Deposits of the Udokan-Chineysky ore-magmatic system of Eastern Siberia

The aggregate of ore deposits localized in the Udokan-Chineysky ore district is unique and is the result of multi–stage, polygenetic formation. The deposits of copper and other metals formed at various depths occur within a limited area. The oxide and sulfide ore are spatially associated in the sedimentary rocks pertaining to the Paleoproterozoic Udokan Supergroup and the intrusive mafic–ultramafic rocks of the Chineysky Complex. The granite rocks of the Kodar Complex and gabbro rocks of the Chineysky Complex also date back to Paleoproterozoic. The same age has been established for metasomatic Nb–Ta–Zr–REE–Y and U mineralization in the albitized terrigenous rocks of the Udokan Supergroup (Katugin deposit and Chitkanda prospect) and U–Pd prospects hosted in terrigenous rocks. The U–REE mineralization superposed on the titanomagnetite deposits in the Chineysky pluton has not analogues in the world’s practice. The occurrences of uranium mineralization have been noted in form of pitchblende and U–Th rims around chalcopyrite grains at the Unkur copper deposit hosted in sedimentary rocks. The enrichment in U and Pb has been documented in crosscutting quartz veinlets with bornite mineralization at the Udokan deposit.

A new global optimum solution model based on multi-value dynamic programming for the cut-off grade optimization in a surface mining complex

In techno-economic concern, cut-off grade (COG) optimization is the key for efficient mineral liquidation from thehuge metalliferous surface mining sector. In this paper, a sequentially advancing algorithm based on discretemulti-value dynamic programming (MDP) has been developed to calculate the global optimum COG of alarge-scale open-pit metalliferous deposit. The proposed COG optimization algorithm aims to overcome thelimitations of straightforward classical techniques in determining the optimum COG. This discrete COG-MDPmodel is the first of its kind and has the novelty of dealing with the simulation of eight dynamic possibilities toachieve the maximal global Net Present Value (NPV). A high-level programming language (Python) has been usedto develop the computer model to deal with the complexity of handling a minimum of 500 series of dynamicvariables. This model can generate results in polynomial-time from the complex of mining, milling, and smeltingand refining system corresponding to various limiting conditions. The prime objective considered in the model isto optimize the COG of a metalliferous deposit. A working open-pit copper mining complex from India has beenused to validate the model. In this study, the optimum COG for the Malanjkhand copper deposit has been found tobe (0.33%, 0.23%, 0.52%, 0.26%, 0.27%, 0.22%, 0.24%) with a maximum NPV of ₹ (12204, 14653, 16948, 14609,21454, 26717, 38821) million corresponding to various scenarios. The findings also show that the present valuegradually hits zero after the project’s life cycle, confirming the typical pattern of other mining firms.

A sequentially advancing algorithm based on multi-value dynamic programming for the cut-off grade optimization in open-pit metalliferous deposits

In techno-economic concern, cut-off grade (COG) optimization is the key for efficient mineral liquidation from the huge metalliferous surface mining sector. In this paper, a sequentially advancing algorithm based on exact multi-value dynamic programming (MDP) has been developed to determine the optimum COG of an open-pit metalliferous deposit. The proposed COG optimization algorithm aims to overcome the limitations of straightforward classical techniques in determining the optimum COG. This discrete COG-MDP model is the first of its kind and has the novelty of dealing with the simulation of eight dynamic possibilities to achieve the maximal Net Present Value (NPV). A high-level programming language (Python) has been used to develop the computer model to deal with the complexity of handling a minimum of 500 series of dynamic variables with a precision value of 0.01% in grade bins. This model can generate results in polynomial-time from the complex mine, mill, and smelter and refinery system corresponding to various limiting conditions. The prime objective considered in the model is to optimize the COG of a metalliferous deposit. The model validation has been done using a real-life case study of an open-pit copper mine in India (Malanjkhand Copper Mine, HCL), considering the fixed yearly output of the mining, milling, and smelting and refining. In this study, the optimum COG for the Malanjkhand copper deposit has been found to be (0.33%, 0.23%, 0.52%, 0.26%, 0.27%, 0.22%, 0.24%) with a maximum NPV of ₹ (12204, 14653, 16948, 14609, 21454, 26717, 38821) million corresponding to various scenarios. The findings also show that the present value of net cash-flow grows in the early years, peaks at a specified mid-life time, and then drops as the reserve is depleted. The present value gradually hits zero after the project’s life cycle, confirming the typical pattern of other mining firms.

Three-Dimensional Audio-Magnetotelluric Imaging including Surface Topography of the Cimabanshuo Porphyry Copper Deposit, Tibet

Cimabanshuo deposit is a newly discovered porphyry copper (Cu) deposit with giant metallogenic potential, found in the western segment of the Gangdese metallogenic belt, Tibet. The average elevation of the deposit is greater than 5500 m and the terrain on which it is found is steep and complex. Therefore, it is untraversed, and the existing exploration works on it are weak. We used 59 AMT sites belonging to an array covering the main, proven mineralization zone and ore bodies of this deposit for an analysis of its underground electrical structure. Dimensionality and strike analysis revealed the apparent three-dimensional (3D) features near the Cu ore bodies. 3D inversion with topography was conducted for the AMT array data. A large range of high-resistivity anomaly (~500–2000 Ωm) appears beneath the proven Cu mineralization zone and ore bodies, which is interpreted as intrusive rocks with potassic alteration. Although containing chalcopyrite, it is characterized by middle–high resistivity due to a low sulfide content and poor connectivity. Moreover, a series of scattered conductors (~10–300 Ωm) around the Cu ore bodies are distributed in the shallow layer from near the surface to ~200 m, possibly indicating phyllic alteration containing pyritization and connected metal sulfides. The proven ore bodies of Cimabanshuo are mainly located at the junction regions between high-resistivity intrusive rocks and high-conductivity sericitization alteration zones. According to this research, the 3D inversion with topography of AMT data can visually display the 3D distribution of intrusive rocks and alteration zones beneath porphyry Cu deposits in high-elevation regions, and provides a reference for further exploration works.

The Crystal Structure of Bornite Cu5FeS4: Ordered Fe and Split Cu

The crystal structure of bornite with ideal formula Cu5FeS4 from the Saishitang skarn copper deposit in Qinghai Province, along with bornite from the Yushui spouting hydrothermal copper deposit in Guangdong Province and the Bofang sandstone copper deposit in Hunan Province, has been refined by single-crystal X-ray diffraction with R1 = 0.0259–0.0483 (I > 2σ) and 0.0338–0.1067 for 2732 to 3273 unique reflections. As represented by the Saishitang sample, it is orthorhombic with a Pbca space group and unit cell parameters a = 10.97016(18) Å, b = 21.8803(4) Å, c = 10.9637(2) Å, V = 2631.61(8) Å3 and Z = 16. The structure is composed of sulfur layers parallel to the (0 1 0) lattice plane with interstices occupied by metal atoms. The Fe atoms occupy two tetrahedral sites with full occupancy, but the Cu atoms are all partially distributed over 20 paired sites, split from 10 sites with a distance ranging from 0.24 Å to 0.54 Å. The Fe-S tetrahedra are not split with Fe-S lengths from 2.2609 Å to 2.3286 Å (average 2.2997 Å). The Cu-S lengths in pyramidal triangles are from 2.218 Å to 2.397 Å (average 2.288 Å), whereas the Cu-S tetrahedra are strongly distorted, with great variations in Cu-S lengths from 2.224 Å to 2.604 Å (average 2.391 Å). The orthorhombic unit cell is stacked from 16 1a-type (5.5 Å) cubes; each cube has one tetrahedrally-coordinated Fe atom, five split from 3- to 4-coordinated Cu atoms, and two vacancies, i.e., 5CuIII–IV+FeIV+2[]+4S. The phenomenon of site-splitting of Cu atoms may provide for a more accurate structure of bornite, allowing for a better understanding of its magnetic properties and ore-formation conditions.

Porphyry copper deposit formation in arcs: What are the odds?

Arc magmas globally are H2O-Cl-S–rich and moderately oxidized (ΔFMQ = +1 to +2) relative to most other mantle-derived magmas (ΔFMQ ≤ 0). Their relatively high oxidation state limits the extent to which sulfide phases separate from the magma, which would otherwise tend to deplete the melt in chalcophile elements such as Cu (highly siderophile elements such as Au and especially platinum-group elements are depleted by even small amounts of sulfide segregation). As these magmas rise into the crust and begin to crystallize, they will reach volatile saturation, and a hydrous, saline, S-rich, moderately oxidized fluid is released, into which chalcophile and any remaining siderophile metals (as well as many other water-soluble elements) will strongly partition. This magmatic-hydrothermal fluid phase has the potential to form ore deposits (most commonly porphyry Cu ± Mo ± Au deposits) if its metal load is precipitated in economic concentrations, but there are many steps along the way that must be successfully negotiated before this can occur. This paper seeks to identify the main steps along the path from magma genesis to hydrothermal mineral precipitation that affect the chances of forming an ore deposit (defined as an economically mineable resource) and attempts to estimate the probability of achieving each step. The cumulative probability of forming a large porphyry Cu deposit at any given time in an arc magmatic system (i.e., a single batholith-linked volcanoplutonic complex) is estimated to be ~0.001%, and less than 1/10 of these deposits will be uplifted and exposed at shallow enough depths to mine economically (0.0001%). Continued uplift and ero­sion in active convergent tectonic regimes rapidly remove these upper-crustal deposits from the geological record, such that the probability of finding them in older arc systems decreases further with age, to the point that porphyry Cu deposits are almost nonexistent in Precambrian rocks. A key finding of this paper is that most volcanoplutonic arcs above subduction zones are prospective for porphyry ore formation, with prob­abilities only falling to low values at late stages of magmatic-hydrothermal fluid exsolution, focusing, and metal deposition. This is in part because of the high threshold required in terms of grade and tonnage for a deposit to be considered economic. Thus, the probability of forming a porphyry-type system in any given arc segment is relatively high, but the probability that it will be a large economic deposit is low, dictated to a large extent by mineral economics and metal prices.

Application of Dirichlet Process and Support Vector Machine Techniques for Mapping Alteration Zones Associated with Porphyry Copper Deposit Using ASTER Remote Sensing Imagery

The application of machine learning (ML) algorithms for processing remote sensing data is momentous, particularly for mapping hydrothermal alteration zones associated with porphyry copper deposits. The unsupervised Dirichlet Process (DP) and the supervised Support Vector Machine (SVM) techniques can be executed for mapping hydrothermal alteration zones associated with porphyry copper deposits. The main objective of this investigation is to practice an algorithm that can accurately model the best training data as input for supervised methods such as SVM. For this purpose, the Zefreh porphyry copper deposit located in the Urumieh-Dokhtar Magmatic Arc (UDMA) of central Iran was selected and used as training data. Initially, using ASTER data, different alteration zones of the Zefreh porphyry copper deposit were detected by Band Ratio, Relative Band Depth (RBD), Linear Spectral Unmixing (LSU), Spectral Feature Fitting (SFF), and Orthogonal Subspace Projection (OSP) techniques. Then, using the DP method, the exact extent of each alteration was determined. Finally, the detected alterations were used as training data to identify similar alteration zones in full scene of ASTER using SVM and Spectral Angle Mapper (SAM) methods. Several high potential zones were identified in the study area. Field surveys and laboratory analysis were used to validate the image processing results. This investigation demonstrates that the application of the SVM algorithm for mapping hydrothermal alteration zones associated with porphyry copper deposits is broadly applicable to ASTER data and can be used for prospectivity mapping in many metallogenic provinces around the world.

Pollution and Risk Assessment of Heavy Metals in the Sediments and Soils around Tiegelongnan Copper Deposit, Northern Tibet, China

The surface sediments of the Rongna River and the surface soils around the Tiegelongnan copper deposit were collected, and the heavy metals Cu, Zn, Pb, Cr, Cd, As, Hg, and Ni were measured for their concentrations and health risk assessment. When the Rongna River passed through the Cu deposit area, the concentrations of Cu, Zn, As, Cd, Ni, and Hg in the surface sediments increased significantly, and the concentrations of Cu, Zn, and As exceeded the corresponding Grade II environmental quality standard. The heavy metals in the soil of the mining area were greater than the background value of the soil in Tibet. The geoaccumulation index indicated that the sediments of the river entering the mining area were very highly polluted by Cu and moderately polluted by Cd and Zn, and the soils in the mining area were moderately polluted by Cu. The potential ecological risk (PER) indices revealed that the sediments of the river entering the mining area had significantly high ecological risks, while the PER of the sediments away from the river section of the mining area was low, and the PER of the soils around the Cu deposit was moderate. The results of the health risk assessment indicated that the noncarcinogenic risks of heavy metals in sediments and soil of the mining area were within the acceptable range for adults and children. However, the carcinogenic risk of As and Cd in the sediment and As in the soil exceeds the relevant national standards, which may pose a certain risk to human health.