Search Results (159)

The variation features of metallic element grades can reflect the enrichment level of mineral deposits. Thus, quantitative characterization of the spatial patterns of metallogenic elements is fundamental to studying ore-forming processes and implementing mineral exploration. This study applies fractal theory and self-developed MATLAB computational scripts to process Cu grade datasets from 28 drillholes within the Pulang porphyry copper deposit, Yunnan Province. Both rescaled range (R/S) analysis and correlation integral methods were applied to clarify the spatial patterns of Cu grades in drill-cores. The calculated Hurst exponents ranged from 0.510 to 0.636, which demonstrated the persistent variation of Cu grades along the vertical direction of drillholes. This work further explored the correlation between Cu mineralization and fluctuations in correlation dimension (D_C), with D_C values spanning 0.011–2.873. Results indicate steep fractal gradient zones host high-grade copper ore bodies, and fractal dimension is a robust indicator to trace the migration of hydrothermal fluids. The Hurst exponents of Cu grade sequences correlate strongly with mineralization intensity, and ore-bearing veins extend continuously throughout all sampled drillholes. Accordingly, fractal gradients can be utilized to depict prospective zones for favorable mineralization in uncharted regions. This methodology may be applicable to other structurally controlled mineral deposits where similar fracture-controlled mineralization occurs, though further testing on different deposit types is needed. Full article

This study examines the impact of regrinding on the interfacial properties of sulfide minerals and the flotation performance of weathered copper–porphyry tailings. The feed material is characterized by a low copper grade (0.17%) and a high proportion of oxidized species (53.84%), which contribute to its inherent chemical stability and poor flotation kinetics. The findings indicate that regrinding serves a dual role: facilitating the liberation of mineral intergrowths and inducing mechanical surface renewal. This renewal is characterized by a significant decrease in the oxidation–reduction potential (ORP) and an intensification of the surface reactivity. Experimental results identify an optimal grinding fineness of 77%–81% passing −0.045 mm, yielding a copper recovery of 16.26% in the absence of a sulfidizing agent. The integration of sodium sulfide (400 g/t) with regrinding significantly enhances recovery to 36.37%, driven by the establishment of a reducing environment (ORP ≈ −150 mV) and the chemisorption-mediated activation of mineral surfaces. While ultrafine grinding (90%–100% passing −0.045 mm) further increases recovery to 51.47%, it is accompanied by deleterious sliming effects and a subsequent loss of process selectivity. The study confirms that mechanical surface rejuvenation and the optimization of electrochemical conditions are critical for improving the processing efficiency of anthropogenic resources, providing a theoretical framework for establishing rational beneficiation regimes. Full article

Titanite is a widespread accessory mineral in magmatic, metamorphic, and hydrothermal systems and can incorporate trace elements that are sensitive to ore-forming processes. Although titanite trace-element chemistry has been widely applied to individual ore systems and deposit comparisons, its potential for supervised machine-learning-based discrimination across multiple ore deposit types remains less systematically explored. In this study, we compiled a literature-based LA-ICP-MS titanite trace-element dataset comprising 1679 analyses from five major ore deposit types: porphyry, skarn, iron oxide–apatite (IOA), iron oxide copper–gold (IOCG), and orogenic Au deposits. A common feature set of 21 trace elements, including REE, Y, Zr, Hf, Nb, Ta, Th, and U, was used to evaluate six supervised machine-learning algorithms: K-nearest neighbors, support vector machine, random forest, XGBoost, TabMap, and TabPFN. Two-dimensional element and element-ratio diagrams showed substantial overlap among deposit types, whereas machine-learning models better captured deposit-type-related multielement patterns in the compiled dataset. TabPFN achieved the highest stratified 5-fold cross-validation performance, with an accuracy of 0.957 ± 0.011 and a macro-F1 score of 0.944 ± 0.012, followed by TabMap and XGBoost. SHAP and TabMap-SHAP interpretations suggest that deposit classification is mainly associated with coupled variations in REE-Y, Eu, HFSE, and Th-U systematics rather than with a single diagnostic element. These results indicate that titanite trace-element compositions may provide a useful quantitative and interpretable approach for deposit-type discrimination within compiled geochemical datasets, while broader application requires expanded standardized datasets and independent validation samples. Full article

Multisource remote sensing and Earth observation (EO) products provide scalable covariates for regional mineral prospectivity mapping, but their integration with incomplete and preferentially sampled occurrence records can produce biased prediction maps. We present a bias-aware machine learning workflow for porphyry copper prospectivity mapping that integrates satellite-derived alteration proxies, topographic variables, regional geology, structural context, and accessibility-related EO layers on a harmonized 1 km grid. The workflow separates remote sensing/geological predictors from survey-effort proxies and combines this decomposition with positive-unlabeled learning, stacked ensembling, rank-optimized blending, fold-wise calibration, and spatial block cross-validation. The case study covers the eastern Central Asian Orogenic Belt (CAOB) and uses porphyry Cu occurrences together with covariates derived from ASTER short-wave infrared information, Landsat 8 reflectance, SRTM topography, VIIRS night-time lights, GHSL population data, geological units, and active fault information. Across held-out spatial folds, the final RO-BAB ensemble provides a modest but exploration-relevant improvement in ranking relative to the all-covariate XGBoost baseline, increasing PR-AUC from 0.0297 to 0.0364 and recovering 26.75% of known deposits within the top 5% of ranked cells. The resulting maps delineate coherent remote sensing-supported prospective corridors while exposing regions where predictions may be influenced by historical accessibility and recording effort. The study demonstrates how machine learning that accounts for sampling bias can improve the reliability and interpretability of remote sensing mineral prospectivity products in the presence of only reference data. Full article

The mineral system model formalizes the critical geological processes and mappable parameters that control ore formation, which can then be translated into spatial predictors used as input features in machine learning (ML)-based mineral prospectivity mapping (MPM). In most MPM studies, exploration evidence features are indeed derived from the mineral system model of the targeted deposit type. However, not all features produced in this way are necessarily informative or favorable for prospectivity analysis. This challenge can be addressed by using feature selection frameworks to identify the most relevant features before applying ML and deep learning (DL) algorithms for mathematical integration. To address this need, this study employs an unsupervised variational autoencoder (VAE) framework to evaluate and rank exploration evidence layers. The VAE quantifies feature importance through a systematic strategy that measures the sensitivity of reconstruction-error components, mean squared error (MSE), mean absolute error (MAE), and Kullback–Leibler (KL) divergence, to individual feature variations. In this way, the VAE ranks the exploration features and helps to identify those that are the most useful for prospectivity mapping. The proposed approach was applied to a real geo-dataset from a porphyry copper district in Iran. Based on the conceptual model of porphyry copper mineralization, 15 evidence layers were generated, including proximity to phyllic, argillic, propylitic, iron oxide, and silicification alteration zones; proximity to intrusive rocks, faults, and fault intersections; and geochemical maps of Cu, Mo, Sb, Pb, Zn, As, and W. The VAE-based ranking indicated that evidence layers related to hydrothermal alterations, intrusive rocks, and faults were the most influential exploration features, whereas geochemical evidence layers showed lower relative importance. Based on this evaluation, two modeling scenarios were considered: in the first, all available features were used, and in the second, only the features selected by the VAE framework were included. In both cases, the final prospectivity model was produced by an autoencoder (AE). For comparison, the prediction-area (P–A) plots of the two prospectivity models were generated using 14 known mineral occurrences as positive ground-truth labels, indicating that the model based on the selected features achieved a higher prediction rate (80%) than the model based on all features (72%). These results demonstrate that the evidence layers derived from the mineral system approach can benefit from unsupervised VAE-based evaluation, leading to improved performance of the prospectivity modeling. Full article

Hydrothermal alteration mapping is a critical component of porphyry copper exploration because alteration assemblages provide important vectors toward mineralization. This study presents a systematic evaluation of supervised machine learning algorithms for delineating hydrothermal alteration zones using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) short-wave infrared (SWIR) surface reflectance data (AST_07XT). The investigation focuses on the Nain region within the central Urumieh–Dokhtar Magmatic Arc (UDMA), Iran, a major metallogenic belt hosting numerous porphyry copper systems. Representative spectral endmembers corresponding to Al–OH-bearing and Mg–OH-bearing hydrothermal alteration minerals were extracted using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-dimensional visualization techniques. These endmembers were subsequently used to train and evaluate a comprehensive suite of supervised machine learning classifiers, including linear, kernel-based, tree-based, ensemble, probabilistic, boosting, and neural-network algorithms for pixel-wise hydrothermal alteration mapping. Model performance was evaluated using multiple statistical metrics, including overall accuracy (OA), average accuracy (AA), precision, recall, F1-score, Cohen’s kappa coefficient, area under the ROC curve (AUC), spatial cross-validation accuracy, uncertainty analysis, and spatial agreement analysis. Among the evaluated classifiers, SVM_Linear, SVM_RBF, LDA, and MLP achieved the highest classification performance, with overall accuracies exceeding 94% and strong spatial consistency between classified maps. The resulting alteration maps display spatially coherent distributions of Al–OH and Mg–OH minerals that are consistent with established hydrothermal alteration zoning models in porphyry–epithermal systems. The mapped hydrothermal alteration zones show strong spatial correspondence with known mineralized areas and alteration patterns within the Urumieh–Dokhtar Magmatic Arc, confirming the geological reliability of the classification results. Uncertainty analysis further indicates high model confidence across most alteration zones, with higher uncertainty values mainly restricted to transitional and spectrally heterogeneous regions. The results demonstrate that integrating ASTER SWIR imagery with supervised machine learning algorithms provides a robust, scalable, and transferable framework for regional-scale hydrothermal alteration mapping and mineral exploration in porphyry copper provinces. Full article

Identifying mineral deposits with minimal environmental impact requires the optimization of heterogeneous datasets. This study validates a rapid geospatial exploration methodology using statistical distances to identify patterns in limited raw data. The approach was applied to a Cu-Ag stratabound deposit in Tiltil, Metropolitan Region, Chile. The method consists of processing diverse spatial variables to generate similarity maps based on user-defined criteria, utilizing a statistical comparison of variable distributions between known mineralized zones, such as El Olivo, Esmeralda, and El Manzano, and unexplored areas. Results demonstrate that the application of statistical distances effectively delineates high-probability mineralization zones, where all 12 generated targets coincided with previously documented mineralized bodies. Specifically, the Total Variation Distance (TVD) yielded the highest precision and contrast for target discrimination. This methodology proves effective for small-scale mining exploration and is potentially adaptable to copper porphyry systems at district and regional scales, significantly optimizing resource allocation in early-stage exploration. Full article

Porphyry copper deposits are one of the main sources of copper in the world and are usually associated with intrusive complexes of calc-alkaline composition formed in subduction-related geodynamic settings. Eastern Kazakhstan is characterized by the presence of a number of large porphyry copper-molybdenum deposits, but the metallogenic potential of many intrusive complexes in the region remains insufficiently studied. This paper presents new geological, mineralogical, and geochemical data on the Bala-Urpek deposit, located within the Sarykol intrusive complex (Eastern Kazakhstan), aimed at identifying diagnostic criteria for porphyry copper mineralization. The present study is based on field geological observations, petrographic analysis, and whole-rock geochemical data obtained by XRF and ICP-MS. Intrusive ore-bearing rocks are mainly represented by granitoids of the calc-alkaline series with I-type geochemical characteristics. Mineralogical studies have revealed veinlet-disseminated sulphide mineralisation, represented mainly by chalcopyrite and pyrite, as well as the development of hydrothermal alteration associations typical of porphyry systems. The geochemical characteristics of the rocks, including enrichment with large-ion lithophile elements and depletion with high-charge elements, indicate the subduction nature of magmatism. The combination of the data obtained allows us to identify geological, mineralogical and geochemical criteria characteristic of copper-porphyry systems and indicates the potential of the Bala-Urpek deposit area and the associated apophyses of the Sarykol complex for further exploration and prospecting. Full article

Accurate grade estimation in heterogeneous porphyry copper deposits is frequently constrained by spatial non-stationarity and the excessive smoothing inherent in traditional geostatistical methods. This study introduces the Geological Distance Field-Machine Learning (GDF-ML) framework, which transforms raw spatial coordinates into a geological coordinate system defined by the structural architecture. By mapping grade distribution within this geologically informed space, the framework enables machine learning models to discern non-linear mineralizing patterns that are typically obscured in traditional Euclidean 3D space. Functioning as an expert-constrained regression architecture rather than a purely data-driven interpolator, the framework estimates grade distributions conditional upon established metallogenic controls. In this context, the achieved spatial separation cross-validation R² of 0.851 quantifies the proportion of grade variance structurally explainable by the geological architecture, highlighting the workflow’s capacity to distinguish continuous structural trends from localized random variability. Industrial reconciliation against high-density production data confirms this performance, demonstrating an average grade bias of only 0.79%, compared to 9.68% achieved by Ordinary Kriging. Furthermore, SHAP analysis verifies that these predictions are systematically driven by the non-linear relationship between structural proximity and mineralization. Consequently, this study suggests that incorporating structural distance metrics into regression workflows offers an alternative approach to evaluate the geometric constraints of geological features alongside the localized variability of porphyry mineralization. Full article

The giant Dulong Sn-Zn polymetallic deposit, located in the eastern part of the southeastern Yunnan metallogenic belt, is a world-class ore system. Despite extensive research on the source of tin and its mineralization processes, the specific ore-related intrusion and the source of copper remain highly debated. Recent deep exploration has revealed a deep-seated granite porphyry, yet its geochronological and geochemical characteristics, along with its genetic link to mineralization, are poorly constrained. This study presents new zircon U-Pb age, whole-rock geochemistry, and Sr-Nd-Hf isotopic compositions of this granite porphyry, integrated with a regional comparison to multi-phase Laojunshan granites. LA-ICP-MS zircon U-Pb dating yields a Late Cretaceous age of 85.1 ± 1.2 Ma. The Dulong granite porphyry is strongly peraluminous and high-K calc-alkaline to shoshonitic, exhibiting typical S-type granite affinities with enrichment in Rb, U, and Ta, as well as depletions in Ba, Sr, Nb, and Eu. Isotopic signatures (ε_Nd(t) = −12.5 to −12.0, t_DM2(Nd) = 1.87 to 1.91 Ga; zircon ε_Hf(t) = −10.24 to −1.44, t_DM2(Hf) = 1.24 to 1.79 Ga) suggest that the parental magma was derived from the partial melting of ancient crust, with possible minor input of mantle-derived components in an extensional tectonic setting. The Dulong granite porphyry represents a moderate-to-high temperature, reduced, and highly evolved magmatic system. Notably, its high concentrations of Sn, W, Zn, and Cu indicate that the parental melt was metal-rich, possessing potential for Sn and Cu mineralization. Accordingly, future exploration should prioritize areas characterized by well-developed granite porphyry dykes, skarn–wallrock contact zones, and deep-seated structural intersections. Full article

The Western Chagai Belt of Pakistan hosts major porphyry Cu-Au systems, but prospectivity mapping in this arc remains difficult because favorable lithology, intrusive bodies, fault corridors, hydrothermal alteration, and Cu geochemical anomalies are spatially heterogeneous across a structurally complex and arid terrain. These conditions create a scientific need for an integrated mapping framework that can combine remote sensing alteration evidence, geology, structure, and geochemistry within a unified and reproducible workflow. This study presents GeoHybridGNN, a hybrid deep learning framework for porphyry copper prospectivity mapping in the Western Chagai Belt. The framework integrates multi-source raster evidence, including remote sensing-derived spectral alteration indices, a Cu geochemical raster, and distance-to-fault information, with graph-based node representations that combine regular neighborhood adjacency on retained grid cells with node attributes derived from lithology and aligned geoscientific raster summaries. All predictors were harmonized to a common 30 m reference raster grid and evaluated using five-fold spatial block cross-validation to provide a more spatially realistic assessment than ordinary random splitting. The implemented model combines a CNN-based raster patch encoder with a GraphSAGE-based graph classifier. Raster patches extracted around graph nodes are encoded into 64-dimensional embeddings, and these embeddings are concatenated with node-level graph features before full-batch graph learning and prediction. Copper occurrences were used only for supervised label assignment and evaluation and were not used as predictive inputs. The results show that GeoHybridGNN produces spatially coherent prospectivity maps, stable fold-wise prediction patterns, and improved target delineation relative to the tested comparison models. Cu geochemical integration produces only a limited change in global discrimination but provides modest local target sharpening in selected zones. These results indicate that GeoHybridGNN can serve as an uncertainty-aware and geologically constrained decision support workflow for porphyry copper targeting. More broadly, the framework provides a transparent strategy for exploration screening in structurally complex and data-heterogeneous metallogenic belts where remote sensing, geological, structural, and geochemical evidence must be integrated consistently. Full article

Baixintan is a magmatic Ni–Cu sulfide deposit discovered in the central Tuwu–Yandong porphyry Cu belt of the Eastern Tianshan Orogen (ETO) of NW China in 2016. It is in close proximity (~5 km) to the Tuwu Cu deposit, the largest Carboniferous porphyry Cu deposit (~336 Ma) in Xinjiang. The Baixintan Ni–Cu ore is characterized by a high Cu/Ni ratio, but the reason for it remains unclear. To resolve this question, we present petrographic, geochronological, whole-rock geochemical, and S, Pb, Cu, and Lu–Hf isotopic data. Ore-related hornblende olivine websterite (HOW) and hornblende olivine gabbro (HOG) were emplaced at 296.6 ± 1.1 Ma and 289.7 ± 1.2 Ma, respectively, which were formed in an Early Permian post-collisional extensional setting. Whole-rock Pb and zircon Lu–Hf isotopes suggest that the parental magmas were predominantly mantle-derived. The Baixintan HOW and HOG contain abundant hornblende and are enriched in LILEs and LREEs but depleted in HFSEs, suggesting subduction-related metasomatism in their magma source. The sulfide (chalcopyrite, pyrrhotite, and pentlandite) δ³⁴S values cluster around 0‰ (–0.13 to 0.11, n = 11), and the chalcopyrite has uniformly negative but variable δ⁶⁵Cu values (–0.96 to –0.13, n = 6), which suggest that the ore-forming materials were mainly derived from the subduction-metasomatized mantle. Olivine Ni contents are significantly lower than those crystallized under sulfide-unsaturated conditions, implying that olivine fractional crystallization was an important trigger for sulfide melt segregation at Baixintan. Baixintan is the only known magmatic Ni–Cu sulfide deposit in the ETO that shares a Cu-preconcentrated, metasomatized mantle source with a giant porphyry Cu system. Copper pre-enrichment in the magma source may be the main factor for the relatively high Cu/Ni ratio observed in the Baixintan deposit. Full article

The Duolong porphyry copper–gold district, located in the northwestern segment of the Bangongco–Nujiang metallogenic belt on the southern margin of the South Qiangtang terrane (Tibet), hosts typical porphyry-style Cu-Au mineralization with well-defined alteration zoning from potassic through chlorite–sericite to propylitic assemblages. Based on integrated in situ major/trace element and sulfur isotope analyses of pyrite and chalcopyrite from different alteration zones, we identify two discrete episodes of magmatic-hydrothermal activity that exerted distinct controls on metal endowment. Sulfur isotope signatures define a systematic evolution from the earliest, high-temperature potassic stage (δ³⁴S: Py-I −3.70 to −1.16‰, mean −2.14‰; Cp-I −4.92 to −0.90‰, mean −2.54‰) through propylitic alteration (Py-II: 1.20‰–5.16‰, mean 3.06‰) to the later chlorite–sericite stage (Py-III: −2.00 to 1.86‰, mean 0.06‰; Cp-II: −2.50 to 0.58‰, mean −0.77‰), tracking progressive fluid cooling and changing fluid sources. Trace element systematics further discriminate these episodes: sulfides from potassic and chlorite–sericite zones are enriched in trace elements, whereas propylitic pyrite is depleted, with potassic pyrite recording the highest Cu concentrations (559–7256 ppm, mean 2302 ppm) and chlorite–sericite pyrite containing the lowest Au contents (0.01–0.59 ppm, mean 0.10 ppm). Gold mineralization occurs as native gold exsolved from chalcopyrite, and the markedly low Au concentrations in chlorite–sericite pyrite (0.01–0.59 ppm, mean 0.10 ppm) demonstrate that gold exsolution was largely completed during the first, high-temperature magmatic-hydrothermal stage. Collectively, these results establish a detailed geochemical framework linking sulfide composition to specific hydrothermal stages, providing new constraints on the processes of porphyry copper–gold mineralization in a collisional setting. Full article

The Zhongdian Arc is an important copper polymetallic ore cluster in China’s Sanjiang Tethyan Metallogenic Domain, and the Langdu deposit is a representative porphyry–skarn Cu deposit in this region. This study aims to constrain the timing of magmatic activity at the Langdu deposit. It also seeks to reveal the magma’s physical–chemical properties and evolution, and to identify the factors controlling mineralization. To achieve these objectives, this study used LA-ICP-MS zircon U-Pb dating and elemental analysis, combined with halogen and trace element data from apatite. Zircon U–Pb dating shows that the Langdu intrusions were emplaced at ca. 216 Ma in a continental arc setting associated with the westward subduction of the Garzê–Litang oceanic crust during the Late Triassic. Geochemical and mineralogical features indicate that the Langdu intrusions are I-type granite. They originated from partial melting of the mantle wedge metasomatized by subduction fluids. During their ascent, these magmas experienced fractional crystallization dominated by amphibole, titanite, rutile, and monazite. Geochemical records from zircon and apatite further reveal that the ore-forming magma of the Langdu intrusions exhibited high oxygen fugacity (ΔFMQ = +1.53), elevated H₂O content (avg. 7.63 wt.%), and enrichment in S (avg. 560 ppm) and Cl (avg. 2141 ppm). This Cl-rich magma experienced fluid exsolution during its early evolutionary stage. This provided the necessary conditions for metal extraction and transport. In summary, the key factors controlling the formation of the Langdu porphyry–skarn Cu deposit are high-oxygen-fugacity magma enriched in water and volatiles (S and Cl), coupled with efficient fluid exsolution. This understanding is important for better understanding regional metallogeny and for guiding mineral exploration. Full article

Depletion of reserves of rich copper–porphyry ore deposits necessitates the development of highly efficient methods for Mo (VI) extraction from complex, corrosive hydro-metallurgical media. The present study undertakes a comprehensive assessment of sorptive concentration of Mo (VI) from strongly acidic sulfate solutions (120 g/L H₂SO₄) by employing a spectrum of commercially available strong- and weak-base anion-exchange resins. It has been established that the macroporous weak-base anion exchanger Purolite A-100 demonstrates decisive superiority over gel-type analogs (Lewatit M-800, AB-17), facilitating unimpeded intra-gel diffusion of bulky molybdenyl sulfato-complexes anions, thereby circumventing the obstructive “sieve effect.” Thermodynamic and kinetic investigations revealed that the sorption process exhibits pronounced concentration- and pH-dependent characteristics. Peak extraction efficiency (up to 95.91%) is achieved at pH ≈ 1, a finding that correlates with the region of maximal protonation of tertiary amino groups within the resin matrix. Kinetic acceleration of mass transfer upon heating to 80 °C has been experimentally confirmed, yielding 94.6% extraction within 60 min. The obtained results corroborate the prospective integration of macroporous weak-base anion exchangers into operational hydro-metallurgical schemes as an environmentally benign and efficacious alternative to conventional solvent extraction of molybdenum. Full article