Ore and Energy Resource Geology最新文献

Existing algorithms for estimating the maturity of lunar soils are not optimized for data from any of the orbital sensors which are currently active. This paper addresses this issue by proposing an algorithm for estimating soil maturity (I_S/FeO) using spectroscopic data at the spectral resolution of the Moon Mineralogy Mapper (M³). As part of this method, four key spectral parameters for estimating I_S/FeO are identified and used to train a Support Vector Regression (SVR) model. The physical significance of each parameter is discussed, and the equation of the predictive hyperplane is provided for increased transparency. The proposed method outperforms state-of-the-art algorithms and returns a coefficient of determination (R²) of 0.92 over the Lunar Soil Characterization Consortium (LSCC) dataset.

Research on fluid inclusions in Iron Oxide Copper Gold hydrothermal systems has significantly progressed over the past few decades. This paper summarizes the major research advances including petrography, microthermometry, and the compositional of fluid inclusions, as well as the main theories regarding the origins and evolution of ore formation. Fluid inclusions are prevalent within hydrothermal minerals of IOCG systems, including aqueous biphasic (L-V), three-phase (L-V-S) and multiphase (L-V-nS), aqueous-carbonic (L_H2O-L_CO2) and aqueous-carbonic with solids (L_H2O-L_CO2-S), single phase (L_CO2) inclusions. The types and quantity of fluid inclusions are varied in different hydrothermal alteration and mineralization stages. Geochemical investigations have identified at least two distinct fluid contributors in IOCG systems: a hot, saline magmatic-hydrothermal fluid and an external, non-magmatic fluid (e.g., basin brine, meteoric water, formation water, or metamorphic fluids). It is generally believed that the early stages of the hydrothermal alteration are primarily controlled by magmatic fluids rich in metals and volatiles, with the Fe (-REE) mineralization. Non-magmatic fluids mixing at the last stage can effectively induce a temperature decrease or added reduced sulfur may be a significant factor contributing to Cu-Au precipitation.

Black shale serve as a new uranium sources and provide reducing agents for uranium deposits through hydrocarbon generation. However, the systematically interplay between the black shale and the formation of uranium deposits is not obscure. This paper provides a comprehensive summary and critical assessment of the depositional control factors of uranium in black shales, the extent of uranium migration during hydrocarbon generation, the enrichment of uranium in oil/gas reservoirs, and the relationship between crude oil-natural gas leakage and uranium. The study show that U-riched constituents within black shale persist through mechanisms of adsorption, reduction, complexation, and absorption within matrices rich in phosphates, iron, carbonate minerals, and organic matter. The U in black shale originates from continental weathering, volcanic eruptions, and seabed hydrothermal activities. The average U concentration in black shale is determined by the U content in atmosphere, while its relative content is controlled by the degree of anoxia during the geological historical periods.. Black shale prior to the Late Neoproterozoic period exhibits minimal migration with hydrocarbon substances, whereas black shale post the Late Neoproterozoic period demonstrates migration, with a migration rate ranging approximately between 55 and 75 %. However, the migrated uranium does not accumulate in (ancient) reservoirs. The correlation between hydrocarbon substances and sandstone-type uranium deposit is attributed to the oxygen uptake by hydrocarbons or the thermochemical sulfate reduction (TSR). The study has fundamental significance for further understand the interaction mechanisms between uranium and hydrocarbon substances.

Soil geochemical analyses from central Tanzania reveal significant gold (Au) values, highlighting the potential for further exploration in the region. This study employs ensemble machine learning models—XGBoost-RF, XGBoost-SVM, and XGBoost-ANN—to enhance predictions of Au distribution. Among these, the XGBoost-ANN model showed the highest accuracy during the training phase, achieving a Mean Absolute Percentage Error (MAPE) of 1.275, a Root Mean Square Error (RMSE) of 0.031, an R² of 0.999, and a Pearson Correlation Coefficient (PCC) of 0.999. However, its performance declined in the testing phase with a MAPE of 0.0668 and an RMSE of 0.2491, indicating reduced predictiveness on new data. Spatial analyses using Global and Local Moran's I tests revealed no significant global spatial autocorrelation but identified localized clusters of high and low Au concentrations. Specific areas showed significant spatial dependence, enhancing our understanding of the complex geospatial distribution of Au. These findings support the combined use of predictive modeling and spatial statistical methods to refine mineral exploration strategies, highlighting the value of advanced analytics in identifying promising exploration targets.

Drilled well data and 3D seismic data are integrated to model the seal capacity of the Late Campanian- Maastrichtian mudstone in the exploration Block 52 operated by PETRONAS Suriname Exploration & Production BV. The study proposes simple relationships between interval velocity and porosity, porosity against permeability, and normal compaction of the mudstone with depth, based on the drilled well data. The most likely model of the top seal capacity suggests a maximum column height to be in the range of 330 – 350 m in the study area for gas. The model also suggests a gradual increase in the top seal risk towards the northeastern direction of the study area which may be due to the effect of sediment fairway from the nearby Demerara High. On the other hand, an increasing trend of top seal capacity towards the southwestern side of the block relates to the possible effect of the thicker deposition of transgressive fine-grained package with more compaction resulting in a decreasing trend of porosity and permeability of the mudstone.

1. Three-dimensional (3D) deterministic modeling is crucial for analyzing the controlling factors of mineralization. The Shanggong gold deposit, situated in the southern margin of North China Craton, represents a magmatic hydrothermal deposit. To construct the 3D deterministic model, boreholes, cross-sections, and geochemistry assays are integrated based on the geological characteristics of the Shanggong gold deposit. This paper summarizes its contents as follows: 1) GoCAD software can be utilized to build a 3D geology model encompassing faults and ore bodies; 2) Geostatistics and discrete smooth interpolation (DSI) can be employed to create a 3D attribute model involving grade and Au/Pb ratio. The findings reveal that: 1) High-value zones are associated with NE and NNE trending cross faults; 2) Deterministic modeling, such as grade model and Au/Pb ratio model, effectively elucidates the metallogenic process mechanism.

Lithium is a critical mineral resource for development of hi-tech green energy technologies. Finland is one of the few EU countries with high potential for lithium mineral resources. Presence of significant lithium ore reserves within the rare-element granitic pegmatites classified as LCT (Li-Cs-Ta) pegmatites in the Kaustinen lithium province in Finland, makes it a substantial region for lithium resources in Europe. Hence the present study presents geochemical characterization of the Kaustinen lithium province for lithium exploration targeting. Results of multivariate statistical analysis applied to till geochemical data are presented. These map the geochemical signatures of (a) major lithostratigraphic units and (b) lithium-bearing spodumene pegmatites and differentiate them from the geochemical signatures of other potentially lithium enriched rocks such as mica schists, mica gneisses and granitoids in the region. Two unsupervised clustering algorithms are employed, namely principal component analysis (PCA) and factor analysis (FA). The results indicate that each lithostratigraphic unit is characterized by a distinct geochemical signature which is distinguishable in the factor and PC maps derived. The boundaries of the lithostratigraphic units in the PC and factor maps conform to the units mapped from the geophysical datasets.

This study demonstrates that FA and PCA of till geochemical data can be used for mapping bedrock lithology in poorly exposed terrains. Additionally, the factor analysis maps also differentiate the Li signature of the mica schists and gneisses of the basement complex from that of pegmatitic granite units, which are interpreted to be the source rocks of the spodumenepegmatite generating fluids. The FA highlights the spatial association of spodumene pegmatites with the mafic metavolcanic lithological units within the Evijärvi-Himanka thrust zone, indicating that the compressional regime during the Svecofennian orogeny in this region provided the structural architecture as well as pre-existing permeable pathways for the parental fluids of the spodumene pegmatites; while at the same time the competency contrast between mafic metavolcanic rocks (amphibolite) and surrounding schists in a compressional regime is interpreted to be one of the controls on the channelization and emplacement of the pegmatitic melts.

Due to the lack coupling analysis of heavy metals in soil-plant-atmosphere system, the migration and transformation of heavy metals remains unclear. In this study, 240 surface (0–20 cm) soil samples, 365 plant samples and 168 atmospheric dust samples were collected in a large coal mining area, in which selected heavy metals arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), plumbum (Pb), zinc (Zn) were analyzed, to elucidate the spatial distribution and source of heavy metals and their bioavailability. According to the results of ArcGIS mapping analysis, the distribution patterns of Cd, Cu, Pb, and Zn in the three media are similar, while the distribution characteristics of Cr and Ni are highly consistent. The factors contributing to these spatial distribution characteristics of heavy metals could be associated with human activities and the characteristics of the elements. Compared with the background value, Zn, Cr, Pb in soil, Zn, Cr, Cu in plants and Zn, Pb, Cu in atmospheric dust apparently accumulated. The geological accumulation pollution index (I_geo) shows that there exist mild to moderate levels of Cr and Cd pollution in soil, and mild to moderate levels of Cd, Cr, Pb, and Zn pollution in the air. The evaluation of potential risks of heavy metals showed moderate ecological hazards in soil and strong ecological hazards in atmospheric dust. The sources of heavy metals were assessed using principal component analysis (PCA), land use type, and spatial analysis. Anthropogenic factors are the main sources of heavy metal content. Coal-related industrial processes related to coal, sewage irrigation, the use of agricultural chemicals, and vehicle emissions may be anthropogenic sources of heavy metals.

The Xiaoyuzan deposit is a typical intermediate-sulfidation epithermal Au deposit in the Boluokenu metallogenetic belt. The orebodies are hosted by the Lower Carboniferous Dahalajunshan Formation volcanic rocks and controlled by the NW- and NNW-striking faults. The metal minerals present in the ores mainly include pyrite, sphalerite, galena, chalcopyrite, and tetrahedrite. The non-metallic minerals are primarily composed of quartz, calcite, and sericite. Three ore-forming stages are distinguished based on mineral assemblages, wall-rock alteration, and vein crosscutting relationships, including the quartz-pyrite stage (I) with silicification and propylitization, quartz-sulfide stage (II) with phyllic alteration, and quartz-carbonate stage (Ⅲ) with carbonatization. Three different types of pyrite are classified: coarse-grained PyI with cubic from the wall rock, fine-grained PyII with a crystal form of pentagonal dodecahedron in the quartz-sulfide veins, and coarse-grained cubic PyⅢ in the quartz-carbonate veins. The in-situ δ³⁴S value range of sulfides from stage I, stage II and stage Ⅲ are 5.0 ‰ to 5.5 ‰, 4.3 ‰ to 6.5 ‰ and 5.7 ‰ to 6.2 ‰, respectively. The composition of S isotopes indicates that the source of the sulfur is magmatic in origin, with main contribution from the host rock. All the types of pyrite are relatively enriched with Sb, Cu, Ag, Pb, Bi, and As. The composition of pyrite suggests that the Au in the pyrite present as lattice gold (Au⁺¹). The gradual decrease in Co contents from in PyⅠ, PyⅡ to PyⅢ indicates a gradual decrease in temperature during fluid evolution. The contents of trace elements in sphalerite are relatively low, with Fe, Mn, Cd, and Cu being relatively enriched. Using the sphalerite geothermometer (GGIMFis), the calculated temperature falls within the range of 303.0 to 334.3 °C. Pyrite II is characterized by the occurrence of oscillatory zones, suggesting rhythmic changes in fluid physicochemical conditions and compositions. Although coexisting of liquid-rich and vapor-rich aqueous inclusions were locally observed in stage II quartz according to previous studies, the absence of crustiform/colloidal/lattice bladed quartz in the main stage suggests that slight or gentle fluid boiling has occurred. In summary, it is proposed that fluid-rock reactions made great contribution for the precipitation of gold and sulfides in the Xiaoyuzan deposit.