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Remote sensing techniques have emerged as highly effective and widely encouraged tools for mineral exploration and lithological mapping. Given the association of various types of metallic minerals with collisional belts, the northern region of Pakistan presents a unique opportunity to explore metallic mineralization related to the orogenic events of the Himalaya-Karakoram-Hindukush ranges, particularly in the Chitral region. However, due to the high relief and rugged area topography, traditional detailed surveys are challenging. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data provide a better alternative for mapping the lithological units and mineralization zones. Several remote sensing methods, including False Color Composite (FCC), Decorrelation Stretch (DS), Principal Component Analysis (PCA), Supervised Classification (Train Random Trees Classification (TRTC)), and Matched Filtering (MF), were used to process the remote sensing data. The detected results revealed the presence of chlorite, limonite, sericite (muscovite), kaolinite, malachite, and calcite and effectively discriminated the lithological units. The spatial distribution of anomalies for muscovite, kaolinite, and limonite correlates with the presence of granitic plutons and carbonate rocks exposed in the region. Additionally, this study identified five new mineralization targets associated with alteration zones, particularly correlated to copper and iron mineralization. The new mineralization zones are well distributed in the valleys of Shishi, Maroi, and Drunil. These findings were confirmed through comprehensive field campaigns, petrographic investigations, X-ray Diffraction (XRD), and spectral measurements. [ABSTRACT FROM AUTHOR]

Mineral grain recognition is an extremely important task in many fields, especially in mineral exploration, when trying to identify locations where precious minerals can possibly be found. The usual manual method would be to collect samples; a specialized individual using expensive equipment would manually identify and then count the grain minerals in the sample. This is a tedious task that is time-consuming and expensive. It is also limited because small portions of areas can be surveyed; even then, it might require extremely long periods. In addition, this process is still prone to human errors. Developing an automatic system to identify, recognize, and count grain minerals in samples from images would allow for more precise results than the time required by humans. In addition, such systems can be fitted on robots that collect samples, take images of the samples, and then proceed with the automated recognition and counting algorithm without human intervention. Vast amounts of land can be surveyed in this way. This paper proposes a modified approach for microscopic grain mineral recognition and classification using hybrid features and ensemble algorithms from images. The enhanced approach also included a modified segmentation approach, which enhanced the results. For 10 classes of microscopic mineral grains, using the modified approach and the ensemble algorithm resulted in an average accuracy of 84.01%. For 8 classes, the average reported accuracy is 94.93% using the Boosting ensemble learning with the C4.5 classifier. The results obtained outperform similar methods reported in the extant literature. [ABSTRACT FROM AUTHOR]

The Dschang area has substantial mineral and geological exploration potentialities. However, its basement is unclear due to lack of studies on mineral and lithology mapping, and other mineralization indices. The lithological units and potential hydrothermal alteration zones in the Dschang area are investigated here using remote sensing; geographic information systems (GIS); and statistical analysis which are essential method for geological exploration. Landsat 9 OLI, ASTER data using False Color Composites (FCC), Band Ratios (BRs), Principal Component Analysis (PCA), Spectral Angle Mapper (SAM), fuzzy-logic methods, and field observations are used to identify the rocks units and potential mineralization. The integration o these multiple methods allowed the identification of orthogneiss, granites and basalts with iron-oxides, hydroxyl and ferrous bearing as potential mineralization in the Dschang area. The Evaluation of the fuzzy membership of each alteration mineral from Landsat 9 OLI and ASTER data indicates that the highest favorability index varies from 0.8 to 1 indicating a rating index related to iron mineralization. From the statistical analysis of the geochemical data, the calcic, alkaline-calcic, and metaluminous to weakly peraluminous I-type character of the Dschang granites prove their parent magma was fertile for mineralization in Rare Earths, Cu, Sn, Mo, Zn, and Pb. In addition, analysis of lineaments illustrated three structural directions in the area (ENE-WSW to NE-SW, N-S to NNE-SSW, and NW–SE). The innovative aspect of this research is the integration and processing of Landsat 9 OLI, Fuzzy, ASTER, statistical geochemical analysis of previous data, and field investigations, which allows for the identification of rock units and potentially mineralized rock formations and defining exploration targets as well. [ABSTRACT FROM AUTHOR]

Mapping alteration zones, a crucial step for mineral exploration, faces challenges in tropical areas. Dense vegetation hides important geological features, recent clay formation hides deeper alterations, and human activities like farming make it more complicated. However, alteration zones are crucial clues for specific ore deposits. We explore two approaches: one based on knowledge and the other on data. The knowledge-driven method involves experienced geologists analyzing GIS layers, including lineaments, drainage patterns, rock types, and topography. They use this data to identify key signs of ore-forming alterations. Translating this expert knowledge into spatial data helps us map alteration zones effectively. While this approach provides good approximations, it lacks direct evidence. The data-driven method involves advanced remote sensing tools like ASTER imagery. High-resolution data allows us to use image processing techniques to extract alteration information. However, conventional techniques face challenges in the tropics due to dense vegetation and human activity. To overcome this, we use machine learning algorithms trained on carefully selected samples. We found that among selected ASTER-derived products of conventional DIP techniques (reflectance, band ratio, PCA, DPCA), directed PCA alone is capable of demarcating alteration for the study area with a total accuracy of 81.41, 83.92, and 84.42% for LR, ANN, and RF, respectively. Besides, we used contextual geological evidence of alteration presence as another validation method. To validate results, we use the knowledge-driven approach again, employing Relative Alteration Indexes. All alteration indicative field and geological knowledge were weighted with the Analytical Hierarchy Process (AHP) and spatially integrated with three probability classes in the GIS platform. This combined strategy reveals that while Random Forest has the highest accuracy, Logistic Regression yields more geologically significant results. The high value of Relative Alteration Indexes representing highly altered zones indicates their successful mapping from both data and knowledge-driven techniques. This study shows the strengths of both approaches in understanding alteration zones in the tropics. By combining expert knowledge with advanced technology, we can pinpoint areas rich in valuable minerals, even in difficult-to-explore places. Our successful test in the South Purulia region suggests similar discoveries are possible in other unknown areas. [ABSTRACT FROM AUTHOR]

In mineral exploration, the ores with different magnetic susceptibility can be well delineated by physical property inversion of magnetic data. However, further wide applications of the magnetic inversion method have been seriously restricted by the existence of strong remanence and the computation of large-scale datasets. To solve these two problems, this study innovatively presents an efficient magnetic inversion algorithm in the presence of strong remanence. The weighted data misfit term and regularization term were used to establish the objective function. The detailed related formulas were derived and presented. For numerical computation, the Taylor series approximation was introduced in the objective function to linearize the relationship between the transformed magnetic data and magnetic susceptibility. The objective function was converted to a general Tikhonov form by several matrix transformations. To realize computational efficiency, a fast randomized algorithm was utilized to efficiently solve the Tikhonov problem and to efficiently determine the suitable regularization parameter along with the generalized cross-validation (GCV) method. Comparative tests on synthetic example show the effectiveness and efficiency of the proposed algorithm. The proposed efficient algorithm is successfully applied to two sets of real magnetic data, and the inversion results are verified. [ABSTRACT FROM AUTHOR]

The use of ultrasonic technology for rock breaking, with its high-frequency and microamplitude impact load, is anticipated to help overcome the challenges of high economic cost and low rate of penetration in mineral resource exploration. A three-dimensional finite-element model of a heterogeneous granite mineral crystal was created using random Voronoi tessellation and cohesive elements. The distribution of the multipolycrystalline structure was achieved in a proportional manner. Then, the differences between conventional cutting and ultrasonic-assisted cutting in rock breaking were compared. The effects of different vibration frequencies (20–35 kHz) and amplitudes (10–50 μm) on crack propagation, crack size, stress distribution, and energy consumption were investigated. The results indicate that higher ultrasonic vibration frequencies significantly improve crack formation, with enhancements ranging from 25.16% to 44.30% compared to conventional cutting. It is important to note that ultrasonic load plays a crucial role in enhancing rock-breaking efficiency by enlarging crack sizes and increasing the number of intergranular tensile cracks instead of solely increasing the total number of cracks. When the vibration frequency is 30 kHz, the crack volume and the proportion of intergranular tensile cracks reach their peak values, while the cutting force decreases to its lowest point, 74.51% lower than conventional cutting. Increasing the vibration frequency is advantageous for reducing mechanical energy consumption. However, once the vibration frequency surpasses 30 kHz, further increasing it does not significantly enhance the ultrasonic effect. Additionally, raising the amplitude can increase the quantity and size of cracks, leading to an effective improvement in the stress situation of the polycrystalline diamond compact cutter. [ABSTRACT FROM AUTHOR]

We present here the first experimental science (consensus)-based mineral prospectivity mapping (MPM) method and its validation results in the form of national prospectivity maps and datasets for PGE–Ni–Cu–Cr and Witwatersrand-type Au deposits in South Africa. The research objectives were: (1) to develop the method toward applicative uses; (2) to the extent possible, validate the effectiveness of the method; and (3) to provide national MPM products. The MPM method was validated by targeting mega-deposits within the world's largest and best exploited geological systems and mining districts—the Bushveld Complex and the Witwatersrand Basin. Their incomparable knowledge and mega-deposit status make them the most useful for validating MPM methods, serving as "certified reference targets". Our MPM method is built using scientific consensus via deep ensemble construction, using workflow experimentation that propagates uncertainty of subjective workflow choices by mimicking the outcome of an ensemble of data scientists. The consensus models are a data-driven equivalent to expert aggregation, increasing confidence in our MPM products. By capturing workflow-induced uncertainty, the study produced MPM products that not only highlight potential exploration targets but also offer a spatial consensus level for each, de-risking downstream exploration. Our MPM results agree qualitatively with exploration and geological knowledge. In particular, our method identified areas of high prospectivity in known exploration regions and geologically and geospatially corresponding to the known extents of both mineral systems. The convergence rate of the ensemble demonstrated a high level of statistical durability of our MPM products, suggesting that they can guide exploration at a national scale until significant new data emerge. Potential new exploration targets for PGE–Ni–Cu–Cr are located northwest of the Bushveld Complex; for Au, promising areas are west of the Witwatersrand Basin. The broader implications of this work for the mineral industry are profound. As exploration becomes more data-driven, the question of trust in MPM products must be addressed; it can be done using the proposed scientific method. [ABSTRACT FROM AUTHOR]

Mineral exploration combined with prospectivity mapping has become the standard process for utilising mineral exploration data. Nowadays, most techniques integrate multiple layers of information and use machine learning for both data-driven and knowledge-driven approaches. This study introduces a novel and generalised methodology for comparing different layers of information by using superpixels instead of pixels to identify similarities. This methodology provides an enhanced statistical representation of regions, facilitating and enabling effective comparisons. Three different statistical distance measures were considered: Kullback–Leibler divergence, Wasserstein distance and total variation distance. We apply the proposed process to data from the Antofagasta region of northern Chile, a well-known area for metallogenic belts, that contain notable copper reserves. Each metric was used and compared, resulting in different similarity maps highlighting interesting mineral exploration areas. The study results lead to the conclusion that the proposed methodology can be applied at different scales and helps in the identification of areas with similar characteristics. [ABSTRACT FROM AUTHOR]

We investigated the non-breeding movements and distribution of West African Crested terns (Thalasseus albididorsalis) breeding in one of the largest colonies of the species, in Guinea-Bissau. Through GPS tracking of six individuals, we show that half of the birds performed long-distance migrations to non-breeding sites located up to 5039 km south of the colony, mostly in Nigeria and Angola. The other half performed short-distance migrations to areas within 340 km of the breeding colony, mainly in Guinea. These results show high variability on the migratory strategies of the West African Crested Tern and suggest low migratory connectivity within the studied population. Regardless of their migratory strategy, all tracked birds visited at least one additional breeding colony both after and before the breeding season. This behaviour might be particularly relevant for species breeding in unstable environments, which is the case of the studied species, nesting in highly dynamic coastal sandbanks. During the non-breeding season West African Crested terns used coastal areas falling within 18 different jurisdictions, highlighting the need for concerted action between all coastal countries of Western Africa, from Senegal to Angola, for the conservation of this species. [ABSTRACT FROM AUTHOR]

Multisource data fusion technology integrates the strengths of various data sources, addressing the limitations of relying on a single source. Therefore, it has been widely applied in fields such as lithological classification and mineral exploration. However, traditional deep learning algorithms fail to distinguish the importance of different features effectively during fusion, leading to insufficient focus in the model. To address this issue, this paper introduces a ResHA network based on a hybrid attention mechanism to fuse features from ASTER remote sensing images, geochemical data, and DEM data. A case study was conducted in the Altay Orogenic Belt to demonstrate the lithological classification process. This study explored the impact of the submodule order on the hybrid attention mechanism and compared the results with those of MLP, KNN, RF, and SVM models. The experimental results show that (1) the ResHA network with hybrid attention mechanisms assigned reasonable weights to the feature sets, allowing the model to focus on key features closely related to the task. This resulted in a 7.99% improvement in classification accuracy compared with that of traditional models, significantly increasing the precision of lithological classification. (2) The combination of channel attention followed by spatial attention achieved the highest overall accuracy, 98.06%. [ABSTRACT FROM AUTHOR]

This study is based on the coupling of geological field data with geophysical studies in the geological mapping of the Central Domain of the Mbere Division (Adamawa-Cameroon). The first step was to calculate the residual grid associated with the geophysical data that will be used for the mapping of the basement. The second step before mapping consisted in calculating the analytical signal associated with the residual grid, used to interpret the geological formations under cover or in intrusion. Subsequently, a geological mapping approach based on the categorisation of geophysical signatures into ranges using the Encom Discover program was adopted. This approach is based on the analysis of the relief of geophysical anomalies. The process of categorisation which is done iteratively, takes into consideration in the same georeferenced space, the resolution of the input data, the variation of the field considered and the structural model previously interpreted. The result of the categorisation is then compared to the spatial distribution of outcrop data to build the geological model. The application of this approach to the Central Domain of the Mbere Division led to the production of a synthetic geological map at a scale of 1/75,000. The superposition of this map to the topographic model allows to observe a concordance between the Cretaceous basins represented by their edge faults and the topographic depression zones. The various mineral indicators (primary and secondary) collected during the fieldwork, combined with the geological model (1:75,000 scale) as well as the Cretaceous deposits and vein intrusion models were used to create the mineral indicators and targets map for this sector. All these models can thus be used to underpin mineral exploration projects in the study area. [ABSTRACT FROM AUTHOR]

Geophysical methods depend on a range of physical and chemical mechanisms, and each method has different sensitivities and resolution of Earth parameters, and over different scale lengths. Additionally, such geophysical methods will also have their own statistical characteristics. Thus, it is a significant challenge to combine different methods through a single inversion framework. Rather than attempting to find an optimal and single model for different geophysical responses, an alternative approach is to use FCM clustering to identify clusters of parameters from two or more different geophysical data sets or models that have similar statistical properties. In this paper, we apply the FCM approach to integrate data and model sets for an array of 100 broadband magnetotelluric (MT) and 100 passive seismic receivers spaced 1 km apart on a 10 by 10 grid above the Vulcan IOCG prospect in the Olympic Cu–Au Province, southern Australia. The challenge for exploration of the Vulcan prospect is that it lies beneath 750 m of regolith sedimentary cover, no single geophysical method provides a unique characterization of the deposit geometry, and drilling is very expensive. Fuzzy c-mean cluster analyses are undertaken in 2D for gravity data and shear-wave velocity model data for basement depths beneath cover and in 3D for shear-wave velocity and electrical resistivity model data. Fuzzy c-mean clustering is shown to provide a simple and efficient method of integrating different geophysical measurements to produce a geological framework that can be verified with drilling information. [ABSTRACT FROM AUTHOR]

The presence of thick (>100 m) and electrically conductive (< 10 Ω.m) ground cover is a major impediment in mineral exploration of deep resources. Iron-oxide copper gold (IOCG) systems in Australia are often associated with a pronounced potential field anomaly, depending on the oxidation state of iron, but the gravity and magnetic field signatures have little vertical resolution unless constrained by drill hole petrophysics. Additionally, IOCG systems have deep magmatic sources, with their conductivity anomalies extending at least to the lithospheric mantle. The Vulcan IOCG prospect lies about 30 km northeast of the Olympic Dam IOCG mine and is defined by a significant gravity anomaly associated with brecciated haematite beneath 850 m of sedimentary cover sequences. To image the physical properties and structural geometry of the Vulcan IOCG prospect, a 100-site broadband MT and passive seismic array was deployed in a 1 km grid over a 9 by 9 km area. Three-dimensional inversion of MT responses resolve structure in three distinct domains. Firstly, broad limestone-quartzite-shale stratigraphy (1–30 Ω.m) in the 850 m cover is delineated in resistivity and corresponds well with changes in shear-wave velocity. Secondly, the region of brecciated haematite below the cover sequences is shown to have lower resistivity (< 60 Ω.m) than surrounding country rock (> 100 Ω.m). Thirdly, a more electrically conductive (< 30 Ω.m) vertical zone that extends > 5 km is imaged a few kilometers to the northeast of the Vulcan haematite breccia, and appears to be linked by a region of low shear-wave velocity in the depth range 1–2 km. Two-dimensional inversion of more regional MT responses along a 200-km line passing through the Vulcan prospect suggest this vertical region of low resistivity links to the lower-crust with anomalous resistivity of < 60 Ω.m in a similar way as imaged beneath the Olympic Dam mine. It is suggested that this conductive region is associated with graphite precipitated from magmatically derived CO2-rich fluids cooling in a reducing environment. [ABSTRACT FROM AUTHOR]

The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES. [ABSTRACT FROM AUTHOR]

Abstract\nKEY POINTSThe Dugald River Shear Zone is a structurally complex, anastomosing shear zone that hosts high-grade Zn mineralisation within the Dugald River Slate. The Dugald River Zn–Pb–Ag mine is situated within the Mount Isa Inlier, Australia and developed through two phases of mineralisation with high-grade Zn mineralisation intimately associated with the development of the brittle–ductile Dugald River Shear Zone. The first phase of mineralisation occurred during the regionally extensive, D2 ductile fold and axial planar cleavage forming event and resulted in the development of a sulfide horizon. This horizon was a preferential site for strain partitioning during early D4 ductile deformation, which resulted in transposition-related concentration and thickening of a sulfide horizon marking the second phase of mineralisation. The Dugald River Shear Zone developed during D4 wherein strain rate incompatibilities between a ductile-deforming sulfide horizon and the brittle-deforming Dugald River Slate resulted in the development of a Riedel shear zone. A high-resolution 3D model of the shear zone was constructed from robust drilling and mapping datasets in which releasing and restraining bends highlight the thickening and thinning of ore lenses, respectively. The 3D model with detailed structural analysis and observations allows for predictive modelling of dilational zones within throughgoing Y-shears, which are prospective sites for remobilisation of high-grade Zn sulfides as the ore body developed coevally with progressive shearing.Detailed structural analysis provides 3D modelling parameters and exploration vectors.The Dugald River Shear Zone developed as an evolved Riedel shear network during dextral transpression.Understanding the shear zone’s 3D geometry and kinematics allows for predictive modelling of ore lens distribution and identification of dilational zones, which are crucial for exploration and mining strategies. [ABSTRACT FROM AUTHOR]

Supramolecular polymers are typical self-assemblies, in which repeating monomer units are bonded together with dynamic and reversible noncovalent interactions. Supramolecular polymers can combine the advantages of polymer science and supramolecular chemistry. Aggregation-induced emission (AIE) means that a molecule remains faintly emissive in the dispersed state but intensively luminescent in a highly aggregated state. AIE has brought new opportunities and further development potential to the field of polymeric chemistry. The integration of AIE luminogens with supramolecular interactions can provide new vitality for supramolecular polymers. Therefore, it is essential for scientists to understand the preparation and applications of AIE-active supramolecular polymers. This review focuses on the recent advanced progress in the preparation of AIE-active supramolecular polymers. In addition, we summarize the newly developed supramolecular polymers with an AIE nature and their applications in chemical sensing, and in vitro and in vivo imaging, as well as the visualization of their structure and properties. Finally, the development trends and challenges of AIE-active supramolecular polymers are prospected. [ABSTRACT FROM AUTHOR]

Recombinase polymerase amplification (RPA) has emerged as a rapid, efficient, and highly sensitive method for nucleic acid amplification, thus becoming a focal point of research in the field of virus detection. This paper provides an overview of RPA, emphasizing its unique double-stranded DNA synthesis mechanism, rapid amplification efficiency, and capability to operate at room temperature, among other advantages. In addition, strategies and case studies of RPA in combination with other technologies are detailed to explore the advantages and potential of these integrated approaches for virus detection. Finally, the development prospect of RPA technology is prospected. [ABSTRACT FROM AUTHOR]

Photothermal hydrogels (PTHs) are considered next‐generation biomaterials as they offer remotely defined biophysical information of the extracellular milieu. PTHs allow precise and non‐genetic control for the regeneration of native tissues, which is the ultimate goal of tissue engineering (TE). Molecular and physical properties of PTHs, such as components, structural configurations, and mechanical characteristics, collectively serve as determinants for understanding the dynamic tissue response and clinical translation. PTHs have entered a period of fruition due to the development of numerous manufacturing technologies and polymeric matrices. Herein, this review comprehensively and meticulously elucidates the mechanisms of regenerative therapeutics underlying the design and fabrication of PTHs. Recent advances in the photothermal principles and various categories of photothermal agents (PTAs) have been extensively discussed. Vital components and structures of PTHs are summarized to enable efficacious and precise therapeutic energy delivery. Emerging applications of PTHs in TE are also demonstrated, which expand the strategies for the intrinsic regeneration of injured tissues. Then deliberate the structural and chemical engineering of PTHs to enhance prognosis while highlighting the challenges associated with clinical translation. In this review, we aim to provide guidance and prospects for exploration and innovation of PTHs in the field of TE. [ABSTRACT FROM AUTHOR]

Reappraisal of legacy reflection seismic data has shown to deliver value in mineral exploration, particularly in brownfield settings. In this work, we demonstrate how the reappraisal and processing of legacy reflection seismic data can be advantageous in the mineral exploration industry. We use today's standard seismic processing tools to improve the imaging of deep and complex geological structures that host mineral deposits. The recovered and processed 25.3 km long legacy seismic profile in this study was acquired in 1983 by the Gold Division of Anglo‐American as part of the Witwatersrand Gold Fields exploration program. This study aims to improve the imaging of the Ventersdorp Contact Reef gold‐bearing horizon (termed reef), a world‐class gold deposit (2 m thick) situated at depths between ∼2400 and ∼4100 m below the ground surface near South Deep Gold Mine in Fochville, South Africa. The final processing results from the pre‐stack time and phase‐shift migration approaches clearly reveal a dipping reflection associated with the gold‐bearing horizon and major steeply dipping faults that crosscut and displace the deposit. The final results are integrated with borehole information, 1D synthetic modelling and aeromagnetic data to constrain the structural interpretation. In particular, 1D synthetic simulation and borehole data constrain the depth position of the gold deposit. The magnetic data provides additional constraints on the complex faulted blocks of the host rocks such as the intrusions that may have a direct impact on ore resources and evaluation. The mining companies, such as South Deep Gold Mine, operating closer to the seismic profiles can use this new structural information to update the current geological models and improve future mine planning and designs, thus providing some insight into the prospectivity of unmined ground. [ABSTRACT FROM AUTHOR]

The dipping dike model has shown to be a useful approximation for mineral deposits. To make this model more realistic, we include the thickness, which yields the depth to the bottom, as an additional parameter. The magnetic anomaly is obtained by combining the anomalies of two infinite dikes, so that the resulting expression is simpler than the classical prismatic models with polygonal cross section. We employ a Metropolis-Hasting (MH) algorithm coupled with the Levenberg-Marquardt (LM) method to invert magnetic profiles assuming a model of multiple dike-like sources. We use a few iterations of the LM method to improve the candidate solutions at the end of each random walk generated by MH. The following parameters are obtained: depth to the top, thickness, half-width, horizontal location of the top center, geological dip, in addition to two effective parameters that depend on the intensity of magnetization and the directions of the induced and remanent fields. For synthetic anomalies, both noise-free and noisy magnetic data are considered, with examples presented for each scenario. These examples highlight the discrepancy between models with finite and infinite sources. They also illustrate the higher accuracy of the hybrid MH-LM method over the pure MH approach. Moreover, two field examples related to mineral exploration have been considered: the Pima copper mine, United States, where the relative differences between the parameters obtained by our algorithm and those known from drilling are not higher than 10%, and a magnetic profile over iron ore deposits located in Laje, northeast Brazil, where the inverted parameters were useful for detailing previous studies. [ABSTRACT FROM AUTHOR]

The Xinjiazui gold deposit marks a notable significance in prospecting within the Back-Longmenshan tectonic belt, located on the northwest margin of the Yangtze Block, China. Despite the extensive studies conducted on this deposit, the source of the ore-forming materials remains unclear, leading to ongoing debates regarding the genesis of this deposit. This study analyzed in situ (EPMA and LA-ICP-MS) trace elements and S-Pb isotopes of arsenopyrite, solely from the principal metallogenic stage and paragenetic with native gold. The results show that the gold in arsenopyrite occurs as invisible gold (Au3+), with an average concentration of 9.38 ppm, whereas the concentrations of magma-related elements, such as W, Sn, Mo, and Bi, are very low. The sulfur isotopes (34S) of arsenopyrite range from 8.32‰ to 10.16‰, aligning closely with the deep metamorphic basement (Pt3l). Meanwhile, the lead isotopes in arsenopyrite display characteristics typical of those found in orogenic belts. A comprehensive analysis of the abundance of gold indicated that the metallogenic materials (sulfur and gold) primarily originated from Pt3l. Additionally, the arsenopyrite thermobarometer indicated that the Xinjiazui gold deposit formed in a medium–low-temperature, medium metallogenic environment (5.57–8.69 km), with a sulfur fugacity (log f (S2)) below −8.4. Combined with previous research results, this study proposes that the Xinjiazui gold deposit is a subduction-related mesozonal orogenic gold deposit. In gold prospecting and exploration in the Back-Longmenshan tectonic belt, it is essential to focus on the distribution of brittle-ductile shear zones and location of the quartz veins associated with pyrite and arsenopyrite mineralization. [ABSTRACT FROM AUTHOR]

In the field of mineral resource exploration, accurate imaging of subsurface structures is key to discovering and assessing potential mineral deposits. Traditional single geophysical methods, limited by terrain variations and their own constraints, can lead to divergent solutions and structural inconsistencies, affecting the reliability of exploration outcomes. To address these challenges, this paper presents a joint inversion method for three-dimensional direct current (DC) resistivity and gravity data based on a deformed hexahedral mesh. The article begins by outlining the current state of development of the method under study and proposes a research plan, followed by a detailed explanation of the theoretical basis and algorithmic implementation of the proposed method. Model tests confirm the advantages of the deformed hexahedral mesh in reducing terrain impacts and enhancing model resolution, demonstrating the optimization and complementarity of the resolution between the two methods after joint inversion. Finally, applying this method to actual data from the Huaniu Mountain area shows that joint inversion not only improves the consistency of the ore belt structure but also provides a more precise analysis for the quantitative interpretation of the distribution of underground mineral resources. This confirms the method's effectiveness and potential in practical geological exploration. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High-precision magnetic surveys are one of the key methods for mineral exploration. These surveys provide valuable information from both shallow and deep sources, and they are effective in inferring concealed geological structures. By identifying the magnetic characteristics of different lithologies, the morphology and spatial distribution of geological bodies can be discerned, which in turn guides mineral exploration indirectly. Haobugao Copper-Zinc Deposit is located in Huanggang-Ganzhuermiao tin polymetallic metallogenic belt in the southern Greater Khingan Range. The ore bodies are primarily hosted within the skarn in the sliding zone between Permian Dashizhai Formation marble and silty slate layers, with some hosted within the contact zone between marble and concealed biotite granite, displaying typical characteristics of skarn-type deposits. Through the detailed geological structure interpretation of high-precision magnetic survey data, combined with geological exploration results, the variations in the occurrences of ore-controlling structures and the morphology and scale of ore-forming geological bodies were analyzed and summarized. This helped identify ore-controlling factors and exploration indicators, assess exploration directions for deep and peripheral areas, and provide a basis for the layout of future exploration projects. [ABSTRACT FROM AUTHOR]

This review article examines the critical role of remote sensing techniques in analysing land cover and its implications for copper deposit exploration. The study aims to provide a comprehensive review of current research and technical advancements in using remote sensing to characterise land cover in copper-rich areas. It draws attention to the complex relationships that exist between subsurface copper mineralisation, surface vegetation, and soil types by combining case studies and modern literature. Integrating satellite imagery, geospatial data, and advanced analytical methods, this review demonstrates how remote sensing can effectively identify and map areas with high potential for copper deposits. Furthermore, it discusses the challenges and opportunities associated with remote sensing applications in geological studies and offers insights into future research directions to enhance mineral exploration and environmental management practices. [ABSTRACT FROM AUTHOR]

The article explores the history of the Te Aroha community and mining district in New Zealand, focusing on the gold rush in the 1860s and the impact on the community. It discusses the presence of both Maori and European settlers and the tensions between them. The text also highlights the decline of the Ngati Rahiri tribe and the loss of their land due to government policies. It provides insights into the cultural and social dynamics of the community during this time period. Additionally, there are documents containing references and citations from various sources related to the Te Aroha community and mining district, offering valuable insights for researchers. [Extracted from the article]

Magnetite is common in various magmatic and hydrothermal ore deposit types, and its trace element geochemistry has become increasingly used in ore genesis studies and mineral exploration. While fractional crystallization has been shown to influence the chemistry of igneous magnetite, the extent to which this process regulates the trace element composition of hydrothermal magnetite remains poorly understood. In this study, we analyzed trace elements in hydrothermal magnetite from the Baijian Fe-(Co) skarn deposit in eastern China and used Rayleigh and equilibrium fractionation modeling to demonstrate the importance of magnetite precipitation in controlling fluid and magnetite chemistry during Fe skarn mineralization. The Baijian Fe-(Co) skarn deposit has three stages of magnetite. From early Mag-1 to later Mag-2 and Mag-3, the concentrations of compatible elements (Ni and V) decrease, whereas those of incompatible elements (Zn, Mn, and Co) increase. There are obvious trends of increasing incompatible/compatible element ratios (e.g., Co/Ni, Zn/V, and Zn/Ni) and decreasing compatible/incompatible element ratios (e.g., V/Mn, Ni/Mn, and V/Co) from Mag-1 to Mag-3, with strong correlations between each of these ratios. Such systematic trace element variations in successive stages of magnetite can be best explained by increasing degrees of fractional crystallization with time. The wide range of incompatible/compatible element ratios (spanning 2-4 orders of magnitude) in Mag-2 and Mag-3 suggests that magnetite crystallization follows a process akin to Rayleigh fractionation. Results from this study highlight the significant role that magnetite crystallization during skarn formation has on the trace element chemistry of this mineral. Moreover, as the crystallization of magnetite progresses, the Co/Fe ratio of residual hydrothermal fluids is elevated, which favors the precipitation of Co in late-stage sulfides. This process helps to explain why some Fe skarn deposits, as well as magnetite-rich iron oxide-apatite and iron oxide-copper-gold deposits, are potentially important economic sources for Co, currently necessary as one component in Li-ion batteries. [ABSTRACT FROM AUTHOR]

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In deep mineral exploration, it is difficult to constrain the complex geological structures using a single geophysical method. To tackle the difficulty, integrated geophysical surveys and joint data interpretation are essential. Machine learning (ML) provides more accurate predictions than traditional methods, especially when dealing with complex data from multiple sources or varied statistical distributions. However, a major challenge in using ML for deep mineral exploration is the scarcity and imbalance of labeled samples, mainly due to budget constraints and the complexity of ore deposits. This issue reduces the accuracy of predictive models and introduces bias. Additionally, limited labeling can lead to difficulties in predicting previously undefined classes in training datasets. To address these challenges, we introduce a robust semisupervised ML framework that integrates diverse geophysical and geological datasets to improve model reliability with limited labeled data. Our approach uses a semisupervised ML variational Gaussian mixture model (SsL-VGMM) to handle issues related to insufficient and imbalanced data. We enhanced the model's predictive capability for unseen data by introducing a novel penalty factor in the 'cannot-link' function. Moreover, we employed Bayesian optimization, focusing on the mean-mixture weight, to avoid local optima during model training. Our model demonstrated high accuracy and efficiency, with classification and prediction accuracies of 95.33% and 87.4%, respectively, in numerical and electromagnetic simulation scenarios. Its effectiveness was further validated by locating Pb–Zn–Ag deposits in Inner Mongolia, supported by actual drilling data. This paper highlights the model's potential in complex mineral exploration and its significant practical and innovative value for deep mineral exploration. [ABSTRACT FROM AUTHOR]

Carbonatites are the primary geological sources for rare earth elements (REEs) and niobium (Nb). This study applies machine learning techniques to generate national-scale prospectivity models and support mineral exploration targeting of Canadian carbonatite-hosted REE +/− Nb deposits. Extreme target feature label imbalance, diverse geological settings hosting these deposits throughout Canada, selecting negative labels, and issues regarding the interpretability of some machine learning models are major challenges impeding data-driven prospectivity modeling of carbonatite-hosted REE +/− Nb deposits. A multi-stage framework, exploiting global hierarchical tessellation model systems, data-space similarity measures, ensemble modeling, and Shapley additive explanations was coupled with convolutional neural networks (CNN) and random forest to meet the objectives of this work. A risk–return analysis was further implemented to assist with model interpretation and visualization. Multiple models were compared in terms of their predictive ability and their capability of reducing the search space for mineral exploration. The best-performing model, derived using a CNN that incorporates public geoscience datasets, exhibits an area under the curve for receiver operating characteristics plot of 0.96 for the testing labels, reducing the search area by 80%, while predicting all known carbonatite-hosted REE +/− Nb occurrences. The framework used in our study allows for an explicit definition of input vectors and provides a clear interpretation of outcomes generated by prospectivity models. [ABSTRACT FROM AUTHOR]

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Mineral exploration is essential for economic growth, but it must be conducted with careful consideration of health and environmental impacts. This study focuses on mapping potential mineralization areas and evaluating environmental consequences in the Jabal Hamadat area. By integrating ASTER, Landsat-9 and airborne radiometric data, the study includes: (1) identification of alteration mineral indicators including iron oxides (gossanized zone), chlorite (propylitic zone), kaolinite, sericite, montmorillonite/illite (argillic zone), and alunite (advanced argillic zone) via the Band Ratio (BR) technique; (2) preparation of a lineament density map through an automated lineament extraction technique; and (3) identification of areas with elevated F-parameter values exceeding 10. Ten areas with high mineralization potential are delineated for further exploration. Additionally, the study assesses environmental radiation exposure, finding that certain areas exceed the safe radiation limit of 1.0 mSv/year. Peak radiation levels range from 0.75 to 1.25 mSv/year, with mining sites showing the highest readings at 1.25 mSv/year and 0.64 mSv/year. These findings highlight elevated radiological hazards, emphasizing the need for comprehensive monitoring and effective mitigation strategies to protect human health and minimize environmental impact. The methodology's success in this area indicates its potential applicability to other mining areas, contributing to enhanced safety and environmental protection. [ABSTRACT FROM AUTHOR]

The booming development of mobile devices has remarkably improved the lives of people, but it also leads to severe electromagnetic radiation problems. Therefore, it has become significantly important to develop electromagnetic interference (EMI) shielding materials for electromagnetic protection. In recent years, MXenes have attracted much attention in the field of EMI shielding, owing to outstanding electrical conductivity, high specific surface area and hydrophilicity. Firstly, this review introduces EMI shielding mechanism and categorization of EMI shielding materials. Subsequently, the research progress of MXenes/polymer‐based EMI shielding materials is reviewed in detail, especially for preparation methods of MXenes/polymer‐based EMI shielding blocks. Finally, the key scientific problems in MXenes/polymer‐based EMI shielding materials are proposed, and their development trend are prospected. Highlights: The EMI shielding mechanism and categorization of EMI shielding materials is presented.The latest research progress of MXenes/polymer‐based EMI shielding materials is reviewed.Scientific problems for MXenes/polymer‐based EMI shielding materials are proposed.Development trend of MXenes/polymer‐based EMI shielding materials is prospected. [ABSTRACT FROM AUTHOR]

Amidst the rapid advancement of artificial intelligence and information technology, the emergence of big data and machine learning provides a new research paradigm for mineral exploration. Focusing on the Tethyan metallogenic domain, this paper conducted a series of research works based on machine learning methods to explore the critical geochemical element signals that affect the metallogenic potential of porphyry deposits and reveal the metallogenic regularity. Binary classifiers based on random forest, XGBoost, and deep neural network are established to distinguish zircon fertility, and these machine learning methods achieve higher accuracy, exceeding 90%, compared with the traditional geochemical methods. Based on the random forest and SHapley Additive exPlanations (SHAP) algorithms, key chemical element characteristics conducive to magmatic mineralization are revealed. In addition, a deposit classification model was constructed, and the t-SNE method was used to visualize the differences in zircon trace element characteristics between porphyry deposits of different mineralization types. The study highlights the promise of machine learning algorithms in metallogenic potential assessment and mineral exploration by comparing them with traditional chemical methods, providing insights into future mineral classification models utilizing sub-mineral geochemical data. [ABSTRACT FROM AUTHOR]

Low-maturity shale oil predominates in shale oil resources. China's onshore shale oil, particularly the Cretaceous Nenjiang Formation in the Songliao Basin, holds significant potential for low-maturity shale oil, presenting promising exploration and development prospects. This study delves into the hydrocarbon generation conditions, reservoir characteristics, and oil-bearing property analysis of the mud shale from the Nen-1 and Nen-2 sub-formations of the Nenjiang Formation to pinpoint favorable intervals for shale oil exploration. Through the integration of lithology, pressure, and fracture distribution data in the study area, favorable zones were delineated. The Nen-1 sub-formation is widely distributed in the Changling Depression, with mud shale thickness ranging from 30 to 100 m and a total organic content exceeding 2.0%. Type I kerogen predominated as the source rock, while some samples contained type II kerogen. Organic microcomponents primarily comprised algal bodies, with vitrinite reflectance (Ro) ranging from 0.5% to 0.8%. Compared to Nen-1 shale, Nen-2 shale exhibited less total organic content, kerogen type, and thermal evolution degree, albeit both are conducive to low-maturity shale oil generation. The Nen-1 and Nen-2 sub-formations predominantly consist of clay, quartz, feldspar, calcite, and pyrite minerals, with minor dolomite, siderite, and anhydrite. Hydrocarbons primarily reside in microfractures and micropores, including interlayer micropores, organic matter micropores, intra-cuticle micropores, and intercrystalline microporosity, with interlayer and intra-cuticle micropores being dominant. The free oil content (S1) in Nen-1 shale ranged from 0.01 mg/g to 5.04 mg/g (average: 1.13 mg/g), while in Nen-2 shale, it ranged from 0.01 mg/g to 3.28 mg/g (average: 0.75 mg/g). The Nen-1 and Nen-2 sub-formations are identified as potential intervals for shale oil exploration. Considering total organic content, oil saturation, vitrinite reflectance, and shale formation thickness in the study area, the favorable zone for low-maturity shale oil generation is primarily situated in the Heidimiao Sub-Depression and its vicinity. The Nen-2 shale-oil-enriched zone is concentrated in the northwest part of the Heidimiao Sub-Depression, while the Nen-1 shale-oil-enriched zone lies in the northeast part. [ABSTRACT FROM AUTHOR]

The East Kunlun Orogenic Belt is an essential part of the Qin-Qikun composite orogenic system, the most crucial orogenic belt in Qinghai Province, and an important gold ore-producing area in China. The Gouli gold field in its eastern section is one of the most important gold fields discovered in the belt in recent years. The Mailong mining area is an important gold mining area in the Gouli ore-concentrated area. The area has experienced frequent and intense magmatic activity, with intrusive rock bodies extensively exposed and intersected by a complex network of fault structures, providing excellent geological conditions for the formation of gold deposits. However, it is difficult to explore due to high altitude, poor transportation, and shallow coverage. This study used an audio magnetotelluric sounding method to track the deep direction and inclination of known mineral belts in the Mailong mining area, and identified mineral exploration targets, providing a basis for mineral exploration. Subsequently, a gold ore body was discovered through drilling verification, achieving a breakthrough in deep mineral exploration. The electromagnetic exploration method works well for exploring structurally altered rock-type gold deposits in plateau desert areas, and combined with the results of this electromagnetic exploration, a metallogenic geological model and genesis process of the Mailong mining area has been constructed. [ABSTRACT FROM AUTHOR]

Maseve Mine is located in the western limb of the Bushveld Complex, recognized as the largest layered igneous intrusion in the world. The study shows results from surface (SP1, SP2, and SP3) and tunnel (T3a, T3b, and TP4b) reflection seismic profiles, totaling 4150 m. Tunnel seismic data were acquired using a seismic landstreamer and spiked geophones with 5 m receiver and shot spacing, as well as a sledgehammer for shots due to space constraints and safety. The profiles, 10–50 m above mineral deposits, crossed major geological structures. Surface seismic profiles used cabled systems and wireless sensors with 5 m and 10 m receiver spacing, respectively, and a 500 kg drop hammer as a source with 10 m shot spacing. Despite high noise levels from mine infrastructure and power cables, a careful processing workflow enhanced target reflections. Interpretation was constrained using borehole data, geological models, and 2D/3D seismic modeling. The processed data exhibit gently dipping reflections associated with faults and dykes, imaging the target mineralization (Merensky Reef and Upper Group 2) and a possible extension. Tunnel seismic experiments demonstrated the application of seismic methods using in-mine infrastructure, while surface experiments proved efficient, illustrating small-scale seismic surveys' capability to image the subsurface, adding value in active mining environments for exploration with cost-effective seismic equipment. [ABSTRACT FROM AUTHOR]

Geochemical measurements of stream sediments are practical for small-scale mineral exploration. However, traditional grid interpolation methods cause element concentrations to diffuse and smooth out anomalies, particularly in complex terrains, making it challenging to reflect the actual distribution of elements accurately. We applied the Dynamic Enhanced Weighted Drainage Catchment Basin (DE-WDCB) method to enhance the retention and identification of local anomalies by limiting the scope of analysis to specific drainage units. This method reduces interference from varying background values across different watersheds, effectively enhancing geochemical element anomalies and aligning better with geomorphic conditions. The DE-WDCB method was tested in the Duobaoshan–Heihe area, a significant copper polymetallic mineral district in northeastern China. Compared with traditional grid interpolation methods, the DE-WDCB method retained and strengthened low and weak abnormal information of favorable mineralization elements, particularly in the Luotuowaizi area. The method demonstrated a higher spatial coverage rate with mineral points and a more vital ore-indicating ability. Specifically, the DE-WDCB method identified anomalies with a mean accuracy of 63.57% (p < 0.05, 95% CI: 47.64%–79.50%), compared to 50.53% for traditional methods. In conclusion, in regions with a complex topography and watershed differences, the DE-WDCB method effectively reduces local geochemical background interference, accurately identifies low and weak geochemical anomalies, and better reflects the actual distribution of elements. This makes it a significantly advantageous method for geochemical anomaly extraction, delineating higher-confidence exploration targets in the Sandaowan–Luotuowaizi area in the east and the triangular area between Duobaoshan, Yubaoshan, Sankuanggou, and the midstream highlands of the Guanbird River in the west. [ABSTRACT FROM AUTHOR]

The Xifanping deposit is a distinct Cenozoic porphyry Cu (Au) deposit located in the Sanjing porphyry metallogenic belt 100–150 km east of the JinshajFiang fault in the western Yangtze craton. We present new zircon U–Pb–Lu–Hf isotopic studies and geochemical data of the ore-bearing quartz monzonite porphyry from the Xifanping deposit to determine their petrogenesis and geodynamic mechanisms. LA–ICP–MS zircon U–Pb dating yielded precise emplacement ages of 31.87 ± 0.41 Ma (MSWD = 0.86) and 32.24 ± 0.61 Ma (MSWD = 1.8) for quartz monzonite porphyry intrusions, and 254.9 ± 5.1 Ma (MSWD = 1.7) for inherited zircons of the monzonite porphyry. The ore-bearing monzonite porphyry is characterized by high-K calc–alkaline to shoshonite and peraluminous series, relatively enriched in light over heavy REEs, with no distinct Eu anomalies, as well as enrichment in LILEs and depletion of HFSEs, with adakitic affinities. The zircon Lu–Hf isotope data ranged from εHf(t) values of −2.94 to +3.68 (average −0.47) with crustal model (TDM2) ages ranging from 0.88 to 1.30 Ga, whereas the inherited zircons displayed positive εHf(t) values ranging from +1.83 to +7.98 (average +5.82), with crustal model (TDM2) ages ranging from 0.77 to 1.17 Ga. Results suggest that the Xifanping porphyry Cu (Au) deposit is related to two periods of magmatic activities. Early magmas were generated from the Paleo-Tethys oceanic subduction during the Late Permian. The subsequent porphyry magma was likely formed by the remelting of previously subduction-modified arc lithosphere, triggered by the continental collision between the Indian and Asian plates in the Cenozoic. The deep magmas and late hydrothermal fluids took advantage of the early magma transport channels along tectonically weak zones during the transition from an extrusive to an extensional–tensional tectonic environment. Early dikes from remelted and assimilated crust contributed to the two age ranges observed in the porphyry intrusions from the Xifanping deposit. The juvenile lower crust materials of the early magmatic arc were potential sources of the Cenozoic porphyry magmas, which has significant implications for mineral exploration and the geological understanding of porphyry Cu deposits in this region. [ABSTRACT FROM AUTHOR]

The study of lithium (Li), gallium (Ga), and rare earth elements (REY) within coal-bearing strata represents a cutting-edge concern in coal geology, ore deposit studies, and metallurgy research. With the rapid advancement of technology and emerging industries, the global demand for Li-Ga-REY has significantly escalated. Several countries worldwide are facing immense pressure due to shortages in Li-Ga-REY resources. Coal-associated Li-Ga-REY depositions have emerged as a pivotal direction for augmenting Li-Ga-REY reserves. To ascertain the enrichment distribution patterns and genetic mechanisms of Li-Ga-REY within the coal-bearing strata of the late Permian Heshan Formation in Wanfu mine, Xian'an Coalfield, Guangxi Province, this study carried out comprehensive testing and analysis on Li-Ga-REY enriched in the mineralized layers within the strata. The Heshan Formation in Wanfu mine presents four layers of Li-Ga-REY-enriched mineralization, labeled from bottom to top as mineralized layers I, II, III, and IV, corresponding to coal seams K5, K4, K3, and K2. These critical metals are predominantly hosted within clay minerals (kaolinite, illite/smectite, and chlorite). The enrichment of critical metals within the Heshan Formation is closely related to terrigenous detrital materials from the Daxin paleocontinent, volcanic detrital materials induced by the Emeishan mantle plume and the Yuenan magmatic arc. The accumulation of Li-Ga-REY and other critical elements within the mineralized layers is the result of inputs from terrestrial and volcanic detrital sources, interactions between peatification and diagenesis stages, and occasionally the input of metal-enriched fluids. In the mineralized layers I, II, and III, the content of lithium oxide (Li2O) surpasses the boundary grade, and the levels of REY, Ga, and (Nb,Ta)2O5 are close to boundary grades, indicating promising exploration prospects. The Wanfu mine in the Xian'an Coalfield can be considered a primary target zone for the exploration and development of coal-associated critical metal resources in Guangxi. [ABSTRACT FROM AUTHOR]

Critical minerals are those minerals that are essential for modern technologies, economic development and national security. Resilent acess to critical minerals is required for low carbon technologies in line with India's COP26(COP26 stands for the 26th Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC). The UNFCCC is a treaty that came into effect in 1994. The COP is the supreme decision-making body of the UNFCCC, and all states that are parties to the convention are represented at the COP.) commitemnts. Critical minerals are distinguished by distinctive chemical and physical properties. Apart from geological processes that determine the physical availability of these minerals there are a host of other factors that influence access to the resources on the ground such as quantity of mineral present, its grade, and the assessment of the optimum methods for mining and processing of ore. Therefore, reliable estimates of the total amount of these minerals that may be available in the Earth's crust are difficult to ascertain at preliminary level of exploration. This implies building capacity at each value chain stage (mineral exploration, mineral extractions, intermediate processing, manufacturing, and recycling). India is dependent on import for many critical minerals including lithium, cobalt, and nickel. Thrust has been given in National Mineral Policy 2019 towards exploration of energy critical minerals, for which the country is mainly dependent on imports. To boost the critical minerals supply chain in India, a number initiative that the government has taken will be discussed in this paper. [ABSTRACT FROM AUTHOR]

Disruptions in the minerals supply chain play a central role in defining the future stock of minerals; therefore, an in-depth analysis of the outcomes and variables affecting exploration is required. In comparative terms, the exploration of critical minerals and major minerals presents geological and technical differences; thus, exploration budgets for critical minerals should be expected to depart from those observed in other minerals. In this context, the main goal of this paper is to contrast how exploration budgets differ between critical and major minerals when considering a set of key variables. We take a multivariate statistical analysis approach based on firm-level budget exploration data to show four key findings: exploration budgets allocated for critical minerals remain consistently lower than major minerals even when controlling for other factors. Moreover, they present a higher sensitivity to fluctuations in commodity prices. Besides, the investment made by larger companies in critical minerals significantly lags behind those made by junior companies. Additionally, the focus of exploration activity for critical minerals predominantly lies in the earlier stages of the exploration process. We expect these initial results to be used as a step forward to facilitate the discussion about exploration policies and, consequently, the reliability of the supply chain. [ABSTRACT FROM AUTHOR]

The rhenium-osmium (187Re-187Os) system is a highly versatile chronometer that is regularly applied to a wide range of geological and extraterrestrial materials. In addition to providing geo- or cosmo-chronological information, the Re-Os system can also be used as a tracer of processes across a range of temporal (millennial to gigayear) and spatial scales (lower mantle to cryosphere). An increasing number of sulfide minerals are now routinely dated, which further expands the ability of this system to refine mineral exploration models as society moves toward a new, green economy with related technological needs. An expanding range of natural materials amenable to Re-Os geochronology brings additional complexities in data interpretation and the resultant translation of measured isotopic ratios to a properly contextualized age. Herein, we provide an overview of the 187Re-187Os system as applied to sedimentary rocks, sulfides, and other crustal materials and highlight further innovations on the horizon. Additionally, we outline next steps and best practices required to improve the precision of the chronometer and establish community-wide data reduction procedures, such as the decay constant, regression technique, and software packages to use. These best practices will expand the utility and viability of published results and essential metadata to ensure that such data conform to evolving standards of being findable, accessible, interoperable, and reusable (FAIR). [ABSTRACT FROM AUTHOR]

Tongling Ore Concentration Area in Anhui Province is an important metallogenic area in the Middle-lower Yangtze Reaches Metallogenic Belt. The Dilongchong Gold Deposit is located in the middle of the ore concentration area, with the tectonic position on the northern margin of the Yangtze Plate. In recent years, typical deposits such as Yeshan Iron Deposit, Caojiachong Copper Deposit, Shujiadian Copper Deposit, and Yangchongli Gold Deposit have been discovered in this area. Through this field exploration work, combined with the existing achievements of the predecessors, the geological characteristics, metallogenic elements, and prospecting indicators of the Dilongchong Gold Deposit are systematically summarized, which provides a basis for further prospecting and exploration work. The results show that the metallogenic types of Dilongchong Gold Deposit are mainly fissure filling types and altered rock types. The genesis of the ore body (vein) is considered to be the late hydrothermal infusion, filling, or metasomatism mineralization. The ore bodies (veins) in the area are strictly controlled by the structure, and the NE-trending main structure and the NW-trending secondary structure are both ore-hosting structures. The intersection of the Fentou Formation, quartz diorite porphyry, and fault structure contact zone and its vicinity are key positions for finding gold deposits. The 4 mineralization alteration combinations of silicification specularite mineralization pyritization (limonite mineralization), specularite mineralization gold mineralization, silicitization pyritization, and beresitization are important prospecting indicators in the mining area and surrounding areas. [ABSTRACT FROM AUTHOR]

Shuiyindong Gold Geposit is one of the super-large gold deposits in the southwestern region of Guizhou. Due to years of mining and expansion of production, the remaining resources are becoming increasingly scarce, necessitating comprehensive research on metallogenic regularities and an analysis of metallogenic potential. Based on a comprehensive analysis of previous data and field investigations, it is suggested that the metallogenic geological body of Shuiyindong Gold Deposit is a deep concealed magmatic body. The metallogenic structure is the Huijiapu anticline and the thrust faults on both sides, while the metallogenic structural surfaces include the shallow Triassic strata fracture surfaces, the interlayer fracture surfaces within the Longtan Formation, and the unconformity contact surfaces between the Longtan and Maokou formations. The mineralization exhibits a distinct vertical zonation. By integrating geophysical and geochemical information, a prospecting prediction model was constructed, identifying 5 potential prospecting areas: the controlled boundary area (including the northern and southern boundaries of known orebodies or mining rights), the area for new mineralization styles or new mineral types (east of Bojitian ore section), the area for new types/spatial prospecting (deep in the uplifted area within the Shuiyindong mining rights), the area for new prospecting directions (the southwestern anticline of the mining area, such as the Aimeidong anticline), and the exploratory prospecting area (the area from west Shuiyindong mining right boundary in Huijiapu anticline to Huilong fault), providing a reference for further prospecting predictions. [ABSTRACT FROM AUTHOR]