Journal of Geochemical Exploration最新文献

When dealing with environmental problems, it is of fundamental importance to establish reference values (geochemical baselines) against which to determine the presence or absence of active contamination processes.

In the effort to develop a method to assess the geochemical baselines for territories featuring complex geological settings and a well-established anthropic environmental pressure, we combined compositional data analysis (CoDA) with geolithological information to reduce the degree of uncertainty possibly affecting the results. The proposed approach comprises (1) a knowledge-driven step to select a number of sample subsets from a geochemical dataset each with a high probability of having its composition strongly influenced by only one of the lithologies outcropping in the study area; (2) a data-driven step to compute compositional principal balances and define geochemical indicators to be used to assign each of the observations in the dataset to one of the geochemical domains associated to a mayor lithologies outcropping in the study area; (3) the determination for each geochemical domain of baseline values based on the samples assigned to them by the data-driven step.

The method was tested using the geochemical data referring to 887 stream sediment samples collected across the Volturno River catchment basin (Southern Italy), featuring a relevant lithological heterogeneity.

The results obtained were easily interpretable as they fitted well with the geomorphological, geochemical, and geodynamic processes characterizing the study area.

Despite the use of stream sediments for the specific case study presented, the application principles of the method hold for any environmental media and for any territory for which there is a need to define baseline values. However, for a successful application of the method, it is crucial to have a fair knowledge of the geological settings of the study area.

Acid sulfate soils (AS-soils) are a common feature along coastlines in many countries that can have significant environmental and economic impacts. AS-soils oxidation may cause soil and water acidification, the release and mobilization of metals and the formation of new precipitated phases. In northern Sweden, some soils are already oxidized and constitute an environmental concern. This study aimed to analyze the geochemistry and mineralogy of AS-soils profiles by identifying element depletion and accumulation zones, the parent material, minerals that contribute to acidity and their oxidation products as well as anomalous element content values that could be related to anthropogenic sources. Two soil profiles were drilled close to the Lule River in Södra Sunderbyn, Luleå. The profiles were characterized by an oxidized zone (OZ) with a declining trend in element content, a transition zone (TZ) where elements tended to accumulate and a reduced zone (RZ) where elements had their maximum content. The pH was a key determinant of the element distribution. Cadmium, Co, Ni and Zn were found to be typical elements released into the environment during AS-soils oxidation. After sample incubation, pH measurements showed a pronounced decrease in layers with higher S and total organic carbon (TOC) content. Both profiles developed a larger thickness of potential acid-risk sediments according to S, TOC and pH measurements during incubation. Iron sulfides were identified as the main acidity generators, represented by an abundance of framboidal pyrites with a Mn-rich rim formed under anoxic-euxinic conditions. Iron sulfates and iron oxyhydroxides (FeOOH, FeOH₃) were identified as the most common products of oxidation processes.

Indium (In) is a critical metal used in the photovoltaic and semiconductor industries, which have exhibited extraordinary growth in demand. Indium is produced as a by-product of mining from different ore deposits (e.g., epithermal, sediment-hosted, and skarn). Volcanogenic massive sulfide (VMS) deposits are an important source of In; however, the mechanism of In enrichment is not fully understood. Here, we combine mineralogy with in situ trace element and S-Pb isotope geochemistry to reveal the enrichment of indium in the Ashele VMS Cu-Zn deposit (1.08 Mt. Cu, 0.43 Mt. Zn) located in Altay, NW China. The Ashele deposit is hosted in the metamorphosed Devonian felsic-bimodal volcanic rocks. This deposit consists of seafloor hydrothermal, metamorphic hydrothermal, and supergene stages. The seafloor hydrothermal stage comprises macro-scale Cu-rich bands (chalcopyrite, pyrite, and minor sphalerite) and Zn-rich bands (sphalerite, pyrite, and minor chalcopyrite). Indium is mainly hosted by chalcopyrite (mean 178 ppm) and sphalerite (mean 214 ppm) and occurs in the lattice. Mineral assemblages and trace element geochemistry suggest that the Cu-rich bands were deposited under high temperatures (> 300–350 °C) and sulfur fugacity (−7.2 to −4.9), whereas the Zn-rich bands were formed under lower temperatures (180–220 °C) and sulfur fugacity (−15.7 to −11.5). The interlayered Cu-rich and Zn-rich bands may reflect the oscillating temperature and sulfur fugacity variations. In situ S isotopic compositions of sulfides cluster within two ranges: 1–3 ‰ and 3–6 ‰, suggesting two endmembers: volcanic origin and reduced seawater sulfate. Pb isotopic ratios are similar to those of the host volcanic rocks, indicating that the metals may be derived from the felsic volcanic system. During metamorphism, the indium may be retained, but Cu contents of sphalerite become more homogeneous. Most In-rich VMS deposits worldwide are hosted by the felsic-dominant system in island arc and back-arc settings. These tectonic settings are conducive to the production of felsic volcanic systems, which are more likely to contain In mineralization. This study highlights the enrichment mechanism of indium in VMS deposits and suggests that the South Altay could become an important source of In.

One of the greatest challenges in mineral prospectivity mapping (MPM) research nowadays is to find a solid methodology that ensures the reliability of the prospectivity model during the learning and prediction procedures. Multiple uncertainties such as the location of non-deposit sites or the type of machine learning algorithm (MLA) can bias the MPM. To investigate these effects, we used multiple training datasets with different non-deposits locations, randomly created, and MLAs such as Artificial Neural Network (ANN), Random Forests (RF) and Support Vector Machine (SVM), to model orogenic-Au prospectivity in the Pitangui Greenstone Belt (PGB, Brazil). Regarding the implications in the methodology for MPM, there are great differences between the models' performances in mapping prospective zones when there is a slightly change in the location of negative samples. These changes can be observed by using the Shapley additive explanation metrics (SHAP values), which can help mitigate such effects by choosing an optimal model among all randomly created datasets. The SHAP values of non-deposit sites also showed that ANN and SVM present overfitting problems despite the use of balanced data. RF on the other hand outperformed in all ten datasets and showed great recognition and adjustment to the negative samples. The results presented in this research are also promising to the prospective studies in the PGB, as it shows a map capable to correctly predict 97 % of the known deposits and occurrences in 3 % of the total area and points the new frontiers for gold exploration in the PGB.

Multivariate analysis of soil geochemistry is a powerful tool for differentiating lithological units and detecting geochemical dispersion halos related to mineralization or contamination. While univariate analysis can effectively identify lithological units with pronounced variations, it may fail to differentiate between subtler variations in lithologies. Traditional multivariate techniques such as principal component analysis (PCA) have limitations, including difficulties in understanding the individual contributions of each variable and an inability to work with non-Gaussian data. Independent component analysis (ICA) has emerged as a potential alternative, as it can effectively identify independent components of non-Gaussian data. In this study, we compared the effectiveness of PCA and ICA in relating multivariate soil geochemistry to parent lithology using the Soil Geochemical Atlas of Cyprus and associated digital geological maps. Both PCA and ICA were able to differentiate between the ultramafic units within the Troodos Ophiolite (TO) and the Circum-Troodos Sedimentary Succession (CTSS). However, ICA was more effective than PCA in identifying pillow lavas, providing a clear separation in the scores for IC4 and IC5. Furthermore, both PCA and ICA were able to separate the sheeted dykes from the cumulate mafic units within the TO. The gabbro unit is closely defined by IC2 scores. In contrast, PCA failed to provide factors that effectively delineated the Mamonia Terrane from other units, especially the TO, while ICA was able to provide a distinct separation in IC4 and IC5 scores. Separation between the CTSS and Quaternary units was weakly observed in IC2 scores. These findings demonstrate that there is a difference in the effectiveness of PCA and ICA in identifying different lithological units and emphasize the need for a careful selection of multivariate methods to differentiate between subtle differences in soil geochemistry relating to variations in parent lithology.

Bauxite deposits are an important source of Al and also host a variety of critical metals, including rare earth elements (REEs). However, the parent rocks of bauxite deposits generally have low REE contents, leading to uncertainty over the REE sources and enrichment mechanisms. In this study, we report high REE (2095 ppm; Ce = 1340 ppm) contents in the Lijiatian bauxite deposit in Luxi County, western Hunan Province, South China. The Lijiatian bauxite deposit is the largest deposit in Hunan Province, with a total Al resources exceeding 7 × 10⁶ tons. The ore-bearing strata in the lower Permian Liangshan Formation (ca. 275 Ma) are comprised a 3–5-m-thick bauxite layer (bauxite ore/clay) and an underlying 4–8-m-thick Fe layer (Fe ore/Fe-rich clay). The high REE contents in the bauxites can be attributed to the widespread presence of bastnäsite. The bastnäsite commonly coexists with authigenic chlorite, which formed at temperatures of 221–285 °C, indicative of a hydrothermal origin. Uranium–Pb dating of the bastnäsite yielded an age of 141.4 ± 7.0 Ma, indicating a hydrothermal overprint occurred during the Early Cretaceous, which resulted in the enrichment of REEs in the bauxites. The high contents of REEs, Ba, and P in the Cambrian Niutitang Formation black shales indicate it was a potential source of these elements for bastnäsite and coexisting barite and apatite. The deep-seated hydrothermal fluids (enriched in HF, CO₂, and H₂S) associated with the late Yanshanian extensional tectonic event in South China (155–123 Ma) ascended along fractures. The ascending fluids interacted with the black shales, extracting REEs, Ba, and P. When the fluids reached a certain depth, they mixed with the infiltrating basinal waters to form a mineralising fluid. The fluid then migrated towards the bauxite layers, underwent cooling and depressurisation, and precipitated bastnäsite, barite, apatite, and chlorite. We propose that a hydrothermal event led to the enrichment of REEs in the bauxites in Hunan Province. Considering the scarcity of REEs in the parent rocks of bauxites worldwide, hydrothermal activity likely has a key role in REE enrichment in bauxites.

Application of advanced data mining methods to various types of geochemical data is able to fingerprint valid signatures of mineralization, thus unveiling ore genesis and discovering new minerals. But individual studies that apply data mining methods to both local- and regional-scale, both sediment and whole-rock multi-element geochemical data sets are relatively scarce. Here, we applied data mining methods, including multivariate statistical analysis (principal component analysis), spatial analysis (trend surface analysis), unsupervised machine learning algorithm (K-means clustering), supervised algorithms (random forest and deep neural network) to both regional sediment geochemical and local lithogeochemical data from the Duolun-Guyuan prospect, in order to determine the geochemical signatures of volcanic-type uranium mineralization through characterizing: (1) representative element associations; (2) axial zonation of primary haloes; (3) element distribution patterns; and (4) crustal structures (via deep learning-based predictive hafnium (Hf) isotopic mapping). Results of principal component analysis and random forest show that samples from known ore districts (e.g., Zhangmajing and Daguanchang) exhibit a distinct combination of major ore-forming elements (U and Mo), chalcophile elements (Ag, Hg, Pb, Sb and As), rare and rare earth elements (Be, Li, La, Nb and Y), tungsten (W), bismuth (Bi), and rock-forming elements (SiO₂, K₂O, Na₂O and Al₂O₃), differing from samples of both the mineralized and barren areas. The axial zonation of primary haloes in Daguanchang is comprised of supra-ore haloes (rare earth elements, Th, Nb, Zr, Hf, Ga and Rb), near-ore haloes (U, Mo, Pb, Zn, Cd and Sb), and sub-ore haloes (Li, Be, Sc, V, Cu, Sr, Cs, Ba, W and Bi). Moreover, trend surface analysis shows that in the study area, the spatial distribution pattern of the supra-, near-, and sub-ore elements forms a northwesterly alignment, with the supra-ore elements concentrated in the southeast, the sub-ore elements in the northwest, and the near-ore elements in between. Finally, deep learning-based predictive hafnium (Hf) isotopic mapping reveals that the Duolun-Guyuan prospect is dominated by negative mean zircon ε_Hf(t) values ranging from −17 to 0, except for some local areas in the west and southwest of Duolun and the north of Weichang. The above results may indicate critical signatures of volcanic-type U mineralization, consisting of meta- or pera-luminous, alkaline rhyolite resulted from crustal reworking, surrounding mantle-derived igneous rocks, proximal heat source, accompanying epithermal deposits (e.g., Ag, Au, etc.), and anomalous concentrations of U, Mo and relevant elements particularly Th, W, Bi, Ag and Sb etc. Our study will effectively provide new exploration geochemical indicators of volcanic-type U deposit.

Geothermometrical calculations in thermal systems are often limited by the presence of secondary processes that modify the chemistry of the deep reservoir fluid during the ascent to the surface (e.g., mixing, degasification). The effect of secondary processes can be avoided by applying some techniques to reconstruct the chemical equilibrium at depth and the fluid original composition. However, the reconstruction of thermal waters mixed with cold surficial waters is complicated when the dilution factors are unknown. For that case, here, we propose to use an approach consisting of the simulation of a concentration process that removed different amounts of water from the thermal solutions until the equilibrium temperatures of anhydrite and quartz converge for the waters affected by mixing in unknown proportions. Using classical geothermometers and geothermometrical modeling, including the water removal process and CO₂ degasification, a temperature range of 78 ± 9 °C at depth has been established for the Sierra Elvira geothermal system whose waters are in chemical equilibrium with respect to calcite, dolomite, anhydrite, quartz, illite, pyrophyllite and beidellite-K. The good agreement in the temperatures obtained for the different thermal fluids of the system suggests a common reservoir for all of them. The methodology used in this study can be applied to other geothermal systems in carbonate rocks affected by mixing.

Research on the mineralogical and geochemical characteristics of altered end-Guadalupian basaltic volcanoclastic rocks can increase the understanding of the alteration history of the overlying stratabound Nb(Ta)-Zr(Hf)-REY-Ga mineralization in the Late Permian coal-bearing sequences. In this paper, based on petrographic observations in addition to XRD, XRF, and ICP-MS analyses, we present and discuss in detail the relationships between primary and secondary minerals and outline the distribution of major and trace element chemistry in the middle/Late Permian basaltic volcaniclastics from the ELIP's zone, western Guizhou, southwest China. The primary clastic suite consists of plagioclase-group minerals, clinopyroxenes, feldspathoids, spinels, and basaltic glasses. Fragments of mafic and, less commonly, felsic and alkaline volcanic rocks are minor components of the studied samples. The alteration products are represented by various chlorite-group minerals (including abundant chamosite), quartz, calcite, albite, analcime, barite, and pyrite, along with relatively minor amounts of titanite, sanidine, magnetite, rutile, and copiapite. The mineralogical and geochemical characteristics of the studied volcaniclastics provide strong evidence correlating them with the high-Ti basalt group, widely distributed within the inner Emeishan large igneous province (ELIP) and coeval rift zone of the middle and outer ELIP. After deposition, the volcaniclastics reacted with complex solutions including heated meteoric waters and were periodically infiltrated by seawater and ascending hydrothermal fluids. As a result, the primary volcanic rocks partly lost alkalis, titanium, silica, and most of the trace elements. These elements, especially the incompatible elements, were probably enriched in the overlying tuffaceous and coal-bearing sequences in the middle and outer ELIP.

The Guojiagou Pb–Zn deposit is located in Li County, Gansu Province, northwestern China. The ores consist of skarn and vein types, with the skarn type occurring at the contact zone between granodiorite and marble, and the vein type hosted in the extension faults within the Triassic Huashiguan Formation limestone. Granodiorite samples from the Weijiazhuang pluton show high ratios of Sr/Y (32.25–43.44) and (La/Yb)_N (15.7–16.5), small Eu anomalies (δEu = 0.73–0.80), high concentrations of Mg^# (57.6–64.2), Cr (100–110 ppm), and Ni (15.9–16.6 ppm), abundant mafic micro-granular enclaves, and have zircon εHf_(t) values of −1.9 to −4.6 and T_DM2 of 1149.6 to 1285 Ma. This suggests that the Weijiazhuang granodiorites were generated by the partial melting of the Meso-Proterozoic high-K basaltic lower crust with the addition of mantle-sourced melts. The ore-forming process can be subdivided into five stages: prograde skarn (stage I), retrograde skarn (stage II), quartz-sulfide (stage III), sphalerite-calcite (stage IV), and quartz-calcite (stage V). The δ¹³C values range from −5.1 to −1.3 ‰ and δ¹⁸O values range from −4 to 18.6 ‰ in calcites, suggesting a mixed source of magma, limestone, and pore or basinal water for CO₃²⁻. The δ³⁴S values (6–7.7 ‰) of sulfides indicate that sulfur mainly originated from magma, with a minor contribution from host limestone. The Pb isotopes of sulfides from stages III and IV (²⁰⁸Pb/²⁰⁴Pb = 38.176–39.218, ²⁰⁷Pb/²⁰⁴Pb = 15.889–15.678, and ²⁰⁶Pb/²⁰⁴Pb = 18.147–18.903) showed mixed sources of crust and mantle. The Weijiazhuang pluton and Guojiagou Pb–Zn deposit yield ages of 220 ± 1.8 Ma (MSWD = 0.35) and 213 ± 3.0 Ma (MSWD = 1.5), respectively, obtained by zircon and garnet LA–ICP–MS U–Pb analysis. These results indicate that the Guojiagou Pb–Zn deposit formed in a syn-collisional tectonic regime during the Late Triassic. Based on the data presented in this study and previous research on mineralization in the eastern West Qinling Orogen, we conclude that the Guojiagou Pb–Zn deposit is a typical skarn-type deposit and that Pb–Zn mineralization in the eastern West Qinling Orogen is closely related to Triassic magmatism, which provided not only thermal energy but also ore-forming materials and fluids.