Search Results (126)

This study developed a copper mineral prospectivity map for the Koldar massif, Kazakhstan, using an integrated approach combining geophysical and satellite methods. A strong spatialgenetic link was identified between faults and hydrothermal mineralization, with faults acting as key conduits for ore-bearing fluids. Lineament analysis and density mapping confirmed the high permeability of the Koldar massif, indicating its structural prospectivity. Hyperspectral and multispectral data (ASTER, PRISMA, WorldView-3) were applied for detailed mapping of hydrothermal alteration (phyllic, propylitic, argillic zones), which are critical for discovering porphyry copper deposits. In particular, WorldView-3 imagery facilitated the identification of new prospective zones. The transformation of magnetic and gravity data successfully delineated geological features and structural boundaries, confirming the fractured nature of the massif, a key structural factor for mineralization. The resulting map of prospective zones, created by normalizing and integrating four evidential layers (lineament density, PRISMA-derived hydrothermal alteration, magnetic, and gravity anomalies), is thoroughly validated, successfully outlining the known Aktogay, Aidarly, and Kyzylkiya deposits. Furthermore, new, previously underestimated prospective areas were identified. This work fills a significant knowledge gap concerning the Koldar massif, which had not been extensively studied using satellite methods previously. The key advantage of this research lies in its comprehensive approach and the successful application of high-quality hyperspectral imagery for mapping new prospective zones, offering a cost-effective and efficient alternative to traditional ground-based investigations. Full article

The Chagai porphyry copper belt is a major component of the Tethyan metallogenic domain, which spans approximately 300 km and hosts several giant porphyry copper deposits. The tectonic setting, whether subduction-related or post-collisional, and the deep dynamic processes governing the formation of these giant deposits remain poorly understood. Mafic microgranular enclaves (MMEs), mafic dikes, and multiple porphyries have been documented in the Saindak mining area. This work examines both the ore-rich and non-ore intrusions in the Saindak porphyry Cu-Au deposit, using methods like molybdenite Re-Os dating, U-Pb zircon ages, Hf isotopes, and bulk-rock geochemical data. Geochronological results indicate that ore-fertile and barren porphyries yield ages of 22.15 ± 0.22 Ma and 22.21 ± 0.33 Ma, respectively. Both MMEs and mafic dikes have zircons with nearly identical ²⁰⁶Pb/²³⁸U weighted mean ages (21.21 ± 0.18 Ma and 21.21 ± 0.16 Ma, respectively), corresponding to the age of the host rock. Geochemical and Sr–Nd–Hf isotopic evidence indicates that the Saindak adakites were generated by the subduction of the Arabian oceanic lithosphere under the Eurasian plate, rather than through continental collision. The adakites were mainly formed by the partial melting of a metasomatized mantle wedge, induced by fluids from the dehydrating subducting slab, with minor input from subducted sediments and later crust–mantle interactions during magma ascent. We conclude that shallow subduction of the Arabian plate during the Oligocene–Miocene may have increased the flow of subducted fluids into the sub-arc mantle source of the Chagai arc. This process may have facilitated the widespread deposition of porphyry copper and copper–gold mineralization in the region. Full article

This study was conducted on a copper porphyry deposit located in Espinar, Cusco (Peru), with the objective of developing and comparing predictive models for processing capacity in SAG grinding circuits. A total of 174 samples were used for the JK Drop Weight Test (JKDWT) and 1172 for the Bond Work Index (BWi), along with 36 months of operational plant data. Three modeling methodologies were evaluated: DWi-BWi, SGI-BWi, and SMC-BWi (Mia, Mib), all integrated into a geometallurgical block model. Validation was performed through reconciliation with actual plant data, considering operational constraints such as transfer size (T₈₀) and maximum throughput (TPH). The model based on SMC parameters and BWi showed the best predictive performance, with a root mean square error (RMSE) of 143 t/h and a mean relative deviation of 1.5%. This approach enables more accurate throughput forecasting, improving mine planning and operational efficiency. The results highlight the importance of integrating geometallurgical and operational data to build robust models that are adaptable to ore variability and applicable to both short- and long-term planning scenarios. Full article

Probabilistic models are used to describe random processes and quantify prediction uncertainties in a principled way. Examples include geotechnical and geological investigations that seek to model subsurface hydrostratigraphic properties or mineral deposits. In mining geology, model validation efforts have generally lagged behind the development and deployment of computational models. One problem is the lack of industry guidelines for evaluating the uncertainty and predictive performance of probabilistic ore grade models. This paper aims to bridge this gap by developing a holistic approach that is autonomous, scalable and transferable across domains. The proposed model assessment targets three objectives. First, we aim to ensure that the predictions are reasonably calibrated with probabilities. Second, statistics are viewed as images to help facilitate large-scale simultaneous comparisons for multiple models across space and time, spanning multiple regions and inference periods. Third, variogram ratios are used to objectively measure the spatial fidelity of models. In this study, we examine models created by ordinary kriging and the Gaussian process in conjunction with sequential or random field simulations. The assessments are underpinned by statistics that evaluate the model’s predictive distributions relative to the ground truth. These statistics are standardised, interpretable and amenable to significance testing. The proposed methods are demonstrated using extensive data from a real copper mine in a grade estimation task and are accompanied by an open-source implementation. The experiments are designed to emphasise data diversity and convey insights, such as the increased difficulty of future-bench prediction (extrapolation) relative to in situ regression (interpolation). This work enables competing models to be evaluated consistently and the robustness and validity of probabilistic predictions to be tested, and it makes cross-study comparison possible irrespective of site conditions. Full article

The aim of this study is to explore the feasibility of using flotation wastewater from copper–porphyry ore processing to synthesize a gel that serves as a precursor for a polymer nanocomposite used in supercapacitor electrode fabrication. These wastewaters—characterized by high acidity and elevated concentrations of metal cations (Cu, Ni, Zn, Fe), sulfates, and organic reagents such as xanthates, oil (20 g/t ore), flotation frother (methyl isobutyl carbinol), and pyrite depressant (CaO, 500–1000 g/t), along with residues from molybdenum flotation (sulfuric acid, sodium hydrosulfide, and kerosene)—are byproducts of copper–porphyry gold-bearing ore beneficiation. The reduction of Ni powder in the wastewater induces the degradation and formation of a gel that captures both residual metal ions and organic compounds—particularly xanthates—which play a crucial role in the subsequent steps. The resulting gel is incorporated during the oxidative polymerization of aniline, forming a nanocomposite with a polyaniline matrix and embedded xanthate-based compounds. An asymmetric supercapacitor was assembled using the synthesized material as the cathodic electrode. Electrochemical tests revealed remarkable capacitance and cycling stability, demonstrating the potential of this novel approach both for the valorization of industrial waste streams and for enhancing the performance of energy storage devices. Full article

Copper is typically acknowledged as a critical mineral and one of the vital components of various of today’s fast-growing green technologies. Porphyry copper systems, which are an important source of copper and molybdenum, typically consist of large volumes of hydrothermally altered rocks, mainly around porphyry copper intrusions. Mapping hydrothermal alteration zones associated with porphyry copper systems is one of the most important indicators for copper exploration, especially using advanced satellite remote sensing technology. This paper presents a sophisticated remote sensing-based method that uses ASTER satellite imagery (SWIR bands 4 to 9) to identify hydrothermal alteration zones by combining the discrete wavelet transform (DWT) and the median absolute deviation (MAD) algorithms. All six SWIR bands (bands 4–9) were analyzed independently, and band 9, which showed the most consistent spatial patterns and highest validation accuracy, was selected for final visualization and interpretation. The MAD algorithm is effective in identifying spectral anomalies, and the DWT enables the extraction of features at different scales. The Urmia–Dokhtar magmatic arc in central Iran, which hosts the Zafarghand porphyry copper deposit, was selected as a case study. It is a hydrothermal porphyry copper system with complex alteration patterns that make it a challenging target for copper exploration. After applying atmospheric corrections and normalizing the data, a hybrid algorithm was implemented to classify the alteration zones. The developed classification framework achieved an accuracy of 94.96% for phyllic alteration and 89.65% for propylitic alteration. The combination of MAD and DWT reduced the number of false positives while maintaining high sensitivity. This study demonstrates the high potential of the proposed method as an accurate and generalizable tool for copper exploration, especially in complex and inaccessible geological areas. The proposed framework is also transferable to other porphyry systems worldwide. Full article

The Wubaduolai copper deposit, a newly discovered porphyry-type deposit located in the western section of the Gangdese metallogenic belt, shows great potential for mineralization. Investigating the ore-bearing potentiality of the adakitic granite in this area is crucial for identifying concealed ore bodies and assessing the metallogenic potential. This paper presents the zircon U-Pb dating, Hf isotope analysis, and whole-rock major and trace geochemical analysis of the plutons in the Wubaduolai mining area. The results indicate that the zircon U-Pb concordia age of the monzogranite is 15.7 ± 0.1 Ma, while the granodiorite porphyry has a concordia age of 15.9 ± 0.2 Ma, both corresponding to a Miocene diagenesis. The geochemical data show that both plutons belong to the high-K calc-alkaline series, characterized by a relative enrichment of large-ion lithophile elements (K, Rb, Ba, and Sr) and a depletion of high-field-strength elements (Nb, Ta, and Ti). Both plutons are characterized by low Y, low Yb, and high Sr/Y values, displaying the typical geochemical characteristics of adakites. Their mineral composition is similar to that of adakite. The ε_Hf(t) values of the monzogranite and granodiorite porphyry range from −5.34 to −2.3 and −5.2 to −3.43, respectively, with two-stage model ages (T_DM2) of 1246–1441 Ma and 1318–1432 Ma. Based on the regional data and this study, the plutons in the Wubaduolai mining area formed in a post-collision setting following the India–Asia continental collision. The magma source is identified as the partial melting of a thickened, newly formed lower crust. The above characteristics are consistent with the diagenetic and metallogenic ages, magma source, and dynamic backgrounds of the typical regional deposits. Full article

A new machine learning algorithm, BoxRF, was developed specifically for estimating grades from drillhole datasets. The method combines the features of classical estimation methods, such as search boxes, search direction, and estimation based on inverse distance methods, with the robustness of random forest (RF) methods that come from forming numerous random groups of data. The method was applied to a porphyry copper deposit, and results were compared to various ML methods, including XGBoost (XGB), k-nearest neighbors (KNN), neural nets (NN), and RF. Scikit-learn RF (SRF) performed the best (R² = 0.696) among the ML methods but underperformed BoxRF (R² = 0.751). The results were confirmed through a five-fold cross-validation exercise where BoxRF once again outperformed SRF. The box dimensions that performed the best were similar in length to the ranges indicated by variogram modeling, thus demonstrating a link between machine learning and traditional methods. Numerous combinations of hyperparameters performed similarly well, implying the method is robust. The inverse distance method was found to better represent the grade–space relationship in BoxRF than median values. The superiority of BoxRF over SRF in this dataset is encouraging, as it opens the possibility of improving machine learning by incorporating domain knowledge (principles of geology, in this case). Full article

Unsupervised anomaly detection algorithms have gained significant attention in the field of mineral prospectivity mapping (MPM) due to their ability to reveal hidden mineralization zones by effectively modeling complex, nonlinear relationships between exploration data and mineral deposits. This study utilizes two tree-based anomaly detection algorithms, namely, isolation forest (IF) and extended isolation forest (EIF), to enhance MPM and exploration targeting. According to the conceptual model of porphyry copper deposits, several evidence layers were generated, including fault density, multi-element geochemical signatures, proximity to various alteration types (phyllic, argillic, propylitic, and iron oxide), and proximity to intrusive rocks. These layers were integrated using IF and EIF algorithms, and their results were subsequently compared with a geological map of the study area. The comparison revealed a high degree of overlap between the identified anomalous zones and geological features, such as andesitic rocks, tuffs, rhyolites, pyroclastics, and intrusions. Additionally, quantitative assessments through prediction-area plots validated the efficacy of both models in generating prospective targets. The results highlight the significant influence of hyperparameter tuning on the accuracy of prospectivity models. Furthermore, the study demonstrates that hyperparameter tuning is more intuitive and straightforward in IF, as it provides a clear and distinct tuning pattern, whereas EIF lacks such clarity, complicating the optimization process. Full article

In the argillic alteration zone of the SinAbad area of the Urumieh–Dokhtar magmatic belt (Iran), Mg-rich, Fe-poor chlorites, which crystallised at temperatures between 160 °C and 260 °C, were affected by extensive alteration to smectite mixed-layering at the micro- and nano-scales during the retrograde evolution of the hydrothermal system. Chlorites retain their usual optical aspect and properties, except for the index of refraction perpendicular to the (001) layers, which becomes lower than those parallel to the layers, producing an increase in birefringence and change in the optic and elongation signs, in comparison to the ordinary ones for Mg chlorites. Scanning electron microscopy (SEM) maps and compositions, and electron microprobe (EMP) analyses indicate minor but ubiquitous Ca (and K) content. X-ray diffraction (XRD) of chloritic concentrates allowed the identification of chlorite and tosudite. High-resolution transmission electron microscopy (HRTEM) images show major 14 Å (chlorite), with the frequent presence of 24 Å (contracted tosudite) individual layers and small packets up to five layers thick. Lateral change from 14 Å to 24 Å individual layers has been visualised. High-resolution chemical maps obtained in high-angle annular dark-field (HAADF) mode confirm the existence of areas preferentially dominated by chlorite or tosudite. The overall chemical compositions obtained by SEM, EMP, and transmission electron microscopy (TEM) align from the chlorite to the tosudite end-members, whose pure compositions could be determined from extreme analytical electron microscopy (AEM) analyses. The described intergrowths and interlayers, under the optical resolution, could provide a clue to explain changes in the normal optic properties of chlorite, which are mentioned, but not explained, in the literature. Full article

The Kongco area of Nima in the northern part of the Lhasa terrane has a suite of alkaline granitic porphyry dykes associated with Early Cretaceous granites and accompanied by Cu/Mo mineralization. LA-ICP-MS ²⁰⁶Pb/²³⁸U zircon geochronology performed on the dykes produced an age of 104.15 ± 0.94 Ma (MSWD = 0.98), indicating the Early Cretaceous emplacement of the dykes. The dykes exhibit high silica (SiO₂ = 76.22~77.90 wt.%), high potassium (K₂O = 4.97~6.21 wt.%), high alkalinity (K₂O + Na₂O = 8.07~8.98 wt.%), low calcium (CaO = 0.24~0.83 wt.%), low magnesium (MgO = 0.06~0.20 wt.%), and moderate aluminum content (Al₂O₃ = 11.93~12.45 wt.%). The Rieterman index (σ) ranges from 1.93 to 2.34. A/NK (molar ratio Al₂O₃/(Na₂O + K₂O)) and A/CNK (molar ratio Al₂O₃/(CaO + Na₂O + K₂O)) values of the dykes range from 1.06 to 1.18 and 0.98 to 1.09, respectively. The dykes are relatively enriched in Rb, Th, U, K, Ta, Ce, Nd, Zr, Hf, Sm, Y, Yb, and Lu, and they show a noticeable relative depletion in Ba, Nb, Sr, P, Eu, and Ti, as well as an average differentiation index (DI) of 96.42. The dykes also exhibit high FeO_T/MgO ratios (3.60~10.41), Ga/Al ratios (2.22 × 10⁻⁴~3.01 × 10⁻⁴), Y/Nb ratios (1.75~2.40), and Rb/Nb ratios (8.36~20.76). Additionally, they have high whole-rock Zr saturation temperatures (884~914 °C), a pronounced Eu negative anomaly (δEu = 0.04~0.23), and a rightward-sloping “V-shaped” rare earth element pattern. These characteristics suggest that the granitic porphyry dykes can be classified as A2-type granites formed in a post-collisional tectonic environment and that they are weakly peraluminous, high-potassium, and Calc-alkaline basaltic rocks. Positive ε_Hf(t) values = 0.43~3.63 and a relatively young Hf crustal model age (T_DM2 = 826~1005 Ma, ⁸⁷Sr/⁸⁶Sr ratios = 0.7043~0.7064, and ε_Nd(t) = −8.60~−2.95 all indicate lower crust and mantle mixing. The lower crust and mantle mixing model is also supported by (²⁰⁶Pb/²⁰⁴Pb)t = 18.627~18.788, (²⁰⁷Pb/²⁰⁴Pb)t = 15.707~15.719, (²⁰⁸Pb/²⁰⁴Pb)t = 39.038~39.110). Together, the Hf, Sr and Pb isotopic ratios indicate that the Kongco granitic porphyry dykes where derived from juvenile crust formed by the addition of mantle material to the lower crust. From this, we infer that the Kongco granitic porphyry dykes are related to a partial melting of the lower crust induced by subduction slab break-off and asthenospheric upwelling during the collision between the Qiangtang and Lhasa terranes and that they experienced significant fractional crystallization dominated by potassium feldspar and amphibole. These dykes are also accompanied by significant copper mineralization (five samples, copper content 0.2%), suggesting a close relationship between the magmatism associated with these dykes and regional metallogenesis, indicating a high potential for mineral exploration. Full article

Mineral resources, particularly copper, are crucial for the sustained economic growth of developing countries like Kazakhstan. Over the past four decades, the diversity and importance of critical minerals for high technology and environmental applications have increased dramatically. Today, copper is a critical metal due to its importance in electrification. Porphyry deposits are important sources of copper and other critical metals. Conventional exploration methods for mapping alteration zones as indicators of high-potential zones in porphyry deposits are often associated with increased cost, time and environmental concerns. Remote sensing imagery is a cutting-edge technology for the exploration of minerals at low cost and in short timeframes and without environmental damage. Kazakhstan hosts several large porphyry copper deposits, such as Aktogay, Aidarly, Bozshakol and Koksai, and has great potential for the discovery of new resources. However, the potential of these porphyry deposits has not yet been fully discovered using remote sensing technology. In this study, a remote sensing-based mineral exploration approach was developed to delineate hydrothermal alteration zones associated with Aktogay porphyry copper mineralization in eastern Kazakhstan using Landsat-8 and ASTER satellite sensors. A comprehensive suite of image processing techniques was used to analyze the two remote sensing datasets, including specialized band ratios (BRs), principal component analysis (PCA) and the Crosta method. The remote sensing results were validated against field data, including the spatial distribution of geological lineaments and petrographic analysis of the collected rock samples of alteration zones and ore mineralization. The results show that the ASTER data, especially when analyzed with specialized BRs and the Crosta method, effectively identified the main hydrothermal alteration zones, including potassic, propylitic, argillic and iron oxide zones, as indicators of potential zones of ore mineralization. The spatial orientation of these alteration zones with high lineament density supports their association with underlying mineralized zones and the spatial location of high-potential zones. This study highlights the high applicability of the remote sensing-based mineral exploration approach compared to traditional techniques and provides a rapid, cost-effective tool for early-stage exploration of porphyry copper systems in Kazakhstan. The results provide a solid framework for future detailed geological, geochemical and geophysical studies aimed at resource development of the Aktogay porphyry copper mineralization in eastern Kazakhstan. The results of this study underpin the effectiveness of remote sensing data for mineral exploration in geologically complex regions where limited geological information is available and provide a scalable approach for other developing countries worldwide. Full article

The Yidun island arc was formed in response to the Late Triassic westward subduction of the Ganzi–Litang oceanic plate, a branch of the Paleo-Tethys Ocean. The Zhongdian arc, located in the south of the Yidun island arc, has relatively large number of porphyry (skarn) type Cu–Mo ± Au polymetallic deposits, the largest of which is the Pulang Cu (–Mo–Au) deposit with proven Cu reserves of 5.11 Mt, Au reserves of 113 t, and 0.17 Mt of molybdenum. However, the relationship between mineralization and the potassic alteration zone, phyllic zone, and propylitic zone of the Pulang porphyry deposit is still controversial and needs further study. Titanite (CaTiSiO₅) is a common accessory mineral in acidic, intermediate, and alkaline igneous rocks. It is widely developed in various types of metamorphic rocks, hydrothermally altered rocks, and a few sedimentary rocks. It is a dominant Mo-bearing phase in igneous rocks and contains abundant rare earth elements and high-field-strength elements. As an effective geochronometer, thermobarometer, oxybarometer, and metallogenic potential indicator mineral, titanite is ideal to reveal the magmatic–hydrothermal evolution and the mechanism of metal enrichment and precipitation. In this paper, major and trace element contents of the titanite grains from different alteration zones were obtained using electron probe microanalysis (EPMA) and laser-ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to define the changes in physicochemical conditions and the behavior of these elements during the process of hydrothermal alteration at Pulang. Titanite in the potassic alteration zone is usually shaped like an envelope. It occurs discretely or is enclosed by feldspar, with lower contents of CaO, Al, Sr, Zr and Hf; a low Nb/Ta ratio; high ∑REE + Y, U, Th, Ta, Nb, and Ga content; and high FeO/Al₂O₃ and LREE/HREE ratios. This is consistent with the characteristics of magmatic titanite from fresh quartz monzonite porphyry in Pulang and other porphyry Cu deposits. Titanite in the potassium silicate alteration zone has more negative Eu anomaly and a higher U content and Th/U ratio, indicating that the oxygen fugacity decreased during the transformation to phyllic alteration and propylitic alteration in Pulang. High oxygen fugacity is favorable for the enrichment of copper, gold, and other metallogenic elements. Therefore, the enrichment of copper is more closely related to the potassium silicate alteration. The molybdenum content of titanite in the potassium silicate alteration zone is 10²–10⁴ times that of the phyllic alteration zone and propylitic alteration zone, while the copper content is indistinctive, indicating that molybdenum was dissolved into the fluid or deposited in the form of sulfide before the medium- to low-temperature hydrothermal alteration, which may lead to the further separation and deposition of copper and molybdenum. Full article

The Luang Prabang (Laos)–Loei (Thailand) metallogenic belt is located on the northwestern margin of the Indochina Block. It is one of the most important gold–copper metallogenic belts in Southeast Asia. This region underwent tectonic and magmatic evolution in the late Paleozoic-Mesozoic period within the Paleo-Tethys realm, resulting in complex metallogenic processes. Consequently, epithermal Au-Ag, porphyry-skarn Au-Cu, and hydrothermal vein-type gold deposits were formed. However, the genetic type of the vein-type gold deposits is still not fully understood. The relationship between the three types of gold deposits and the regional tectonic evolution has not been summarized up until today. We summarize the previous mineralization characteristics and exploration data of commonly known deposits and combine them with new evidence and ore deposit insights from our recent studies on the source and evolution of ore-forming fluids in the region. We confirm that the hydrothermal vein-type gold deposits in the belt are typical orogenic gold deposits. Based on previous regional tectonic-magmatic-metallogenic studies, metallogenic characteristics, and temporal and spatial distribution of three types of typical gold–copper deposits in the belt, we synthesize and establish a regional metallogenic model related to the subduction-closure of the Paleo-Tethys Ocean and subsequent continental–continental collision process, resulting in the formation of epithermal Au-Ag during the late Permian-early Triassic subduction, porphyry-skarn Au-Cu in the early–middle Triassic period during the closure of the ocean, and orogenic Au during the late Triassic collision. Since there are few reports on the geochemical characteristics of gold–copper deposits and their related magmatic rocks, the potential for gold–copper mineralization and their links to the magmatic rocks in the belt still needs further study. Full article

In this study, an innovative methodology using trivariate copula-based conditional quantile regression (CBQR) is proposed for estimating copper recovery. This approach is compared with six supervised machine learning regression methods, namely, Decision Tree, Extra Tree, Support Vector Regression (linear and epsilon), Multilayer Perceptron, and Random Forest. For comparison purposes, an open access database representative of a porphyry copper deposit is used. The database contains geochemical information on minerals, mineral zoning data, and metallurgical test results related to copper recovery by flotation. To simulate a high undersampling scenario, only 5% of the copper recovery information was used for training and validation, while the remaining 95% was used for prediction, applying in all these stages error metrics, such as R², MaxRE, MAE, MSE, MedAE, and MAPE. The results demonstrate that trivariate CBQR outperforms machine learning methods in accuracy and flexibility, offering a robust alternative solution to model complex relationships between variables under limited data conditions. This approach not only avoids the need for intensive tuning of multiple hyperparameters, but also effectively addresses estimation challenges in scenarios where traditional methods are insufficient. Finally, the feasibility of applying this methodology to different data scales is evaluated, integrating the error associated with the change in scale as an inherent part of the estimation of conditioning variables in the geostatistical context. Full article