety, summarizing a set of high-efficiency shale gas drilling equipment and technology systems based on geological drilling equipment and techniques (that can be used for solid mineral exploration). We report on: 1) an improved vertical shaft drilling rig adapted to shale gas well control safety; 2) high-efficiency core drilling techniques, focusing on coring tools, and techniques incorporating an inverted tower drilling tool combination, air circulation follow-through technology, and expanded casing technology; 3) research progress on high-efficiency core drill bits, including non-planar tooth polycrystalline diamond compact bits and impregnated diamond core bits, along with their application effects. This research provides substantial advances in drill-core technology and improvements in exploration efficiency. Moreover, it provides a reference frame for well structural design and selection of construction technology for shale gas exploration drilling projects.

Modern Lines in Geochemistry: Anniversary Conference

Abstract —On 21–25 November, 2022, Vinogradov Institute of Geochemistry, Siberian Branch of the Russian Academy of Sciences (Irkutsk), organized and held an All-Russian conference celebrating the 65th anniversary since the foundation of the Institute and the 105th anniversary since the birth of its first director, Academician Lev Vladimirovich Tauson, who headed the Institute from 1961 to 1989. The results reported at the conference encompass a wide range of research fields in modern geochemistry, including isotope geochemistry of igneous, metamorphic, and sedimentary rocks in various geodynamic settings; chemistry of ore-magmatic systems and modern methods of mineral exploration; environmental geochemistry, geoecology, and paleoclimate; laboratory modeling and thermodynamic calculations of natural and production-related processes and materials; advanced analytical methods and information technologies for geosciences. The conference presentations pay tribute to Lev Tauson whose academic carrier, as well as all creative activity, had been closely related with the development of the Institute of Geochemistry. The preface paper provides a review of topics discussed at the conference concerning various geodynamic and geochemical problems, including sources of material, petrogenesis, and metallogeny.

Apatite Chemistry as a Petrogenetic Indicator for Mafic Layered Intrusions

Abstract Mafic layered intrusions constitute a natural laboratory to investigate petrogenetic processes using trace element variations in apatite chemistry. Although these intrusions are related to large igneous provinces, there is a wide range of parameters that can affect the chemistry of the primary melt (i.e. composition of the source, pressure, temperature, oxygen fugacity), followed by possible crustal contamination. In this study, we use a comprehensive dataset of analyses of cumulus and intercumulus apatite from a variety of mafic layered intrusions to demonstrate the use of apatite as a powerful petrogenetic indicator. The dataset (determined in this study and compiled from the literature) comprises electron microprobe and LA-ICP-MS analyses, as well as in-situ LA-MC-ICP-MS analyses of Sr isotopes in apatite from well documented layered intrusions (Sept-Iles, Skaergaard, Bushveld, Panzhihua) and the Sudbury Igneous Complex. For the first time, we show that high values of (La/Nd)N, Th, U, Pb, and As in apatite correlate with high (87Sr/86Sr)initial and are related to contamination with continental crust. An elevated (Gd/Yb)N ratio might indicate melting of a mantle source deep enough to retain Yb in garnet. We also confirm that increasingly negative Eu anomaly and decreasing Sr/Y ratio in apatite are indicators of fractional crystallisation of plagioclase, and that high Sr/Y is indicative of early saturation of apatite and/or delayed crystallisation of plagioclase. The reversal to more primitive compositions caused by magma mixing is expressed by higher Sr, V, Mg and Sr/Y ratio, and lower REE + Y, As and Na concentrations in apatite following magma replenishment. Lastly, we show that apatite signature can efficiently distinguish a mafic from a felsic intrusion using its REE and Sr content coupled to its Eu anomaly. It is also possible to further identify the more primitive from the more evolved parts of a mafic layered intrusion, using the Lu, Th, V and volatile (F/Cl) content to distinguish intercumulus from cumulus apatite, respectively. Finally, identifying a mafic magmatic system using detrital apatite in till will prove useful for provenance and mineral exploration studies.

Denoising of Geochemical Data using Deep Learning–Implications for Regional Surveys

Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern surveys are typically designed to permit quantification of data uncertainty through data quality metrics by using quality assurance and quality control (QA/QC) methods. However, these metrics, such as data accuracy and precision, are obtained through the data generation phase. Consequently, it is unclear how residual uncertainty in geochemical data can be minimized (denoised). This is a limitation to propagating uncertainty through downstream activities, particularly through complex models, which can result from the usage of artificial intelligence-based methods. This study aims to develop a deep learning-based method to examine and quantify uncertainty contained in geochemical survey data. Specifically, we demonstrate that: (1) autoencoders can reduce or modulate geochemical data uncertainty; (2) a reduction in uncertainty is observable in the spatial domain as a decrease of the nugget; and (3) a clear data reconstruction regime of the autoencoder can be identified that is strongly associated with data denoising, as opposed to the removal of useful events in data, such as meaningful geochemical anomalies. Our method to post-hoc denoising of geochemical data using deep learning is simple, clear and consistent, with the amount of denoising guided by highly interpretable metrics and existing frameworks of scientific data quality. Consequently, variably denoised data, as well as the original data, could be fed into a single downstream workflow (e.g., mapping, general data analysis or mineral prospectivity mapping), and the differences in the outcome can be subsequently quantified to propagate data uncertainty.

Zimbabwe's coloured gemstone endowments - A regional geological overview

Zimbabwe hosts a varied array of coloured gemstones. With the exception of emerald deposits and several world-class pegmatites, few of the gemstone occurrences have received detailed attention from the scientific or mineral exploration communities. In the present contribution we summarize the status of knowledge of the gemstone deposits and occurrences in Zimbabwe, paying particular attention to the geological settings in which they were formed. Synthesis of this regional geological approach reveals that there may be significant exploration potential for further gemstone occurrences, particularly in the extensive pegmatite fields and in Al-enriched orogenic belts that have undergone greenschist to granulite facies metamorphism. Further socio-economic and developmental initiatives will aid in optimizing the value generation from this important sub-sector.

Oblique distribution patterns and the underlying mechanical model of orebody groups controlled by structures at different scales

The oblique distribution of orebodies is a basic feature of the spatial distribution of orebody groups in hydrothermal deposits, and it is closely related to the shearing effect. However, the oblique distribution patterns of orebody groups controlled by ore-controlling/ore-forming structures at different scales (orebody, ore deposit and ore field scales) and the underlying formation mechanism are unclear but could be used to directly constrain mineral exploration and prospecting breakthroughs in the deep and peripheral areas of ore deposits. This paper uses the northeastern Yunnan Ge-rich Pb–Zn ore concentration area in the Sichuan–Yunnan–Guizhou metallogenic area as an example to analyse and demonstrate the oblique distribution patterns of orebodies (orebody groups) controlled by ore-forming/ore-controlling structures at different scales and the underlying mechanical model based on the Theory and Methods of Ore field Geomechanics. The results indicate that in 3D space, the oblique distributions of orebodies (veins), orebody groups and ore deposits are controlled by the mechanical properties, kinematic characteristics, and tectonic stress fields of ore-forming/ore-controlling fault structures of different sequences during the mineralization period. This study has an important guiding role for ore field-scale exploration deployment, evaluation of deep and peripheral prospecting, and exploration project arrangement, with the aim of helping mining companies increase resource reserves and production.

Sustainability of Electric Vehicles Using Artificial Intelligence

Electric vehicles (EVs) are widely regarded as a more environmentally sustainable transportation option compared to traditional internal combustion engine vehicles. However, concerns persist regarding the sustainability impacts of EV battery manufacturing, specifically the mining of lithium used in lithium-ion batteries. This literature review examines the potential for artificial intelligence (AI) to improve the sustainability of lithium mining and battery manufacturing for EVs. An extensive literature search yielded insights into the environmental impacts of lithium mining such as high water usage, carbon emissions, and soil contamination. These impacts present a challenge to the sustainability narrative of EVs. However, researchers have identified numerous applications for AI across the lithium-ion battery supply chain that could mitigate these effects. From optimizing mineral exploration and mining operations to recycling spent batteries, AI solutions show promise to reduce the lifecycle impacts of lithium-ion batteries. Additional research is still needed to quantify these potential improvements and implement AI responsibly. Furthermore, technology alone cannot address the complex socio-economic challenges associated with lithium mining. Ultimately, a combination of technological innovation and ethical, inclusive resource management will be required to truly transition to sustainable EV mobility. Keywords: electric vehicles, lithium-ion batteries, lithium mining, sustainability, artificial intelligence.

Social impacts of critical mineral exploration on Indigenous peoples’ lands: A case study from Solomon Islands

The growing uptake of energy transition technologies in response to global and national climate policy goals to transition to a low-carbon future is increasing demand for critical minerals. This increased demand is expected to intensify mineral exploration, particularly on Indigenous peoples’ lands. However, mineral exploration is a protracted process, fraught with uncertainty, and few mineral exploration projects progress to development. While the social impacts of mine construction, operation, and increasingly, closure are well documented in the literature, there is limited empirical evidence on the social impacts of mineral exploration on Indigenous peoples. This paper contributes to this gap in the literature by drawing on the case of nickel exploration in Solomon Islands. Drawing on qualitative data elicited from semi-structured interviews, this research indicates that activities associated with critical mineral exploration on Indigenous peoples’ lands generate more negative than positive impacts for affected communities and interested parties. Overlooking the social impacts of critical mineral exploration can lead to community opposition, which in turn can lead to project abandonment, thereby potentially delaying the energy transition and achievement of global climate goals. The findings underscore the importance of ‘just transition’ in the context of the accelerating energy transition to a low-carbon future.

Alteration Mapping for Porphyry Cu Targeting in the Western Chagai Belt, Pakistan, Using ZY1-02D Spaceborne Hyperspectral Data

Abstract The Chagai porphyry Cu belt in Pakistan is an important metallogenic terrain extending approximately 400 km in an east-west direction. Most of the known porphyry Cu deposits, such as the world-class Reko Diq deposit and Saindak deposit, are located in the western Chagai belt. In this study, the ZY1-02D hyperspectral data acquired by a recently launched spaceborne imaging spectrometer with 166 bands within a 0.4- to 2.5-μm spectral region were used to map mineral information over 8,000 km2 for exploring potential targets of porphyry Cu mineralization in the western Chagai belt. False color composite, spectral angle mapper, and wavelength position mapping methods were used in this research leading to the identification of a series of alteration minerals (including muscovite [sericite], kaolinite, alunite, epidote, chlorite, and calcite) from ZY1-02D data. The alteration mineral maps derived from ZY1-02D data match well with the known deposits and field inspections. Twenty-three new targets were identified as potential porphyry Cu mineralization targets for further exploration in the study area. Three targets, north of Saindak, Koh-i-Sultan, and Durban Chah, and six alteration sites in the southwest of Durban Chah, were inspected in the field, and Cu-Au mineralization was confirmed in all these inspected areas. As the ZY1-02D hyperspectral data covers most of the land area of the earth, this study provides new insights for mineral exploration and lithologic mapping in remote regions.

Picking of weak signal in seismic exploration of non-coal solid mines based on phase-locked technology

The exploration and development of mineral resources are of crucial significance to national economy, people's livelihood and national security. Therefore, in order to extract the weak signal in seismic exploration of non-coal solid mines in shallow surface layer to achieve high-resolution exploration, the mathematical model and physical model of multiplier are established by referring to the frequency point acquisition technology in wireless radar communication, that is, phase-locked technology. The weak effective seismic signals of different frequency points are picked out from the collected 120 dB dynamic range spectrum, and the information of stratigraphic structure under high noise background is obtained more effectively by using mixing detection and two-phase demodulation technology in the harmonic component spectrum, thereby improving the high resolution and high precision of mineral exploration. The lock-in amplifier is added to the front stage of the seismograph to obtain the effective seismic wave reflected by the wave impedance interface required in the exploration task. Experimental results show that this method significantly improves the SNR and protects the weak effective signal from loss. It adds new technology and method to seismic signal acquisition and processing, and provides a new way to obtain high-quality seismic data in the field of mineral geophysical exploration, and will be widely used after being promoted in the fields of mineral exploration, geological disaster prediction, military geophysics, and archaeology.

Genesis of the Hacipushanbei Pb-polymetallic deposit in the East Kunlun Orogen, NW China: Constraints from geochronology and in situ trace-elements and sulfur isotopes of sulfides

A recent mineral exploration programme in the East Kunlun Orogen (EKO) identified the new Hacipushanbei Pb-polymetallic deposit. The ores are hosted in faults in granitic rocks and are marked by high Pb grades up to 22.5 %. U–Pb dating of calcites associated with mineralization yields an intersection age of 208 ± 9 Ma, indicating a late Triassic mineralization event. Fluid-inclusion analysis reveals that the ore-forming fluids are characterized by medium-to-high temperature, medium-to-low salinity, and low density. Fluid boiling or immiscibility during ore-formation is also noted. Three sub-generations of pyrite (Py1-1, Py1-2, and Py2) are recognized in the early mineralization stages. In situ trace element analysis shows low Co and Ni contents in Py1-1 and Py1-2 but high contents in Py2. Conversely, Au and As concentrations are the highest in Py1-2. Variations in As contents are systematically coupled with Au. Additionally, sphalerite in the second stage contains higher amounts of Co, Cu, Cd, and In, via a wide range of isomorphic elemental substitutions of X4+(Sn4+ and Ge4+) + X3+(In3+, Sb3+, and Ga3+) + X+(Ag+ and Cu+) ↔ 4Zn2+ and the associated In enrichment. The average δ34S values of in situ and single minerals of Py1-1, Py1-2, Py2, Sp2, Ccp2, and Gn3 are 2.3 ‰, 3.3 ‰, 2.6 ‰, 2.4 ‰, 2.2 ‰, and 0.9 ‰, respectively. These values display a trend of initial increase and then decrease, consistent with thermodynamic fractionation. These data collectively indicate that the ore-forming fluid originated from a deep-seated magma chamber, and subsequent fluid immiscibility led to the formation of high-grade ores. Given the conspicuous association of Pb-polymetallic deposits with magmatic rocks in the EKO, we envisage that our genetic model can be applied, to some extent, to evaluate the potential of some mineral exploration targets.

Deep investigation of muography in discovering geological structures in mineral exploration: a case study of Zaozigou gold mine

Summary Muography is a promising and rapidly developing physical prospecting technique based on the attenuation of muon flux. The feasibility and potential of applying muography to mining were presented in a small number of previous case studies. In this work, we aimed to address three challenges that limit the applicability and efficiency of muography in mineral exploration: (1) application to low-density-contrast ore body exploration, (2) analysis of altitudinal impacts on measurements, and (3) precise and efficient reconstruction. We conducted the first case of applying muography to the exploration for low-density-contrast ore bodies. Six muon imaging systems were placed underground to collect surviving muons for roughly half a year. We analyzed the altitudinal impact on the data measurements and proposed a simplified method to eliminate it. We also developed the seed algorithm, a novel three-dimensional reconstruction algorithm, that can reconstruct anomalies located far away from the detectors and avoid their elongation along the observed directions. Benefiting from the seed algorithm, a low-density-contrast orebody and a limonitic siliceous slate structure were reconstructed, demonstrating the sensitivity of this technique in density distinction; discoveries of several mined-out areas are important for accident avoidance; and reconstruction of the stope and scarps served as strong circumstantial evidence of the reliability of the result. The success of this experiment shows the great value of muography in the economic, research and safety aspects of mineral exploration and inspection. Moreover, the overcoming of challenges is helpful for the development of muography, making it an effective and competitive technique in this field.

Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions.

How to choose a proper representation of compositional data for mineral exploration?

Regional mineral exploration is based on geochemical data of which the nature is compositional and frequently involves a large number of components. Consequently, it mostly needs multivariate dimension reduction methods such as principal component analysis (PCA) and its various robust versions. The application of such methods, defined for real random variables, require the data to be represented in coordinates supported in the real space. However, a common problem in exploration geochemistry is to select the appropriate representation. Using centered (clr) and isometric (ilr) logratio coordinates to discriminate anomalous zones for orogeny gold exploration throughout Sweden revealed that there is, as expected, no difference between the two representation methods. The main difference affects the interpretation of the coordinates used. This is observed for regional scale exploration, while it is also needed to study different ways of representing geochemical data in local scale.

RockS2Net: Rock image classification via a spatial localization siamese network

The acquisition of rock property information is at the core of regional geological survey and mineral exploration, but hand-crafted feature-based methods are heavily influenced by human prior knowledge and have limited transferability. End-to-end deep learning techniques, exemplified by convolutional neural networks (CNNs), have attained significant accomplishments in the domain of image classification. However, previous end-to-end CNN-based methods are hard to focus on image critical areas, and they also cannot make full use of global alignment dependency of the rocks. In this paper, rock image classification via a RockS2Net is proposed. The RockS2Net framework adopts a Siamese architecture, characterized by two branches that share parameters, enabling the efficient extraction of both global and local features. This approach facilitates the extraction of global features from entire images and focuses on critical areas to extract local features. The architecture of spatial transformer network (STN) is introduced to transform microscopic images of rock sections to their critical areas. By fusing local features and global features, the properties obtained from microscopic images of rock sections can be more accurately predicted. To test the robust generalizability of the proposed method, the constructed CHN-Rock images dataset is used for experiments and evaluation. Experimental results show that the accuracy of the proposed RockS2Net on the CHN-Rock image dataset is 2–3% higher than that of other rock image classification networks.

Fast Initial Model Design for Electrical Resistivity Inversion by Using Broad Learning Framework

The electrical resistivity method is widely used in near-surface mineral exploration. At present, the deterministic algorithm is commonly employed in three-dimensional (3-D) electrical resistivity inversion to obtain subsurface electrical structures. However, the accuracy and efficiency of deterministic inversion rely on the initial model. In practice, obtaining an initial model that approximates the true subsurface electrical structures remains challenging. To address this issue, we introduce a broad learning (BL) network to determine the initial model and utilize the limited memory quasi-Newton (L-BFGS) algorithm to conduct the 3-D electrical resistivity inversion task. The powerful mapping capability of the BL network enables one to find the model that elucidates the actual observed data. The single-layer BL network makes it efficient and easy to realize, leading to much faster network training compared to that using the deep learning network. Both the synthetic and field experiments suggest that the BL framework could effectively obtain the initial model based on observed data. Furthermore, in comparison to using a homogeneous medium as the initial model, the L-BFGS inversion with the BL framework-designed initial model improves the inversion accuracy of subsurface electrical structures and expedites the convergence speed of the iteration. This study provides an effective approach for fast initial model design in a data-driven manner when the prior information is unavailable. The proposed method can be useful in high-precision imaging of near-surface mineral electrical structures.

U-Pb and Re-Os geochronology and lithogeochemistry of granitoid rocks from the Burnthill Brook area in central New Brunswick, Canada: Implications for critical mineral exploration

The Burnthill Brook area of central New Brunswick contains four Late Devonian intrusions (Burnthill, Buttermilk Brook, Dungarvon, and Sisters Brook plutons) emplaced into greenschist-facies Cambro-Ordovician graphite-bearing meta-sedimentary rocks of the Miramichi Group and/or Middle Ordovician bimodal metavolcanic rocks of the Tetagouche Group. To help resolve the complex nature of molybdenite (± tungsten) mineralization and its relationship with the magmatic evolution of the Burnthill Brook area, this study combines whole-rock geochemistry (major- and trace-elements, and Sr-Nd-Hf-Pb isotopes) with U-Pb zircon and Re-Os molybdenite analyses of samples collected from the Falls Creek occurrence (Mo ± W), the Burnthill Sn-W-Mo deposit, and previously obtained Re-Os ages from the Sisson Brook W-Mo-Cu deposit.New geochronological data from the Burnthill Brook area, in combination with previous published Ar-Ar and U-Pb data, defines a protracted (25 Ma) period of magmatism from 396 to 371 Ma. These data indicate that peaks of mineralization, at ca. 380 and 378 Ma, correlate with distinct pulses of magmatism. The younger mineralization corresponds with the final magmatic pulse in the area and is likely genetically related to aplite dykes present within the Falls Creek (FC) occurrence. Furthermore, this study has identified a younger generation of Mo-W-REE mineralization that is hosted by FC aplite dykes.Burnthill Brook plutons are highly evolved, high-silica peraluminous A-type granites that have undergone extensive fractionation, leading to elevated concentrations of incompatible elements (e.g., Rb, Y, Nb, Cs, Th, and U) and fluorine (mean = 0.08 ± 0.05 wt%), as well as economically important elements, such as Sn, Ta, W, Mo, Zn, and Pb. The enrichment of these elements is primarily associated with fractional crystallization, a process that was amplified by repeated magmatic pulses over a protracted period, resulting in Mo + W saturation.The evolution of granite-related mineral deposits in the Burnthill Brook area is attributed to a regional-scale metallogenic epoch associated with the Neoacadian Orogeny. This same metallogenic epoch is likely responsible for the formation of numerous granite-related mineral deposits across the Canadian Appalachians, and further reinforces the conclusion that pulsed magmatism during the Neoacadian Orogeny (390–350 Ma) played an integral role in the formation of these deposits.Geochemical and geochronological evidence collectively suggests that the various plutons are individual components of a Middle to Late Devonian batholith that underlies this region, and is provisionally herein referred to as the Peaked Mountain Batholith. In addition to the area encompassing the exposed Burnthill, Buttermilk Brook, Dungarvon and Sisters Brook granites, this presumptive batholith is projected to extend at least 24 km to the south-southwest to include the unidentified, buried pluton(s) responsible for the coeval Sisson Brook area mineralization (W-Mo-Cu).

Mineralogy and Geochemistry of the Mupane and Shashe Gold Deposits of the Tati Greenstone Belt (NE Botswana): Implications for Mineral Deposit Models and Exploration

Abstract The Archean Tati greenstone belt is located at the southwestern margin of the Zimbabwe craton (northeast Botswana) and hosts numerous Cu-Ni ± platinum group element (PGE) and Au ± Ag occurrences and deposits. Gold occurrences/deposits are poorly studied, and key questions pertaining to their genesis remain unclear, including the mode of occurrence(s) of gold, the relative timing of gold introduction with respect to the evolution of the greenstone belt, the number of gold mineralization events, the alteration patterns, and the relationships between each alteration pattern and gold mineralization. A detailed study that includes sulfide and gold grain chemistry using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and electron probe microanalysis (EPMA), respectively, and X-ray diffraction (XRD) of mineralized rock samples of three Tati greenstone belt gold deposits was carried out to constrain the genesis of gold mineralization and formulate exploration guidelines in the Tati greenstone belt. Gold in the Tati greenstone belt is the result of multiple events, is mainly associated with arsenopyrite, pyrite, and sphalerite, and occurs as (1) microinclusions within sulfides, (2) intergrowth with sulfides, (3) minute particles (<2–10 µm) within the silicate matrix, (4) microfractures and microvug infills, and (5) lattice-bound and sulfide-hosted refractory gold. The first gold event (as electrum) is premetamorphic and associated with sphalerite-quartz veins. The second stage is a postmetamorphic high-grade gold event that is accompanied by extensive carbonatization and propylitization of host rocks. The third stage of gold mineralization is marked by the dissolution of gold in the early formed stages 1 to 2 and subsequent reprecipitation within cracks, fractures, and vugs. Auriferous pyrite composition suggests that Au-bearing mineralizing fluids are predominantly of magmatic origin and that their physicochemical compositions changed during the mineralization process, as supported by chemically zoned Au-bearing arsenopyrite, various alteration types containing gold, and variation in gold fineness across the several gold deposits in the Tati greenstone belt. Gold deposition in the Tati greenstone belt mainly occurred through sulfidation, as indicated by the closest spatial association between gold and Fe-bearing sulfides and ferromagnesian silicates. Gold in the Tati greenstone belt is closely correlated with As, Sb, Pb, Bi, Ni, Hg, Tl, Cd, In, Mo, W, Zn, and Te and moderately to weakly associated with Sn, Se, Cr, Co, Ge, Cd, Mn, V, Ga, and Ag. The correlation between Au and fluid mobile elements, i.e., Te, Sb, Se, As, Hg, and Bi, can be used as a vectoring tool during the exploration of gold within the Tati greenstone belt, as these elements likely form halos that are much broader than the primary footprint of gold mineralization.