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After long-term accumulation, weathering and rainfall of metals and non-metals in tailings ponds, the chemical composition will change, and the surface heavy metals may gradually migrate to the bottom layer, thus showing a cumulative effect. At present, the distribution of cyanide and heavy metals in vertical space has not been reported. In this work, 12 sampling points were arranged for a cyanide tailings pond by the grid method in Jilin, China. The contents of cyanide and heavy metals were determined according to the standard method. The results show that the heavy metals in the tailings were mainly Cu, Pb, Zn and Mn (342.83 mg/kg, 571.09 mg/kg, 610.15 mg/kg and 796.63 mg/kg, respectively), and the concentration of heavy metals in the horizontal direction does not change significantly. It should be noted that the concentrations of zinc and manganese did not change much in the vertical direction. The concentration of copper and lead increased with the increase in depth. The concentration of cyanide at the sampling site far away from human activities was higher at 6.71 mg/kg, and the average concentration is 2.5 mg/kg. In addition, because the cyanide is unstable and affected by factors such as light and rainfall, the concentration change in the vertical direction fluctuates. [ABSTRACT FROM AUTHOR]

The Shulan area in Jilin Province is a part of the Lesser Xing'an–Zhangguangcai Range polymetallic ore belt, which is an important Cu–Mo ore region of northeast China. The discovery of three large Mo ore deposits (Fu'anbu, Chang'anbu, and Jidetun) highlights its potential for porphyry Mo ore deposits. Here we investigated the tectonic setting and mineralization of Mo ore deposits in the Shulan area, based on comparative study of the Fu'anbu, Chang'anbu, and Jidetun deposits. The ore-controlling structures are NE–SW- and NW–SE-trending faults. The main ore mineral in all three deposits is molybdenite. The ore bodies are all hosted in granites, have a stratiform or lenticular shape, and have strongly altered wall rocks. These observations indicate the Mo deposits in the Shulan area are typical porphyry Mo deposits. All were formed during the early Yanshanian (199.6–133.9 Ma). Biotite adamellites from the Chang'anbu deposit yield a U–Pb age of 182.10 ± 1.20 Ma. Molybdenites from the Fu'anbu and Jidetun deposits have Re–Os isochron ages of 166.9 ± 6.7 and 169.1 ± 1.8 Ma, respectively. Quartz and ore minerals were analysed for H–O and S–Pb isotopes, respectively. The results suggest the ore-forming materials were predominantly of upper-mantle origin, with secondary contributions from the lower crust. The ore-hosting granites have high concentrations of SiO2 (66.67–75.43 wt.%) and Al2O3 (12.91–16.44 wt.%), low concentrations of MgO (0.09–1.54 wt.%), and Ritman index (σ = K2O + Na2O)2/(SiO2 − 43)) ratios of 2.09–2.57. The granites are enriched in large-ion lithophile elements and depleted in high-field-strength elements, and have negative Eu anomalies. The ore-hosting rocks are geochemically similar to granites in northeastern China that were generated in a collisional orogeny. We conclude that early Yanshanian (199.6–133.9 Ma) mantle–crust-derived magmatism caused by the subduction of the Palaeo-Pacific Plate was the main source of Mo deposits in the Shulan area. [ABSTRACT FROM AUTHOR]

The Shanmen Ag deposit, located in the southeastern part of the Siping area, Jilin Province, is one of the large-scale Ag deposits in Northeastern (NE) China. Almost all Ag orebodies, Ag-bearing quartz-sulfide veins are strictly controlled by NE-trending faults or brittle fractures and are hosted in the Yanshanian monzonite and quartz diorite. In terms of deposit geology, three mineralization stages are recognized: the pyrite-quartz stage (I), the quartz-Ag-polymetallic sulfide stage (II), and the carbonate-quartz stage (III). The research results of the fluid inclusions in the different stages indicate that the early stage (Stage I) mainly contains three types of fluid inclusions: liquid-rich two-phase (L-type), vapor-rich two-phase (V-type), and CO2 aqueous multi-phase (C-type). The fluid belongs to a medium–high temperature and medium–low salinity H2O-NaCl-CO2 system and has boiling characteristics. The middle stage (Stage II) is mainly characterized by liquid-rich two-phase (L-type) and vapor-rich two-phase (V-type) inclusions, in which the mixing of fluids of different nature leads to the escape of CO2. Only liquid-rich two-phase (L-type) inclusions are distinguished in the late stage (Stage III). The fluids of two later stages belong to the medium-low-temperature and low-salinity H2O-NaCl system. Homogenization temperatures from the early to late stages range from 272.2 to 412.5 °C, 124.1 to 313.3 °C, and 128.6 to 224 °C, respectively. Fluid salinities in the early to late stages range from 1.6 to 12.1, 1.4 to 8.9, and 0.4 to 5.8 wt.% NaCl equivalent, respectively. The gradually decreasing trends of homogenization temperatures and salinities and the reduction in the CO2 content indicate that the release of CO2 and the low-temperature environment are important causes of the precipitation of Ag-bearing minerals. The δ18OH2O values of the ore-bearing quartz veins in the different stages range from −3.7 to +8.1‰, and the δD values of fluid inclusions in the quartz range from −113 to −103‰, indicating that the initial ore-forming fluid was mainly derived from magma and that the input of meteoric water gradually increased during the mineralization process. The δ34S values (ranging from −11.4‰ to +1.8‰) and Pb isotope compositions (206Pb/204Pb = 18.143–18.189, 207Pb/204Pb = 15.543–15.599, 208Pb/204Pb = 38.062–38.251) of sulfides suggest that the ore-forming materials have mixed mantle and crustal sources. Therefore, we propose that the release of CO2 and the low-temperature environment are important conditions for silver minerals precipitation, and the mixing of fluids of different nature is the dominant mechanism causing precipitation. The Shanmen Ag deposit can be classified as an intrusion-related medium–low temperature hydrothermal vein-type deposit. [ABSTRACT FROM AUTHOR]

One-class support vector machine (OCSVM) is an efficient data-driven mineral prospectivity mapping model. Since the parameters of OCSVM directly affect the performance of the model, it is necessary to optimize the parameters of OCSVM in mineral prospectivity mapping. Trial and error method is usually used to determine the "optimal" parameters of OCSVM. However, it is difficult to find the globally optimal parameters by the trial and error method. By combining OCSVM with the bat algorithm, the intialization parameters of the OCSVM can be automatically optimized. The combined model is called bat-optimized OCSVM. In this model, the area under the curve (AUC) of OCSVM is taken as the fitness value of the objective function optimized by the bat algorithm, the value ranges of the initialization parameters of OCSVM are used to specify the search space of bat population, and the optimal parameters of OCSVM are automatically determined through the iterative search process of the bat algorithm. The bat-optimized OCSVMs were used to map mineral prospectivity of the Helong district, Jilin Province, China, and compared with the OCSVM initialized by the default parameters (i.e., common OCSVM) and the OCSVM optimized by trial and error. The results show that (a) the receiver operating characteristic (ROC) curve of the trial and error-optimized OCSVM is intersected with those of the bat-optimized OCSVMs and (b) the ROC curves of the optimized OCSVMs slightly dominate that of the common OCSVM in the ROC space. The area under the curves (AUCs) of the common and trial and error-optimized OCSVMs (0.8268 and 0.8566) are smaller than those of the bat-optimized ones (0.8649 and 0.8644). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets predicted by the common and trial and error-optimized OCSVMs account for 29.61% and 18.66% of the study area respectively, and contain 93% and 86% of the known mineral deposits. The mineral targets predicted by the bat-optimized OCSVMs account for 19.84% and 14.22% of the study area respectively, and also contain 93% and 86% of the known mineral deposits. Therefore, we have 0.93/0.2961 = 3.1408 < 0.86/0.1866 = 4.6088 < 0.93/0.1984 = 4.6875 < 0.86/0.1422 = 6.0478, indicating that the bat-optimized OCSVMs perform slightly better than the common and trial and error-optimized OCSVMs in mineral prospectivity mapping. [ABSTRACT FROM AUTHOR]