Search Results (656)

Aiming to address the problems of low accuracy in coal–rock identification during coal mining, which lead to energy waste and safety hazards, a high-precision coal–rock medium identification method combining terahertz time-domain spectroscopy technology and multiple machine learning algorithms is proposed. By preparing coal–rock samples with a gradient change in coal content, terahertz time-domain spectroscopy data of coal–rock mixed media are collected, and optical parameters such as the refractive index and absorption coefficient are extracted. Principal component analysis is used to reduce the dimensionality of the terahertz data, and machine learning algorithms such as support vector machine, least squares support vector machine, artificial neural networks, and random forests are adopted for classification and identification. The study found that terahertz waves are more sensitive to coal–rock media in the 0.7–1.3 THz frequency band, and that the refractive index and absorption coefficient of coal–rock mixed media are significantly positively correlated with coal content within the range of 0–30%. After feature extraction and K-fold cross-validation, the random forest model achieved a coal–rock classification accuracy of over 96% on the test set, significantly outperforming other comparison algorithms. The research verifies the efficiency and practicality of terahertz technology combined with multiple machine learning algorithms in coal–rock identification, providing a new method for fields such as mineral separation. This method has, to a certain extent, broken through the accuracy bottleneck of traditional coal–rock identification technologies within its applicable range, providing a new solution for real-time detection of coal–rock interfaces and is expected to further reduce the risks of ineffective mining and roof accidents in the future. Full article

The performance of remote sensing-based mineral alteration extraction is significantly restricted in the vegetation area. Spectral unmixing is one of the effective methods to address the vegetation problem during mineral alteration extraction. However, the spectral curves of different tree species vary a lot; if multiple tree species are regarded as a whole during the spectral unmixing stage, the proportions of vegetation would be estimated with more errors. The purpose of this study was to verify the effects of dominant tree species classification on spectral unmixing and reconstruction, and to apply the proposed method to the mineral alteration extraction practice. To accomplish this, the Shabaosi gold deposit region in Mohe City, China, with an area of 650 km², was selected as the study area. Firstly, reference spectral curves, GaoFen-1/6 (GF-1/6) satellite imageries, ZiYuan-1F (ZY-1F) satellite imageries, Sentinel-1B satellite synthetic aperture radar (SAR) data, the ALOS digital elevation model (DEM), and sub-compartment dominant tree species data were collected; subsequently, simulated mixed-pixel reflectance images of ZY-1F, reflectance images of GF-1/6, ZY-1F, backscattering data of Sentinel-1B, slope, aspect, and 5484 tree species samples were derived from the collected data. Secondly, to verify the effect of dominant tree species classification on mineral alteration extraction, the reference spectra of pine, oak, goethite, and kaolinite were used to construct a simulated ZY-1F mixed-pixel image, and spectral unmixing and reconstruction experiments were conducted. Thirdly, fourteen independent variables were selected from the derived data, five dominant tree species classification models were trained and tested using tree species samples via the ResNet50 algorithm, and the pine- and birch-dominated parts were segmented from the ZY-1F images. Fourthly, minimum noise fraction (MNF), pixel purity index (PPI), n-dimensional visualizer auto-clustering, and spectral angle mapper (SAM) methods were separately applied to the pine- and birch-dominated parts of ZY-1F images to extract and identify endmembers; subsequently, the fully constrained least squares (FCLS) and linear spectral unmixing (LSU) methods were separately applied to the pine- and birch-dominated parts to estimate endmember proportions and generate spectrally reconstructed ZY-1F images. Fifthly, the pine- and birch-dominated parts of spectrally reconstructed ZY-1F images were mosaiced, and the SAM was utilized to extract mineral alteration in the study area. The result showed that in the spectral unmixing and reconstruction experiment, the spectral reconstruction error declined from 0.0594 (simulated ZY-1F image without segmentation) to 0.0292 and 0.0388 (simulated ZY-1F image that was segmented by pine- and oak-dominated parts), suggesting that dominant tree species classification could improve the accuracy of spectral unmixing and reconstruction and help obtain a more reliable mineral alteration extraction result. In the study area, the tested overall accuracies (OA) and Kappa coefficients of the five dominant tree species classification models were 0.75 ± 0.03 and 0.50 ± 0.05, respectively, suggesting that conducting dominant tree species classification was feasible in dense vegetation areas and could facilitate mineral alteration extraction. After segmenting the ZY-1F image by pine- and birch-dominated parts and spectral reconstruction, eight main types of alteration, including kaolinite, vesuvianite, montmorillonite, rutile, limonite, mica, sphalerite, and quartz, were identified, and nine mineral alteration areas (MA) were delineated accordingly. Full article

Centuries of cohabitation between the two species of large camelids (dromedary and Bactrian) in Kazakhstan with a long practice of crossbreeding make it necessary to establish a national camel milk breed standard. To obtain data on purebred herds, the present study aimed to (i) describe the morphometric differences between the two species of large camelids based on some body and udder measurements, (ii) describe the composition of the milk (fat, proteins, minerals) of each species and determine the most discriminating parameters, (iii) verify the possible links between milk productivity and body measurements. The analyses, carried out on two “purebred” farms (respectively, 21 Aruana dromedary and 15 Bactrian camel of different parities but all sampled at the 5th month of lactation), made it possible to observe significant difference in the concentration of certain milk components (fat, respectively, 3.59 ± 1.22 vs. 5.27 ± 0.87%, protein: 3.18 ± 0.28 vs. 3.97 ± 0.29%, magnesium: 1357.07 ± 246.2 vs. 929.40 ± 166.8 µg/mL, manganese: 0.10 ± 0.05 vs. 0.03 ± 0.01 µg/mL). The combination of parameters (protein, Ca, Mg, Zn, Mn) made it possible to distinguish 97.6% of the animals. It was also possible to establish the lack of correlations between body measurements and milk production, except the link with heart girth (r = 0.529; p = 0.014) in dromedary only. By automatic classification, 3 different morphological types in dromedary camels and two in Bactrian camels, as well as two profiles of dromedary milk and 3 of Bactrian milk were identified. The present study provided preliminary results which should be validated on a larger number of farms with purebred animals in order to establish a national standard of camel milk, a necessary step for developing the market. Full article

Sustainable development of regional water resources requires objective classification of lake systems according to dominant hydrochemical processes. The aim of the study was to develop a data-driven hydrochemical typology of natural lakes in Polissya based on the Self-Organizing Map (SOM) method to identify functionally distinct water quality regimes and justify management decisions within the basin approach. The study covered nine lakes of different genesis and trophic status. Key water quality indicators were analyzed: total nitrogen (TN), biochemical and chemical oxygen demand (BOD₅, COD), suspended solids (TSS), iron (Fe), and total dissolved solids (TDS). Descriptive statistics, correlation analysis, and neural network SOM modeling with subsequent clustering were applied. The results revealed strong positive correlations between TN, BOD₅, COD, and TSS, indicating joint control by biogenic and organic processes, while TDS showed negative correlations with organic indicators, reflecting mineralization control. SOM classification allowed us to identify three hydrochemical clusters: background systems with low anthropogenic load; organically enriched lakes with intense biogeochemical cycling; and mineralization-controlled water bodies dominated by geogenic factors. It has been established that spatial features of land use and morphometric characteristics (depth, type of feeding, hydrological connectivity) determine the sensitivity of lakes to external loads and their location. Full article

This study presents an integrated approach for predicting the spatial distribution of gold, copper, and lithium concentrations using machine learning, geostatistical methods, and multivariate geospatial data. The problem is formulated as a spatially dependent multivariate regression task, distinguishing it from traditional classification-based mineral prospectivity approaches. A unified database was developed, incorporating geochemical indicators, geomorphometric terrain parameters, remote sensing data, and spatial coordinates. Correlation analysis with an adaptive threshold was applied to optimize the feature set and improve model robustness. The results show that linear methods are limited in capturing nonlinear relationships, while ensemble methods provide significantly higher predictive accuracy. In some cases, geostatistical methods achieve the best performance, emphasizing the importance of spatial structure. Feature importance analysis indicates that gold prediction is primarily driven by geochemical indicators, spatial coordinates, and terrain characteristics. Results for copper and lithium confirm the general applicability of the proposed approach. Overall, the study demonstrates the effectiveness of combining machine learning and geostatistics for modeling geochemical processes. Full article

Gallium (Ga) in coal is a nationally emerging strategic mineral resource, yet research on using petrophysical methods to detect the spatial variation in critical metals in coal seams remains limited. Analyzing the distribution characteristics of Ga-rich coal using geophysical well-logging methods is of great significance for the development and utilization of Ga. This study introduces a quantitative method for predicting Ga-rich laminations in ultra-thick bituminous coal seams by integrating: (i) wireline-log-based lithofacies classification, (ii) lithofacies-constrained mineral inversion, and (iii) lithofacies-constrained and laboratory-established Ga–mineral correlations. The coal seam was first classified into four distinct lithofacies types—(i) parting, (ii) medium-ash coal (MA), (iii) low-ash coal (LA), and (iv) extra-low-ash coal (ELA)—through integration of conventional wireline log interpretation, cluster analysis, and XGBoost machine learning. Second, lithofacies-constrained Ga–host mineral associations were established by integrating core sample analysis, correlation analysis, and linear regression modeling. Third, mineral content predictions for each lithofacies were obtained through wireline-log-based mineral inversion, constrained by petrophysical boundaries. Finally, prediction uncertainties were evaluated using Markov Chain Monte Carlo (MCMC) simulation, while Ga-rich laminations were predicted by integrating log-derived mineral inversion results with regressed Ga prediction models. The results demonstrate strong agreement between mineral inversion and XRD analyses within uncertainty ranges, achieving a prediction accuracy of 73.6% for Ga. This validated methodology presents a novel approach for quantifying Ga concentrations in coal, as demonstrated through a case study. Full article

Artisanal gold mining (AGM) activities are increasing globally and rely on rudimentary methods, such as amalgamation, to recover gold. In this study, mercury (Hg) metallurgical balances were conducted in 18 operations and gold (Au) balances in 35 operations, at a processing site serving approximately 4000 miners in the Korokpa mining area in Minna, Niger State, Nigeria. Ore processing involves grinding ore in hammer mills to below 1 mm, concentrating gold in sluice boxes, followed by amalgamating free gold particles in the concentrate. The results showed an average Au feed grade of 1.74 g/t and an average Au recovery from gravity concentration of 42.7%. Chemical analysis of the gravity separation tailing size fractions indicates that Au is lost in coarse fractions due to poor Au liberation and in fine fractions due to inefficiency in the sluicing process. Hg lost in the tailings was calculated as the mass balance difference between Hg added and the sum of Hg recovered through filtration and volatilized Hg in bonefires. It was found that 34% of Hg was lost during amalgamation, by volatilisation (18%) and with tailings (17%). The Hg lost-to-Au produced ratio was 2.6. By optimising procedures for grinding, classification, and concentration, the efficiency of recovery can be improved. Implementing a simple Hg recovery method, such as using a retort for condensation, and improving amalgam heating time can help miners minimise environmental loss. Full article

As an efficient solid–liquid separation device, the hydrocyclone is widely applied in various industrial fields such as coal preparation and oil impurity removal, and its classification performance directly determines the efficiency of industrial separation operations., As the core separation zone of the hydrocyclone, the cone segment, its structure and the number of cone angles directly affect the flow field distribution characteristics and particle classification performance of the hydrocyclone. To reveal the regulation mechanism of the combined cone angles on the classification performance of hydrocyclones, numerical analysis and experimental verification methods were adopted to investigate the internal flow field and classification performance of hydrocyclones under different cone angle combinations. The evolution laws of velocity field, pressure field, turbulence characteristics, and particle classification effect under different configurations were systematically explored. The results show that the basic characteristics of the core flow field of the hydrocyclone do not change essentially with the increase in the number of cone segments, but the amplitude, distribution, and stability of flow field parameters are significantly regulated. The three-cone configuration achieves the optimal flow field synergy effect: the amplitude of the high turbulence intensity zone is lower and concentrated near the central axis; the zero-velocity envelope surface is stably maintained at approximately 8 mm in the core separation zone; and the full axial fluctuation of the air core is gentle, which effectively inhibits random particle diffusion and flow pattern mixing. In terms of separation performance, the three-cone configuration exhibits the highest classification efficiency in the core range of sub-coarse particles (10~30 μm), with the cut size (approximately 17.5 μm) in a reasonable range, the steepness index reaching a peak value (approximately 0.55), and the pressure drop (approximately 1.8 × 10⁵ Pa) and split ratio (2.8%) achieving synergistic optimization, balancing separation accuracy and energy consumption control. The single-cone configuration causes flow field disturbance due to the one-time contraction of the flow channel, while the four-cone configuration falls into the dilemma of “high pressure drop–marginal performance gain”, and neither achieves optimal performance. The regulation law of the number of cone segments revealed in this study provides a scientific basis for the structural optimization and engineering application of multi-cone hydrocyclones, and is of great significance for improving the particle classification efficiency in fields such as wastewater treatment and mineral processing. Full article

Hydrocyclones are widely applied devices in mineral processing due to their simple design, high capacity and low operational costs. Some of the main applications are classification in closed grinding circuits and desliming, as well as dewatering. However, hydrocyclones have an inherent inefficiency known as the fine particles bypass to the underflow stream, often associated with entrainment by water flow. Several approaches have been proposed to mitigate fine particle bypass, such as optimizing hydrocyclone design, adjusting apex and vortex finder diameters, water injection systems and improved inlet design. The objective of the present work was to assess hydrocyclone performance on different apex and vortex diameter combinations, seeking the reduction in fine particles bypass to underflow on the Paragominas bauxite processing industrial desliming circuit. Two different bauxite samples were used in a hydrocyclone classification test work, carried out on a specially built pilot plant. Six different combinations of apex and vortex were evaluated in a 254 mm diameter hydrocyclone, covering a range of apex-to-vortex diameters from 0.38 to 0.57. The results indicate operating conditions that significantly reduce fine particles bypass to underflow, increasing classification efficiency with minor effects in overflow selected size distribution parameter—d₉₅. Accordingly, smaller apex-to-vortex ratios result in overall better performances, reducing fine particles bypass to underflow from 33% to 7%, as well as reducing the partition curve slope from 0.52 to 0.21 for one of the tested samples. Significant benefits are also obtained in terms of reducing the contents of reactive silica in the underflow of the optimized desliming hydrocyclone. Full article

This paper presents a comparative determination of rare earth elements (REEs) in sandstone-type uranium ore samples from Kazakhstan using a proposed rapid ICP-MS method following microwave digestion in a MARS 6 system with a mixed acid solution of HNO₃, HCl, and HF. To validate the rapid REE determination method, comparative measurements were performed using a certified uranium ore reference material provided by Ore Research & Exploration, representing sandstone-hosted uranium mineralization from a Tanzanian deposit (OREAS 120). Fractionation patterns of chondrite-normalized REEs in uranium ores from Kazakhstan were evaluated. Comparative data on REE distribution in sandstone- and magmatic-type uranium deposits from Australia and Tanzania are presented. Uranium ores of magmatic- and sandstone-hosted types exhibit distinct REE distribution patterns, reflecting differences in the nature of ore-forming processes. This study provides chondrite-normalized REE distribution profiles for major uranium deposit types from three countries, which are subsequently used to assess uranium ore paragenesis through simple linear regression analysis. This study is intended as an applied comparative synthesis of REE fractionation patterns in genetically contrasting uranium deposits, with particular relevance to deposit classification. Full article

Water quality plays a central role in determining the environmental performance of pond-based tropical aquaculture systems. This study aimed to evaluate the relative environmental performance of different tropical pond-based aquaculture systems by identifying multivariate water quality patterns that allow their discrimination and comparison under commercial production conditions. Four pond-based production systems were evaluated: an aquaponic system (APS), a recirculating aquaculture system (RAS), a conventional earthen pond system (CEP), and an integrated rice–chame system (RCS). Fourteen physicochemical water quality variables were monitored throughout the production cycle under real commercial conditions using a comparative observational design. Multivariate discriminant analysis was applied to identify the variables with the highest discriminatory power and evaluate the ability of water quality patterns to correctly classify observations among production systems. The results revealed a clear multivariate separation between technologically intensive systems (APS and RAS) and less intensive and integrated systems (CEP and RCS), reflecting distinct water quality structures and environmental functioning. Variables associated with mineralization and nutrient dynamics, including electrical conductivity, dissolved solids, turbidity, phosphates, chlorides, dissolved oxygen, nitrites, and temperature, contributed most strongly to system discrimination. The discriminant functions achieved a high overall correct classification rate, demonstrating the robustness of the multivariate approach. These findings support the use of water quality variables as consistent environmental signatures for distinguishing tropical pond-based aquaculture systems, providing an operational framework for assessing their relative environmental performance. Discriminant analysis emerges as a valuable tool for system characterization and comparative evaluation, supporting environmentally informed management and optimization of chame aquaculture under tropical conditions. Although water quality represents a robust integrative indicator, it captures only one dimension of environmental performance, and additional factors such as production efficiency, energy use, and effluent characterization should be incorporated in future studies to achieve a comprehensive sustainability assessment. Full article

Elemental migration and enrichment are important processes influencing tea plant growth and the assembly of rhizosphere bacterial communities within the rock–soil–plant continuum. This study explores how soil parent materials (granite, quartz schist, and sericite schist) are potentially associated with these processes and their observed associations with the elemental composition of tea leaves. Exploratory statistical analyses revealed distinct, lithology-specific biogeochemical patterns that serve as a foundation for hypothesis generation. In granite soils, chlorite correlated with the mobility of Cr, Pb, Cu, Ni, Mg, and Na, coinciding with shifts in the relative abundances of Verrucomicrobia, Armatimonadetes, and Chloroflexi. In quartz schist, kaolinite exhibited notable correlations with the dynamics of Pb, Cr, Ni, Zn, and As, which were statistically linked to Planctomycetes, Proteobacteria, and Acidobacteria. Complex mineral–microbe interactions were observed in sericite schist soils, where clay minerals (e.g., chlorite, illite) were closely associated with the migration of multiple elements (Pb, K, Ca, Cd, As, Al, Fe, Zn), paralleling structural variations in communities of Actinobacteria, Planctomycetes, Chloroflexi, and Proteobacteria. Potassium (K), calcium (Ca), and manganese (Mn) showed bioaccumulation tendencies in tea leaves across all lithologies, with an enrichment capacity order of Ca > K > Mn > Mg > Na > Al. Exploratory Classification and Regression Tree (CART) analysis suggested that the migration of K, Ca, Cu, Zn, and Hg corresponded most closely with their soil concentrations. Manganese (Mn) exhibited a mineral-associated trend, with kaolinite content as a potential correlate, while cadmium (Cd) migration was statistically linked to the relative abundance of Armatimonadetes. These findings highlight potential candidate relationships between mineralogy, microbes, and elemental mobility rather than confirming causal mechanisms, emphasizing the need for further validation in larger or experimental datasets. Full article

Prolonged water injection in unconsolidated sandstone reservoirs can induce pore rearrangement and modify flow pathways, thereby affecting reservoir performance. However, quantitative characterization of pore evolution in both temporal and spatial dimensions remains limited. This study investigates the mechanisms of pore-structure evolution during extended injection through a series of multi-scale experiments. Scanning electron microscopy and X-ray diffraction analyses were employed to compare mineral composition and microstructural characteristics before and after injection, while in situ nuclear magnetic resonance (NMR) monitoring captured the dynamic evolution process, enabling pore-size classification from T₂ spectra and fractal assessment of structural complexity. Segmented NMR measurements at different distances further resolved spatial heterogeneity. The results show that prolonged water injection reduced permeability by 10.4–32.1%, whereas porosity exhibited only minor variation, indicating that the decline in flow capacity is primarily controlled by pore–throat structural adjustment rather than pore volume loss. Mineralogical redistribution and fine-particle migration decreased the median pore radius by 21.5–51.8% and the micropore fractal dimension by 23.8–76.5%, with stronger responses observed at higher permeabilities, while meso- and macropore fractal dimensions remained nearly unchanged, indicating preferential modification of micropores with preservation of the main connected flow framework. Consistently, NMR responses reveal pronounced spatial heterogeneity along the flow direction. The NMR signal changes at the injection end were 11.2–18.4% and 7.7–21.7% during the early and intermediate stages, respectively, both exceeding those at the distal end (2.9–12.4% and 1.9–17.1%). These results indicate a downstream-attenuating structural modification gradient. The findings provide new insights into pore-structure evolution during prolonged water injection and offer a scientific basis for optimizing water-injection strategies in unconsolidated sandstone reservoirs. Full article

With its excellent physical and mechanical properties, granite is often the first choice for the foundation material for dams in water conservancy engineering. However, alteration can profoundly change the mineral composition, structure, and mechanical behavior of deep granite, posing critical challenges to project safety. The Quwu Mountain area in Baiyin, Gansu Province, a proposed pumped storage reservoir, exposes extensive Silurian granite. Engineering investigation shows that different levels of clay and hydrothermal alteration have taken place in the granite rock mass, and the level of alteration exhibits a distinct vertical zonation as revealed by borehole core logging. In this study, we quantitatively characterize the porosity, compressive strength, wave velocity, and shear parameters of altered granite of different degrees through mineralogical analysis, laboratory tests, and in situ testing. In order to guide the construction in this area, we establish a classification system that distinguishes weak, moderate, and strong alteration degree, based on macroscopic features, RQD, and clay mineral content. Results of this paper show that alteration is dominated by potassium feldspathization and kaolinitization, leading to increased porosity (4–10%) and structural loosening. Strongly altered granite exhibits severe mechanical degradation, moderately altered granite retains medium strength, and weakly altered granite approaches the properties of fresh rock. This research can provide technical support for engineering safety design and risk prevention in the Quwushan reservoir area, but its applicability to other regions requires further validation. Full article

The Kocayayla Pb–Zn ± Cu vein-type mineralization is located in the Biga Peninsula, northwestern Türkiye. This study aims to constrain the geological, geochemical, and isotopic characteristics of the mineralization and to clarify its genetic classification. The deposit is hosted mainly by andesitic and basaltic andesitic rocks as well as schists and is structurally controlled by E–W-trending strike-slip faults. Mineralogical and petrographic identifications, XRD analyses, whole-rock geochemistry, and sulfur isotope data were integrated to evaluate ore-forming processes. Mineralization is temporally and spatially associated with propylitic and phyllic to argillic alteration and is concentrated within zones of intense silicification and chloritization, accompanied by quartz, sericite, kaolinite/nacrite, chlorite, and carbonate assemblages. The ore assemblage is dominated by galena, sphalerite, and subordinate chalcopyrite, with minor fahlore-group minerals. Rare earth element patterns of ore samples (whole rock) overlap with those of the wall rocks, whereas Pb–Zn enrichment reflects selective hydrothermal metal transport. Sulfur isotope compositions show limited internal variation and indicate sulfur derived predominantly from H₂S-dominated magmatic–hydrothermal fluids. Regional comparison of δ³⁴S datasets and reported Au contents across the Biga Peninsula indicates that Au-rich intermediate-sulfidation epithermal systems exhibit broader and more variable sulfur isotope ranges, whereas Au-poor intermediate-sulfidation epithermal systems show relatively restricted and near-zero δ³⁴S values. These features collectively support the classification of the Kocayayla mineralization as an Au-poor intermediate-sulfidation epithermal Pb–Zn system. Full article