Search Results (80)

Thermodynamic simulations of fluid–rock interactions provide valuable insights into mineral deposit formation mechanisms. This study investigates the Pulang porphyry Cu-Au deposit in the Sanjiang Tethys Orogen, employing both Gibbs energy minimization (GEM) and the Law of mass action (LMA) method to understand alteration overprinting and metal precipitation. The modeling results suggest that the ore-forming fluid related to potassic alteration was initially oxidized (ΔFMQ = +3.54~+3.26) with a near-neutral pH (pH = 5.0~7.0). Continued fluid–rock interactions, combined with the input of reduced groundwater, resulted in a decrease in both pH (4.8~6.1) and redox potential (ΔFMQ~+1), leading to the precipitation of propylitic alteration minerals and pyrrhotite. As temperature further decreased, fluids associated with phyllic alteration showed a slight increase in pH (5.8~6.0) and redox potential (ΔFMQ = +2). The intense superposition of propylitic and phyllic alteration on the potassic alteration zone is attributed to the rapid temperature decline in the magmatic–hydrothermal system, triggering fluid collapse and reflux. Mo, mainly transported as HMoO₄⁻ and MoO₄⁻², precipitated in the high-temperature range; Cu, carried primarily by CuCl complexes (CuCl₄⁻³, CuCl₂⁻, CuCl), precipitated over intermediate to high temperatures; and Au, transported as Au-S complexes (Au(HS)₂⁻, AuHS), precipitated from intermediate to low temperatures. This study demonstrates that fluid–rock interactions alone can account for the observed sequence of alteration and mineralization in porphyry systems. Full article

The Aït Abdellah copper deposit in the Bou Azzer-El Graara inlier of the Moroccan Anti-Atlas provides key insights into structurally and lithologically controlled mineralization in Precambrian terranes. The deposit is hosted in feldspathic sandstones of the Tiddiline Group, which unconformably overlie the Bou Azzer ophiolite, and is spatially associated with a NE–SW-trending shear zone. This zone is characterized by mylonitic fabrics, calcite veining, and an extensive network of fractures, reflecting a two-stage deformation history involving early ductile shearing followed by brittle faulting and brecciation. These structural features enhanced rock permeability, enabling fluid flow and metal precipitation. Copper mineralization includes primary sulfides such as chalcopyrite, bornite, pyrite, chalcocite, digenite, and covellite, as well as supergene minerals like malachite, azurite, and chrysocolla. Sulfur isotope values (δ³⁴S = +5.9% to +22.8%) indicate a mixed sulfur source, likely derived from both ophiolitic rocks and volcano-sedimentary sequences. Carbon and oxygen isotope data suggest fluid interaction with marine carbonates and meteoric waters, potentially linked to post-Snowball Earth deglaciation processes. Fluid inclusion studies reveal homogenization temperatures ranging from 195 °C to 310 °C and salinities between 5.7 and 23.2 wt.% NaCl equivalent, supporting a model of fluid mixing between magmatic-hydrothermal and volcano-sedimentary sources. The paragenetic evolution of the deposit comprises three stages: (1) early hydrothermal precipitation of quartz, dolomite, sericite, pyrite, and early chalcopyrite and bornite; (2) a main mineralizing stage characterized by fracturing and deposition of bornite, chalcopyrite, and Ag-bearing sulfosalts; and (3) a late supergene phase with oxidation and secondary enrichment. The Aït Abdellah deposit is best classified as a shear zone-hosted copper system with a complex, multistage mineralization history. The integrated analysis of structural features, mineral assemblages, isotopic signatures, and fluid inclusion data reveals a dynamic interplay between deformation processes, hydrothermal alteration, and evolving fluid sources. 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

With the rapid development of big data and artificial intelligence technologies, the era of Industry 4.0 has driven large open-pit mines towards digital and intelligent transformation. This is particularly true in mature mining areas such as the Yanshan Iron Mine, where the depletion of shallow proven reserves and the increasing issues of mixed surrounding rocks with shallow ore bodies make it increasingly important to build intelligent mines and implement green and sustainable development strategies. However, previous mineralization predictions for the Yanshan Iron Mine largely relied on traditional geological data (such as blasting rock powder, borehole profiles, etc.) exploration reports or three-dimensional explicit ore body models, which lacked precision and were insufficient to meet the requirements for intelligent mine construction. Therefore, this study, based on artificial intelligence technology, focuses on geoscience big data mining and quantitative prediction, with the goal of achieving multi-scale, multi-dimensional, and multi-modal precise positioning of the Yanshan Iron Mine and establishing its intelligent mine technology system. The specific research contents and results are as follows: (1) This study collected and organized multi-source geoscience data for the Yanshan Iron Mine, including geological, geophysical, and remote sensing data, such as mine drilling data, centimeter-level drone image data, and high-spectral data of rocks and minerals, establishing a rich mine big data set. (2) SOM clustering analysis was performed on the elemental data of rock and mineral samples, identifying key elements positively correlated with iron as Mg, Al, Si, S, K, Ca, and Mn. TSG was used to interpret shortwave and thermal infrared hyperspectral data of the samples, identifying the main alteration mineral types in the mining area. Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. This model was then applied to centimeter-level drone images, achieving high-precision intelligent identification of magnetite in the mining area. Combined with LiDAR image elevation data, a real-time three-dimensional surface mineral monitoring model for the mining area was built. (4) The Bagged Positive Label Unlabeled Learning (BPUL) method was adopted to integrate five evidence maps—carbonate alteration, chloritization, mixed rockization, fault zones, and magnetic anomalies—to conduct three-dimensional mineralization prediction analysis for the mining area. The locations of key target areas were delineated. The SHAP index and three-dimensional explicit geological models were used to conduct an in-depth analysis of the contributions of different feature variables in the mineralization process of the Yanshan Iron Mine. In conclusion, this study successfully constructed the technical framework for intelligent mine construction at the Yanshan Iron Mine, providing important theoretical and practical support for mineralization prediction and intelligent exploration in the mining area. Full article

Models of subsurface structures are important for successful deposit exploration, but are challenged by the need to integrate data from different geophysical methods. In the present study, we evaluated a method of joint inversion in which audio-magneto telluric (AMT) and dual frequency induced polarization (DFIP) data sets are inverted simultaneously to produce a consistent 2D resistivity model to show a clear image of subsurface structures. To achieve the objectives, we conducted AMT and DFIP surveys along the same survey line within the Dongjun lead–zinc deposit in inner Mongolia by measuring 31 AMT survey sites with a station spacing of 40 m on a 1440 m survey track and operated in fifty-three frequencies in the range of 1–10,400 Hz to record the resistivity distribution of subsurface to depths exceeding 800 m. The same survey setup up was applied to the DFIP method using a pole–dipole array configuration and operating frequencies of 4 Hz and 4/13 Hz. The two-dimensional (2D) model obtained from AMT data revealed distinct low-resistivity anomalies in the middle of the 2D inversion model. In contrast, the DFIP inversion model showed a high resistive body in the same region with relatively high percent frequency effect (PFE) indicating high chargeability. In response to the discrepancies observed in the separate 2D inversion models, we implemented a joint inversion for both the AMT and DFIP data sets. The joint inversion resistivity model shows surficial conducting bodies and a high conductive body along the profile with relatively high PFE, indicating high chargeability. The final joint inversion resistivity model clearly images the large silica alteration zone and the Pb-Zn mineralization. This study demonstrates the feasibility of a joint inversion methodology and highlights the value of integrating geophysical methods through joint inversion for enhanced characterization and exploration of lead–zinc ores. Full article

Seafloor hydrothermal vent areas are potential sources of polymetallic sulfide deposits and exhibit distinct mineralization structures under different tectonic settings. The Daxi Vent Field (DVF), located on the Carlsberg Ridge in the northwestern Indian Ocean, represents a basalt-hosted hydrothermal system. To investigate the alteration zone structure of the DVF, high-resolution near-bottom bathymetric and magnetic data were collected during the Chinese DY57 expedition in 2019. Based on the results of magnetic anomaly data processing, including reduction to a level surface and Euler deconvolution, the location and depth of the magnetic sources were identified. In addition, two 2.5D magnetic forward models crossing the active and inactive vent fields were constructed. The results indicate that the range of the alteration zone in the active vent at the DVF extends up to 120 m in width and 80 m in depth, while the hydrothermal deposit at the extinct vent on the northeastern side extends up to 220 m along the ridge axis with a thickness of 30 m. Full article

Characterizing alteration and its geochemical signature provides critical information relevant to ore-deposit genesis and its related footprint; for porphyry-type deposits, zoned potassic-phyllic-propylitic alteration and metal enrichment are critical features. Here we integrate earlier lithological and mineralogical studies of the (10+ Moz Au) Archean Côté Gold porphyry-type Au(-Cu) deposit (Ontario, Canada) with identified alteration types to provide exploration vectors. The ca. 2740 tonalite-quartz diorite-diorite intrusive complex and co-temporal Au(-Cu) mineralization as disseminations, breccias and veins are co-spatial with ore-related alteration types (amphibole, biotite, muscovite). An early, locally developed amphibole event coring the deposit is followed by emplacement of a Au(-Cu) mineralized biotite-rich magmatic-hydrothermal breccia body and broad halo of disseminated biotite and quartz veining. These rocks record gains via mass balance calculations of K, Fe, Mg, LILE, and LREE with Au, Cu, Mo, Ag, Se and Bi. Later muscovite alteration is enriched in K, Rb, Cs, Ba, CO₂, and LOI with varied Au, Cu, Mo, Te, As, and Bi values. A strong albite overprint records extreme Na gains with the loss of most other elements, including ore metals (i.e., Au, Cu). Together these data define an Au-Cu-Mo-Ag-Te-Bi-Se core co-spatial with biotite breccia versus a peripheral stockwork and sheeted vein zone with a Te-Se-Zn-Pb-As association. These features further support the posited porphyry-type model for the Côté Gold Au(-Cu) deposit. Full article

Continuous loading on asphalt pavements induces fatigue damage at the interface between the asphalt binder and aggregate or within the binder itself. The understanding of asphalt’s fatigue response is considered crucial for the prolongation of pavement service life. Variable stress fatigue tests were conducted on asphalt binders, with conditions such as stress amplitude being altered to analyze fatigue performance and life. This study refines asphalt fatigue evaluation systems, introducing a variable stress time sweep test. Modulus recovery after stress changes was revealed through rheological analysis, indicating damage recovery. Fracture surface analysis showed that increased high–stress loadings resulted in reduced edge flow zone width and a flatter surface. Statistical analysis indicated an “exercise effect”, enhancing fatigue life in the second stage. Stress transitions altered fatigue crack paths, surpassing Miner’s linear criterion prediction. The fatigue life curve was accurately fitted using the two–stage life model, affirming its applicability in evaluating variable stress fatigue tests. Full article

The Drake Goldfield, also known as Mount Carrington, is located in north-eastern New South Wales, Australia. It contains a number of low–intermediate-sulfidation epithermal precious metal deposits with a current total resource of 724.51 metric tons of Ag and 10.95 metric tons of Au. These deposits occur exclusively within the Drake Volcanics, a 60 × 20 km NW-SE trending sequence of Late Permian volcanics and related epiclastics. Drilling of the Copper Deeps geochemical anomaly suggests that the volcanics are over 600 m thick. The Drake Volcanics are centered upon a geophysical anomaly called “the Drake Quiet Zone” (DQZ), interpreted to be a collapsed volcanic caldera structure. A total of 105 fresh carbonate samples were micro-drilled from diamond drillcores from across the field and at various depths. A pXRD analysis of these carbonates identified five types as follows: ankerite, calcite, dolomite, magnesite, and siderite. Except for three outlier values (i.e., −21.32, −19.48, and 1.42‰), the δ¹³C_VPDB generally ranges from−15.06 to −5.00‰, which is less variable compared to the δ¹⁸O_VSMOW, which varies from −0.92 to 17.94‰. μ-XRF was used to analyze the elemental distribution, which indicated both syngenetic/epigenetic relationships between calcite and magnesite. In addition, a total of 53 sulfide samples (primarily sphalerite and pyrite) from diamond drillcores from across the Drake Goldfield were micro-drilled for S isotope analysis. Overall, these have a wide range in δ³⁴S_CDT values from −16.54 to 2.10‰. The carbon and oxygen isotope results indicate that the fluids responsible for the precipitation of carbonates from across the Drake Goldfield had complex origins, involving extensive mixing of hydrothermal fluids from several sources including those of magmatic origin, meteoric fluids and fluids associated with low-temperature alteration processes. Sulfur isotope ratios of sulfide minerals indicate that although the sulfur was most likely derived from at least two different sources; magmatic sulfur was the dominant source while sedimentary-derived sulfur was more significant for the deposits distal from the DQZ, with the relative importance of each varying from one deposit to another. Our findings contribute to a greater understanding of Au-Ag formation in epithermal environments, particularly in collapsed calderas, enhancing exploration strategies and models for ore deposition. Full article

This study employs multi-source remote sensing information and machine learning methods to comprehensively assess the geological background, structural features, alteration anomalies, and spectral characteristics of the Malkansu Manganese Ore Belt in Xinjiang. Manganese mineralization is predicted, and areas with high mineralization potential are delineated. The results of the feature factor weight analysis indicate that structural density and lithological characteristics contribute most significantly to manganese mineralization. Notably, linear structures are aligned with the direction of the manganese belt, and areas exhibiting high controlling structural density are closely associated with the locations of mineral deposits, suggesting that structure plays a crucial role in manganese production in this region. The Area Under the Curve (AUC) values for the Random Forest (RF), Naïve Bayes (NB), and eXtreme Gradient Boosting (XGBoost) models were 0.975, 0.983, and 0.916, respectively, indicating that all three models achieved a high level of performance and interpretability. Among these, the NB model demonstrated the highest performance. By algebraically overlaying the predictions from these three machine learning models, a comprehensive mineralization favorability map was generated, identifying 11 prospective mineralization zones. The performance metrics of the machine learning models validate their robustness, while regional tectonics and stratigraphic lithology provide valuable characteristic factors for this approach. This study integrates multi-source remote sensing information with machine learning methods to enhance the effectiveness of manganese prediction, thereby offering new research perspectives for manganese forecasting in the Malkansu Manganese Ore Belt. Full article

Sinongduo is the first low-sulfidation epithermal deposit to be found in the Gangdese metallogenic belt, Xizang, China. Through the integration of Shortwave Infrared (SWIR) spectroscopy, an electron probe microanalysis (EPMA), X-ray diffraction (XRD), and three-dimensional modeling, the following findings were obtained: (1) The alteration minerals in the belt predominantly belong to the white mica group, with mineral assemblages of muscovite → muscovite + illite → muscovite + illite + montmorillonite → muscovite + paragonite + montmorillonite → muscovite + paragonite + illite + montmorillonite from the outer edges to the ore zones. (2) On the basis of the number of interlayer cations, the white mica-group minerals were classified into three distinct types. (3) The SWIR spectral exploration model indicates the spatial distribution of the mineral combinations and their spectral characteristics in the low-sulfidation epithermal deposit. On the basis of these results, we predict that Woruo, an area located north of Sinongduo, has immense potential for future mineral exploration. Full article

In the Bonan area, the lower fourth member of the Shahejie Formation (Es4x) is buried beneath a sedimentary pile ranging from 2500 to 5000 m. Understanding the impact of diagenetic alterations on these deeply buried reservoirs is crucial for effective hydrocarbon exploration and production. This study employs a terminal fan sedimentation model, encompassing depositional environments such as feeder channels, distributary channels, floodplains, and basinal zones, to provide insights into the spatial distribution of reservoir properties and their influence on the localization of optimal reservoirs within the sag. The analysis integrates diagenetic facies with well log responses, subsurface porosity trends, and permeability variations across the formation. The petrographic analysis indicates that the sandstone is composed primarily of litharenite, feldspathic litharenite, lithic arkose, and minor amounts of arkose. The dominant clay cement is illite, accompanied by mixed-layer smectite/illite, chlorite, and kaolinite. Thin section observations reveal secondary porosity formed through the dissolution of quartz grains, volcanic rock fragments, and feldspar, along with their associated cements. These sandstones exhibit relatively good sorting, with average porosity and air permeability values of 14.01% and 12.73 mD, respectively. Diagenetic alterations are categorized into three processes: porosity destruction, preservation, and generation. Key diagenetic mechanisms include compaction, cementation, replacement, and dissolution, with compaction exerting the most significant control on reservoir porosity reduction. Statistical analysis indicates that the average porosity loss due to compaction is approximately 13.3%, accounting for about 38% of the original porosity. The detrital rock cement predominantly comprises quartz (42%), feldspar (32%), clay minerals (14%), and carbonate (12%). Under the prevailing depositional conditions, porosity is enhanced by dissolution and fracturing, while late-stage diagenetic cementation by clay and carbonate minerals—excluding chlorite—adversely affects reservoir quality. Consequently, the distributary zone is identified as the primary target for exploration. Full article

The Biggenden gold-bearing Fe skarn deposit in southeast Queensland, Australia, is a calcic magnetite skarn that has been mined for Fe and gold (from the upper portion of the deposit). Skarn has replaced volcanic and sedimentary rocks of the Early Permian Gympie Group, which formed in different tectonic settings, including island arc, back arc, and mid-ocean ridge. This group has experienced a hornblende-hornfels grade of contact metamorphism due to the intrusion of the Late Triassic Degilbo Granite. The intrusion is a mildly oxidized I-type monzogranite that has geochemical characteristics intermediate between those of granitoids typically associated with Fe-Cu-Au and Sn-W-Mo skarn deposits. The skarn mineralogy indicates that there was an evolution from prograde to various retrograde assemblages. Prograde garnet (Adr_11-99Grs_1-78Alm_0-8Sps_0-11), clinopyroxene (Di_30-92Hd_7-65Jo_0-9), magnetite, and scapolite formed initially. Epidote and Cl-bearing amphibole (mainly ferropargasite) were the early retrograde minerals, followed by chlorite, calcite, actinolite, quartz, and sulfides. Late-stage retrograde reactions are indicated by the development of nontronite, calcite, and quartz. Gold is mainly associated with sulfide minerals in the retrograde sulfide stage. The fluids in equilibrium with the ore-stage calcites had δ¹³C and δ¹⁸O values that indicate deposition from magmatically derived fluids. The calculated δ¹⁸O values of the fluids in equilibrium with the skarn magnetite also suggest a magmatic origin. However, the fluids in equilibrium with epidote were a mixture of magmatic and meteoric water, and the fluids that deposited chlorite were at least partly meteoric. δD values for the retrograde amphibole and epidote fall within the common range for magmatic water. Late-stage chlorite was deposited from metasomatic fluids depleted in deuterium (D), implying a meteoric water origin. Sulfur isotopic compositions of the Biggenden sulfides are similar to other skarn deposits worldwide and indicate that sulfur was most probably derived from a magmatic source. Based on the strontium (⁸⁷Sr/⁸⁶Sr) and lead (²⁰⁶Pb/²⁰⁴Pb, ²⁰⁷Pb/²⁰⁴Pb and ²⁰⁸Pb/²⁰⁴Pb) isotope ratios, the volcanic and sedimentary rocks of the Gympie Group may have contributed part of the metals to the hydrothermal fluids. Lead isotope data are also consistent with a close age relationship between the mineralization at Biggenden and the crystallization of the Degilbo Granite. Microthermometric analysis indicates that there is an overall decrease in fluid temperature and salinity from the prograde skarn to retrograde alterations. Fluid inclusions in prograde skarn calcite and garnet yield homogenization temperatures of 500 to 600 °C and have salinities up to 45 equivalent wt % NaCl. Fluid inclusions in quartz and calcite from the retrograde sulfide-stage homogenized between 280 and 360 °C and have lower salinities (5–15 equivalent wt % NaCl). In a favored genetic model, hydrothermal fluids originated from the Degilbo Granite at depth and migrated through the shear zone, intrusive contact, and permeable Gympie Group rocks and leached extra Fe and Ca and deposited magnetite upon reaction with the adjacent marble and basalt. Full article

New data, including Raman spectroscopy, characterize unusual mineral assemblages from rocks of the Naylga and Khamaryn–Khyral–Khiid combustion metamorphic complexes in Mongolia. Several samples of melilite–nepheline paralava and other thermally altered (metamorphosed) sedimentary rocks contain troilite (FeS), metallic iron Fe⁰, kamacite α-(Fe,Ni) or Ni-bearing Fe⁰, taenite γ-(Fe,Ni) or Ni-rich Fe⁰, barringerite or allabogdanite Fe₂P, schreibersite Fe₃P, steadite Fe₄P = eutectic α-Fe + Fe₃P, wüstite FeO, and cohenite Fe₃C. The paralava matrix includes a fragment composed of magnesiowüstite–ferropericlase (FeO–MgO solid solution), as well as of spinel (Mg,Fe)Al₂O₄ and forsterite. The highest-temperature mineral assemblage belongs to a xenolithic remnant, possibly Fe-rich sinter, which is molten ash left after underground combustion of coal seams. The crystallization temperatures of the observed iron phases were estimated using phase diagrams for the respective systems: Fe–S for iron sulfides and Fe–P ± C for iron phosphides. Iron monosulfides (high-temperature pyrrhotite) with inclusions of Fe⁰ underwent solid-state conversion into troilite at 140 °C. Iron phosphides in inclusions from the early growth zone of anorthite–bytownite in melilite–nepheline paralava crystallized from <1370 to 1165 °C (Fe₂P), 1165–1048 °C (Fe₃P), and <1048 °C (Fe₄P). Phase relations in zoned spherules consisting of troilite +Fe⁰ (or kamacite + taenite) +Fe₃P ± (Fe₃C, Fe₄P) reveal the potential presence of a homogeneous Fe–S–P–C melt at T~1350 °C, which separated into two immiscible melts in the 1350–1250 °C range; namely, a dense Fe–P–C melt in the core and a less dense Fe–S melt in the rim. The melts evolved in accordance with cooling paths in the Fe–S and Fe–P–C phase diagrams. Cohenite and schreibersite in the spherules crystallized between 988 °C and 959 °C. The crystallization temperatures of minerals were used to reconstruct redox patterns with respect to the CCO, IW, IM, and MW buffer equilibria during melting of marly limestone and subsequent crystallization and cooling of melilite–nepheline paralava melts. The origin of the studied CM rocks was explained in a model implying thermal alteration of low-permeable overburden domains in reducing conditions during wild subsurface coal fires, while heating was transferred conductively from adjacent parts of ignited coal seams. The fluid (gas) regime in the zones of combustion was controlled by the CCO buffer at excess atomic carbon. Paralava melts exposed to high-temperature extremely reducing conditions contained droplets of immiscible Fe–S–P–C, Fe–S, Fe–P, and Fe–P–C melts, which then crystallized into reduced mineral assemblages. Full article

Porphyry and the associated skarn-type deposit is one of the most important types of ore deposits worldwide, which usually exhibit significant zoning of mineralization-alteration, but the research on element migration in these mineralization-alteration zones is relatively weak. The Beiya porphyry–skarn gold-polymetallic deposit is a super-large Cenozoic deposit located in the Sanjiang metallogenic belt, northwestern Yunnan, China. In this paper, through a detailed analysis of mineralization and alteration zoning and its element migration regularity, the findings are as follows: (1) Three types of hydrothermal alteration—porphyry alteration, contact alteration, and wall-rock alteration—are developed, and porphyry alteration includes potassic, phyllic, propylitic, and argillic alteration; (2) five types of mineralization—porphyry-type Cu–Au–(Mo), skarn-type Au–Fe–(Cu), hydrothermal vein-type Au–Fe, distal hydrothermal-type Pb-polymetallic, and oxidizing-leaching enriched-type Au—occur in a diversity of forms, which are dominantly controlled by structures and lithologies; (3) concentric-banded mineralization-alteration zones are exhibited centrally from the alkaline porphyry outward or upward, namely [porphyry alteration] potassic → phyllic → propylitic → argillic → [contact alteration] skarnitization–marbleization → [wall-rock alteration] marbleization–silicification–calcitization; (4) porphyry-type mineralization predominantly forms within potassic and phyllic zones, while skarn-type mineralization occurs in contact alteration zones, and proximal and distal hydrothermal (vein)-type mineralization are commonly distributed in marbleization–silicification–calcitization alteration zones; and (5) element migration analysis demonstrates a significantly lateral and vertical zoning in the metallogenic element association of Cu–Mo → Cu–Au → Au–Fe–Cu → Au–Fe → Pb–Zn–Au–Ag–Fe from alkaline porphyry outward to the wall-rock. The mineralization-alteration zoning model indicates the Beiya deposit has similar mineralization and alteration zone characteristics to the typical porphyry copper system; and element migration within mineralization-alteration zones provides new scientific information for understanding the metallogenic regularity and prospecting at Beiya, as well as the similar types of deposits in the Sanjiang metallogenic belt and elsewhere in the world. Full article