Search Results (744)

The research of this paper was carried out on the low-temperature (100 ± 2 °C) pressure (0.2–0.8 MPa) leaching of pyrite, chalcopyrite and their mixture. According to experiments on chalcopyrite dissolution, increasing the oxygen pressure from 0.2 up to 0.8 MPa had a slight effect on chalcopyrite dissolution. Oxygen pressure and initial sulfuric acid concentration in the range of 10–50 g/L had the greatest positive effect on the pyrite oxidation. The SEM and EDX mappings indicate the chalcopyrite and pyrite surfaces to be passivated by elemental sulfur. The oxidation degree of pyrite in its mixture with chalcopyrite increased significantly from 54.5 up to 80.3% in 0–240 min. The reaction time is relative to the dissolution of the individual mineral, while the dissolution of chalcopyrite remained virtually unchanged. The addition of Cu (II) and Fe (III) ions does not influence pyrite dissolution when chalcopyrite is added in a leaching process, which can be explained by the formation of an electrochemical link between the minerals. The positive effect of chalcopyrite addition is associated with a decreased formation of elemental sulfur on the surface of pyrite. The described method can be used for the hydrometallurgical processing of copper raw materials with increased pyrite content, as well as for the pretreatment of copper concentrates with gold-rich pyrite concentrates to increase the recovery of gold and silver. Full article

Understanding paleo-ice flow chronology is essential for reconstructing past ice mass dynamics, interpreting the current landscape, and identifying the sources of Quaternary sediments in deglaciated regions. A recent systematic mapping of striated bedrock and streamlined landforms south of Lake Mistassini in Canada reveals a complex sequence of five ice flows. The earliest flow was directed to the southeast (SE) and originated from a NE-SW ice divide located northwest of Lake Mistassini at the Last Glacial Maximum. A progressive clockwise rotation of this ice divide, likely triggered during the early deglaciation, appears to have generated ice flows toward the south–southeast (SSE) and then toward the south (S). During the later stages of deglaciation, the flow originated from the Québec–Labrador Dome, initially toward the south–southwest (SSW) and then toward the southwest (SW). This study presents new data on ice flows south of Lake Mistassini and shows that the southward and south–southeastward ice events occurred before the late stage of deglaciation. This interpretation contradicts some previous studies and will contribute to the discussion on the dynamics of the Laurentide Ice Sheet in the Mistassini area and support mineral exploration efforts in the region. Full article

Mineral resources are of great significance in the development of the national economy. Prospecting and forecasting are the key to ensure the security of mineral resources supply, promote economic development, and maintain social stability. The methods for prospecting prediction have evolved from qualitative to quantitative prediction, from empirical research to mathematical analysis. In recent years, deep learning algorithms have gradually entered the attention of geologists due to their robust learning and simulation ability in the application of prospecting prediction. Deep learning algorithms can effectively analyze and predict data, which have great significance in improving the efficiency and accuracy of mineral exploration. However, there are not many specific examples of their application in mineral exploration prediction, and researchers have not yet conducted a comprehensive discussion on the advantages, disadvantages, and accuracy of deep learning algorithms in mineral prospectivity mapping applications. This paper reviews and discusses the application of deep learning in prospecting prediction, highlighting the challenges faced by deep learning in the application of prospecting prediction in data preprocessing, data enhancement, system parameter adjustment, and accuracy evaluation, and puts forward specific suggestions for research in these aspects. The purpose of this paper is to provide a reference for the application of deep learning to researchers and practitioners in the field of prospecting prediction. Full article

This study integrates aeromagnetic data with geological information to develop a consistent interpretation of both shallow and deep structural frameworks at various depths in the Missiakat Al Jukh area, located in the Central Eastern Desert, Egypt. The research begins by processing reduced-to-the-north magnetic pole (RTP) anomalies, using Fast Fourier Transformation (FFT) techniques to distinguish between local residual structures and broader regional features. This multi-scale approach enables a more detailed understanding of the geological complexity in the region, revealing its subsurface structures. Advanced geophysical methods such as upward continuation, Euler deconvolution, source parameter imaging (SPI), and global particle swarm optimization (GPSO) were applied to further refine the determination of structural depths, offering critical insights into the distribution and orientation of geological features at varying depths. The study reveals dominant structural orientations aligned in the NNW-SSE, ENE-WSW, north–south, and east–west directions, reflecting the region’s complex tectonic history. This research is of great importance in terms of sustainability. By delivering detailed subsurface maps and providing more accurate depth estimates of basement rocks (between 0.6 and 1.3 km), it contributes to sustainable resource exploration in the region. A better understanding of the geological structure helps minimize the environmental impact of exploration by reducing unnecessary drilling and concentrating efforts on areas with higher potential. Additionally, the use of non-invasive geophysical techniques supports the transition toward more environmentally conscious exploration practices. The integration of these advanced methods promotes a more sustainable approach to mineral and resource extraction, which is crucial for balancing economic growth with environmental preservation in geologically sensitive areas. Ultimately, this work provides a thorough geological interpretation that not only aids future exploration efforts but also aligns with the global push for sustainable and eco-friendly resource management. Full article

Three nano-resolution petrological microtextures were discovered in the siliceous shale at the bottom of the Longmaxi Formation in the Zigong area, Sichuan Basin. Based on observations of the occurrences of the minerals, organic matter, and organic matter pores in the different microtextures and analysis of their relationships by means of nano-resolution petrological image datasets obtained using the Modular Automated Processing System (MAPS 3.18), the formation mechanism of the siliceous shale was studied. The results show that the strong modification of clay-rich sediments by a deep-water traction current was the basis for the formation of the siliceous shale. The clay-rich sediments were converted into flocculent sediments rich in oxygen and nutrients via agitation and transport by the deep-water traction current, providing space and a material basis for microbes to flourish. Under the continuous activity of the deep-water traction current, the clay-rich sediments were transformed into microbial mats, in which in situ terrigenous detrital quartz and feldspar, endogenous detrital calcite, authigenic dolomite, and dolomite ringed by ferrodolomite were scattered. During the burial stage, the microbial mats were lithified into the siliceous shale composed of three petrological microtextures. Microtexture I was mainly transformed by microbes. Microtexture II was formed via lithification of the residual clay-rich sediments. Microtexture III was composed of migratory organic matter filling hydrocarbon-generating pressurized fractures. Due to the universality of deep-water traction flow and the diversity of microbes in deep-water sediments, we firmly believe that more and more deep-water microbialites will be discovered worldwide through systematic characterization of nano-resolution petrology with the booming development of the shale gas industry. Full article

Mineral prospectivity mapping (MPM) is crucial for efficient mineral exploration, where prospective zones are identified in a cost-effective manner. This study focuses on generating prospectivity maps for hydrothermal polymetallic mineralization in the Feizabad area, in northeastern Iran, using unsupervised anomaly detection methods, i.e., isolation forest (IForest) and deep isolation forest (DIF) algorithms. As mineralization events are rare and complex, traditional approaches continue to encounter difficulties, despite advances in MPM. In this respect, unsupervised anomaly detection algorithms, which do not rely on ground truth samples, offer a suitable solution. Here, we compile geospatial datasets on the Feizabad area, which is known for its polymetallic mineralization showings. Fourteen evidence layers were created, based on the geology and mineralization characteristics of the area. Both the IForest and DIF algorithms were employed to identify areas with high mineralization potential. The DIF, which uses neural networks to handle non-linear relationships in high-dimensional data, outperformed the traditional decision tree-based IForest algorithm. The results, evaluated through a success rate curve, demonstrated that the DIF provided more accurate prospectivity maps, effectively capturing complex, non-linear relationships. This highlights the DIF algorithm’s suitability for MPM, offering significant advantages over the IForest algorithm. The present study concludes that the DIF algorithm, and similar unsupervised anomaly detection algorithms, are highly effective for MPM, making them valuable tools for both brownfield and greenfield exploration. Full article

Since their creation, geoparks have been among the fastest growing natural environments. Their attractiveness is one of the most important factors for the success of this natural destination. Despite their importance, a bibliometric analysis on geopark attractiveness is missing from the studied databases. The aim of this paper is to synthesise a heterogeneous body of knowledge of geoparks in terms of attractiveness, highlighting the evolution and breadth of the research field. To achieve this, the following objectives were set: (a) to adopt a method based on functions provided by the bibliometrix package to automatically combine databases, namely WoS, Scopus, PubMed and Dimensions, detailing the method used and (b) to analyse the bibliometric indicators in order to identify the trends in the literature and the possible directions for future research. The applied methodology was based on bibliometric analysis using R for non-coders. From the 707 documents retrieved, the validation process resulted in 349 eligible documents published between 2002 and 2024, on which the analysis was carried out. The current study elaborated a method and examined the key information on the topic trends, which were given by production performance, productivity trends, spatial analysis and abstract approach analysis. Additionally, strategic mapping of the conceptual context was performed. Thus, the result provides a description of the automatic method with practical applications. As discerned from the three-dimensional analysis (spatial, temporal and size), the emerging research directions within scientific creativity encompassed (1) forms of tourism practiced in geoparks, especially focused on ecotourism and volcanic tourism; (2) geomorphological features, mineral springs and mud volcanoes; (3) aesthetic aspects, scenic sites and mining heritage; and (4) methodology, data analysis and modelling methods across different regions and countries. Full article

Rare earth elements (REEs) hold significant industrial, scientific, and modern technological worth. This study focused on detecting and quantifying REEs in various geological ore samples. These samples were collected from different REE-bearing locations recommended by geological experts. The analysis was conducted using laser-induced breakdown spectroscopy (LIBS) and laser ablation time-of-flight mass spectrometry (LA-TOF-MS). In this work, LIBS methodology was employed using three different configurations: standard LIBS, LIBS with an applied magnetic field, and LIBS with both an applied magnetic field and target sample heating within an optimal temperature range. Elements from the REE group, specifically lanthanum (La), cerium (Ce), and neodymium (Nd), were identified and quantified. To detect, quantify, and validate the results from LIBS and LA-TOF-MS, we utilized an array of analytical techniques—Energy-Dispersive X-ray Spectroscopy (EDX), Energy-Dispersive X-ray Fluorescence Spectrometer (ED-XRF), and Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). Interestingly, the quantitative results for REEs (La, Ce, and Nd) in the ore samples obtained using the LIBS technique with various configurations were found to be in agreement with those from LA-TOF-MS, EDX, XRF, and ICP-OES. In addition, LIBS enables detailed microchemical imaging, allowing the map of the spatial distribution of elements within the mineral–ore matrix. The high-resolution microscale elemental mapping of REEs was accomplished using the emission lines Ce (II) at 446.0 nm, La (II) at 492.1 nm, and Nd (II) at 388.8 nm. By integrating multiple analytical techniques, our study enabled the construction of a complete elemental distribution map, providing new insights into the geochemical processes and mineral composition of rare earth ores, while advancing geochemistry and contributing valuable data for rare earth resource exploration. Full article

Groundwater salinization poses a critical threat to sustainable development in arid and semi-arid rurbanizing regions, exemplified by Kerman Province, Iran. This region experiences groundwater ecosystem degradation as a result of the rapid conversion of rural agricultural land to urban areas under chronic drought conditions. This study aims to enhance Groundwater Pollution Risk (GwPR) mapping by integrating the DRASTIC index with machine learning (ML) models, including Random Forest (RF), Boosted Regression Trees (BRT), Generalized Linear Model (GLM), Support Vector Machine (SVM), and Multivariate Adaptive Regression Splines (MARS), alongside hydrogeochemical investigations, to promote sustainable water management in Kerman Province. The RF model achieved the highest accuracy with an Area Under the Curve (AUC) of 0.995 in predicting GwPR, outperforming BRT (0.988), SVM (0.977), MARS (0.951), and GLM (0.887). The RF-based map identified new high-vulnerability zones in the northeast and northwest and showed an expanded moderate vulnerability zone, covering 48.46% of the study area. Analysis revealed exceedances of WHO standards for total hardness (TH), sodium, sulfates, chlorides, and electrical conductivity (EC) in these high-vulnerability areas, indicating contamination from mineralized aquifers and unsustainable agricultural practices. The findings underscore the RF model’s effectiveness in groundwater prediction and highlight the need for stricter monitoring and management, including regulating groundwater extraction and improving water use efficiency in riverine aquifers. Full article

In recent years, machine learning (ML) has been extensively used for the quantitative prediction of mineral resources. However, the accuracy of prediction models is often influenced by data quality, feature selection, and algorithm limitations. This research investigates the benefits of data-driven feature optimization techniques in enhancing model accuracy. Using the Lhasa region in Tibet as the study area, this research applies ensemble learning methods, such as random forest and gradient boosting tree techniques, to optimize 43 feature variables encompassing geology, geochemistry, and geophysics. The optimized feature variables are then input into a support vector machine (SVM) model to generate a prospectivity map. The performance characteristics of the SVM, RF_SVM, and GBDT_SVM models are evaluated using ROC curves. The results indicate that the feature-optimized GBDT_SVM model achieves superior classification accuracy and prediction effectiveness, demonstrating that feature optimization is a necessary step for mineral prospectivity mapping, as it can significantly improve the performance of mineral prospectivity prediction. Full article

Shale gas is a prospective cleaner energy resource and the exploration and development of shale gas has made breakthroughs in many countries. Structure deformation is one of the main controlling factors of shale gas accumulation and enrichment in complex tectonic areas in southern China. In order to estimate the shale gas capacity of structurally deformed shale reservoirs, it is necessary to understand the systematic evolution of organic pores in the process of structural deformation. In particular, as the main storage space of high-over-mature marine shale reservoirs, the organic matter pore system directly affects the occurrence and migration of shale gas; however, there is a lack of systematic research on the fractal characteristics and deformation mechanism of organic pores under the background of different tectonic stresses. Therefore, to clarify the above issues, modular automated processing system (MAPS) scanning, low-pressure gas adsorption, quantitative evaluation of minerals by scanning (QEMSCAN), and focused ion beam scanning electron microscopy (FIB-SEM) were performed and interpreted with fractal and morphology analyses to investigate the deformation mechanisms and structure of organic pores from different tectonic units in Silurian Longmaxi shale. Results showed that in stress concentration areas such as around veins or high-angle fractures, the organic pore length-width ratio and the fractal dimension are higher, indicating that the pore is more obviously modified by stress. Under different tectonic backgrounds, the shale reservoir in Weiyuan suffered severe denudation and stronger tectonic compression during burial, which means that the organic pores are dominated by long strip pores and slit-shaped pores with high fractal dimension, while the pressure coefficient in Luzhou is high and the structural compression is weak, resulting in suborbicular pores and ink bottle pores with low fractal dimension. The porosity and permeability of different forms of organic pores are also obviously different; the connectivity of honeycomb pores with the smallest fractal dimension is the worst, that of suborbicular organic pores is medium, and that of long strip organic pores with the highest fractal dimension is the best. This study provides more mechanism discussion and case analysis for the microscopic heterogeneity of organic pores in shale reservoirs and also provides a new analysis perspective for the mechanism of shale gas productivity differences in different stress–strain environments. Full article

Mississippi Valley-type (MVT) Pb-Zn deposits are a subtype of sedimentary-hosted mineralization. These deposits are hosted by carbonate sequences in passive-margin tectonic settings. This paper uses the Fry technique and distance distribution analysis to model the spatial distribution pattern of MVT Pb-Zn deposits in the west of Semnan province (Iran) and their association with some geological features, aiming at mapping mineral prospectivity in the area. The modeling results reveal that NE–SW trending faults and Permian-Cretaceous dolomites and limestone are, respectively, major structural and lithological controlling factors of mineralization that operate as conduits and physicochemical subsystems of ore formation. The integration of the corresponding evidence maps of the controlling factors with a model of the geochemical signature of MVT Pb-Zn deposits through a supervised random forest approach, a machine learning technique, gains an exceptional prospectivity map predicting 100% of the known MVT Pb-Zn deposits in only 15% of the study area, which is an achievement. The recognized targets can be planned for further exploration. Full article

Developments in scanning electron microscopy (SEM) have introduced instant live coloured SEM images based on elemental composition. Here, we use a technique utilising a Unity BEX detector system, with collection speeds up to 100 times faster than typical standard energy-dispersive X-ray (EDX) analysis systems, to obtain large area backscattered and elemental composition maps of heavy mineral (HM) suites from a sample from an Oligocene fluvio-deltaic system in the Central Myanmar Basin. The fast X-ray collection rate and a high-resolution backscattered (BSE) detector allow for rapid imaging of polished blocks, thin sections, and stubs. Individual HM species can be rapidly classified, allowing for the subsequent collection of compositional and morphological metrics. In addition, the identification of grains such as zircon and apatite allow for further analysis by cathodoluminescence (CL) to identify and record the presence of growth zonation, which is critical for further U-Pb geochronology and thermochronology, using fission track analysis of apatite, zircon, and titanite. The sample used in this study contains a diverse heavy mineral suite due to the complex tectonic history of Myanmar, juxtaposing multiple metamorphic basement terranes alongside volcanic arcs and obducted ophiolites. This, along with the textural and mineralogical immaturity of the sediments themselves (governed by short transport systems and the rapid weathering of the sources), means that a wide variety of heavy mineral species can be identified and tested using this new technique, which provides a time-efficient method in comparison to traditional optical techniques. As the Unity BEX detector is located at the polepiece, it is relatively insensitive to working distance; in addition, the geometry of paired X-ray detectors on either side of the polepiece (at 180°) means that the system is also capable of fully characterising individual particles, on uncut and unpolished grain mounts, without artefacts such as particle shadowing. The development of a more comprehensive heavy mineral EDX database (library) will improve the accuracy of this new technique, as will the correlation with other techniques such as Raman spectroscopy. Full article

The formation of Paleozoic silica–iron-rich sedimentary rocks in the Urals volcanic-hosted massive sulfide (VHMS) deposits is considered a result of seafloor alteration of hyaloclastites mixed with calcareous/organic or sulfide material. These rocks host various Ti mineral phases pointing to the transformation of precursor metacolloidal TiO₂ phases to disordered anatase during seafloor alteration of hyaloclastites, which was later converted to globules and clusters and further to diagenetic rutile. The LA-ICP-MS analysis showed that the Ti content of hyaloclasts partly replaced by finely dispersed Si–Fe aggregates increases to 540–2950 ppm and decreases (<5 ppm) in full Si–Fe pseudomorphs after hyaloclasts. LA-ICP-MS element mapping reveals the enrichment in V, U, Cr, W, Nb, Pb, and Th of the anatase globules and the local accumulation of Zr, Y, and REE on their periphery. Corrosive biogenic textures in the outer zones of some hyaloclasts and biomorphic aggregates in rocks contain anatase particles in assemblage with apatite indicating the biophilic properties of Ti. This work fills the knowledge gaps about Ti mobilization during low-temperature seafloor alteration of hyaloclastites in VHMS deposits. Full article

The Zaozigou gold deposit is recognized as one of the largest and most significant independent gold deposits in northwest China, representing a colossal orogenic gold-antimony deposit. It is imperative to delve into the hydrochemical characteristics and controlling factors within the mining area to unveil the groundwater circulation evolution process and enhance water resource management. In 2018, a comprehensive collection of 50 groups of groundwater samples was conducted, alongside 17 groups of surface water and underground mine water samples in 2023. Compositional descriptive analysis, correlation analysis, Piper three-plot, Gibbs map, ion ratio method, hydrogeochemical simulation and PCA methods were employed to reveal the chemical characteristics and evolution process of groundwater in the Zaozigou gold mine. Furthermore, employing water isotopes theory allowed for the identification of recharge sources and circulation conditions within the Zaozigou Gold Mine. The findings indicated a transition from HCO₃-Na·Ca type polluted water in 2018 to primarily the SO₄-Ca·Mg type and HCO₃·SO₄-Na·Ca·Mg type groundwater hydrochemistry types by 2023. The hydrochemical characteristics of the study area are closely related to the mining depth and time. The chemical characteristics are influenced by various factors such as rock weathering, mineral dissolution, cation exchange processes, atmospheric precipitation as well as human activities related to pollution from industrial mining activities. Full article