Quadrotors have been under development for over a century. The first quadrotors were large, heavy, and difficult to control aircraft operated by a single pilot. The first quadrotors remained in the prototype stage due to accidents, budget cuts, and failure to meet military standards. Production of manned quadrotors ceased in the 1980s. Since the 2010s, manned quadrotors have been used as air taxis, achieving greater success. The development of quadrotor unmanned aerial vehicles (UAVs) began in the 1990s. Their small size, low cost, and ease of control have made them advantageous. Advances in hardware and software technologies have expanded the use of quadrotor UAVs. Today, quadrotor UAVs are used in various fields, including surveillance, aerial photography, search and rescue, firefighting, first aid, cargo transportation, agricultural spraying, mapping, mineral exploration, and counterterrorism. This review examines the development of manned quadrotors and quadrotor UAVs in detail from the past to the present. First, the major manned quadrotors developed are described in detail, along with their technical specifications and photographs. Graphs are provided showing the weight, powerplant, flight duration, and passenger capacity of manned quadrotors. Second, the main quadrotor UAV models entering mass production are discussed, presenting their development processes, technical specifications, areas of use, and photographs. Graphs are presented showing the weight, battery capacity, flight duration, and camera resolution of quadrotor UAVs. Unlike studies focusing solely on the recent past, this review provides a broad overview of the development of quadrotors from their inception to the present.

In early‑stage exploration for minerals, hydrocarbons, and geothermal resources, potential geophysical methods such as Gravity and Geomagnetic surveys stand out for their efficiency, rapid data acquisition, and cost‑effectiveness. Despite their advantages, the interpretation of data derived from these methods is often challenged by the non‑uniqueness of the solution, leading to potential biases in the subsurface models without the support of additional geological or geophysical data. Our study was initiated by developing forward models based on synthetic gravity and magnetic data configured for volcanic terrains anomaly scenarios. This approach facilitates the evaluation of inversion algorithms to mitigate the inherent non‑uniqueness of potential method‑derived models. The main objective of our research is to integrate comprehensive regional geological knowledge, which significantly enhances the accuracy of subsurface interpretations when combined with advanced geophysical techniques. This synergy is further exemplified by applying an Lp‑Norm fast 3D cross‑gradient joint inversion strategy, leveraging gravity and magnetic data to optimize computational efficiency while refining anomaly delineation. Notably, our strategy incorporates a hexahedral terrain model to account for gravitational and magnetic effects of complex terrain, marking a significant advancement in the field. Our findings demonstrate that a nuanced understanding of geological conditions, when integrated with a robust geophysical framework, can lead to the successful reconstruction of volcanic complex subsurface models. This breakthrough has profound implications for geothermal exploration, mineral exploration, and volcanic studies, offering a novel pathway toward more accurate subsurface exploration techniques.

Geological interfaces are crucial elements governing deposit formation, such as silica–calcium surfaces, intrusive contact interfaces, and unconformities can serve as key symbols for mineral exploration prediction. Geological maps provide relatively detailed representations of primary geological interfaces and their interrelationships. However, in previous mineral resource predictions, the type differences in different geological interfaces were ignored, and the types of different geological interfaces vary greatly, thus affecting the validity of the mineral prediction results. Manual interpretation and analysis of geological interfaces involve substantial workloads and make it difficult to effectively apply the rich geological information depicted on geological maps to mineral exploration prediction processes. Therefore, this study proposes a model for intelligent identification of geological interface types based on deep learning. The model extracts the attribute information, such as the age and lithology of the geological bodies on both sides of the geological boundary arc, based on the digital geological map of the Gouli gold mining area in Dulan County, Qinghai Province, China. The learning dataset comprising 5900 sets of geological interface types was constructed through manual annotation of geological interfaces. The arc segment is taken as the basic element; the model adopts natural language processing technology to conduct word vector embedding processing on the text attribute information of geological bodies on both sides of the geological interface. The processed embedding vectors are fed into the convolutional neural network (CNN) for training to generate the geological interface type recognition model. This method can effectively identify the type of geological interface, and the identification accuracy can reach 96.52%. Through quantitative analysis of the spatial relationship between different types of geological interfaces and ore points, it is known that they have a good correlation in spatial distribution. Experimental results show that the proposed method can effectively improve the accuracy and efficiency of geological interface recognition, and the accuracy of mineral prediction can be improved to some extent by adding geological interface type information in the process of mineral prediction.

Geophysical data denoising using dictionary learning method with Ramanujan sums for oil and minerals exploration

Exploration information system: mineral systems anatomy linked to computational techniques for mineral exploration targeting

Lithostructural controls of Pb-Cu-Ag mineralized veins of Jbel Addana district (Western Anti-Atlas, Morocco): Implications for mineral exploration

Modeling mineral and ore bodies from gravity anomalies remains challenging in geophysical exploration due to the ill-posed and non-unique nature of the inverse problem, particularly under conditions of noisy or sparse data. Established inversion methods, including local optimization and metaheuristic algorithms, often require extensive parameter tuning and may yield unstable or poorly constrained solutions. This study proposes a regularized ensemble Kalman inversion (EKI) framework enhanced by Tikhonov regularization to improve numerical stability and mitigate sensitivity to ensemble degeneracy, thereby enabling efficient uncertainty quantification through ensemble statistics. Controlled numerical experiments show that the ensemble size is larger than [Formula: see text] with moderate regularization, we can achieve an optimal balance between convergence stability and model resolution. Benchmarking against established metaheuristic algorithms (PSO, VFSA, and BA) suggests superior computational efficiency and stable convergence. Synthetic and real gravity data inversion (chromite, Pb-Zn, sulphide, and Cu-Au deposits) suggests that the regularized EKI yields stable, geologically consistent results with prior interpretations and drilling data. These results highlight the regularized EKI framework as a robust and efficient tool for mitigating mining risks and supporting strategic decision-making in mineral exploration.

Integrating heterogeneous and multilingual geoscience texts into coherent knowledge graphs is challenged by semantic inconsistencies from terminology variations, diverse expressions, and data heterogeneity, hindering the construction of reliable mineral exploration knowledge systems. We propose a semantic-aware fusion framework that enables consistent and sustainable integration of mineral exploration knowledge. Built on a standardized geological knowledge schema defining core entities and their interrelations, the framework incorporates an incremental update paradigm via a schema-guided fusion mechanism that detects and resolves semantic conflicts while preserving provenance for traceable evolution. Evaluated on textual sources, the framework achieves an overall triple extraction F1-score of 0.82. Notably, for the critical task of entity extraction, it attains an F1-score of 0.88, outperforming BERT-BiLSTM and BERT-BiLSTM-CRF baselines by up to 11 points. Precision for key metallogenic elements exceeds 0.90. It identifies 1432 conflicts during fusion and generates a refined knowledge graph of 18,204 high-quality de-duplicated triples, retaining 87.3% of inputs. The resulting graph supports downstream applications, including case analysis, visualization, question answering, and mineral prospectivity prediction. Unlike conventional aggregation approaches, this work treats knowledge fusion as a semantically guided dynamic process, enhancing consistency, transparency, and adaptability. It provides a practical pathway toward intelligent and sustainable geoscience knowledge infrastructures.

This study presents an advanced joint Euler deconvolution algorithm for the integrated analysis of magnetic and gravity data, which employs an adaptive fixed window size to enhance subsurface depth estimation in the Shavaz region. By simultaneously solving Euler’s equations for both potential fields, the proposed method effectively mitigates the limitations of traditional independent Euler depth estimation analyses, offering improved accuracy in determining the geometry and depth of mineralized bodies and structural discontinuities. Validation through synthetic modeling confirms the accuracy of the algorithm, while its application to field data from the Shavaz region, supported by known geological structures and drilling results, demonstrates its robustness and practical reliability in revealing the spatial distribution of iron mineralization.The adaptive windowing strategy, in conjunction with comprehensive preprocessing, enhances the delineation of anomaly boundaries and the precision of depth solutions. This integrated geophysical methodology provides a robust and efficient tool for mineral exploration within geologically complex terrains, thereby significantly diminishing both interpretational ambiguity and exploration risk.

Abstract. The Limerick Syncline, part of the Irish Zn-Pb Orefield in southwest Ireland, represents a geologically complex and relatively underexplored region, despite hosting the Stonepark and Pallas Green Zn-Pb deposits. The mineral deposits in the Syncline are largely stratabound Zn-Pb systems hosted within Mississippian carbonates. In the area, a thick volcanic sequence overlies and interfingers with the carbonate host rocks, mineralisation and alteration. This has posed significant challenges to seismic imaging in the region, resulting in a poor understanding of the overall structural setting. This study presents an optimised seismic processing workflow tailored to these geological complexities and applied to a 2D seismic reflection profile. The workflow integrates information from newly acquired downhole and laboratory P-wave velocity data with first-arrival travel-time tomography to produce a new velocity model for post-stack migration. This resulted in better signal recovery and enhanced reflector coherence, in particular, reflection continuity. As a result, imaging of key stratigraphic boundaries, internal form lines and the lateral interfingering of volcanic and carbonate units was enhanced. Acoustic impedance analysis using laboratory density data enabled a better understanding of the origins of seismic reflectivity and a more confident geological interpretation of the laterally variable lithologies. A chaotic, low-amplitude seismic facies was recognised representing laterally persistent breccia corridors which may provide a practical indirect seismic proxy for significantly hydrothermally altered zones in the carbonates. Critically, two major previously unrecognised basin-scale faults were identified to the south of the Stonepark and Pallas Green deposits, bounding a significant (half-)graben. Thickness patterns and igneous packages indicate late Tournaisian to early Viséan syn-depositional faulting coeval with emplacement of the Limerick Igneous Suite, with subsequent Variscan inversion providing a net-zero displacement at the surface. These results expand the exploration research space beyond the known mineralisation areas, especially around normal faults on the southern flank of the Syncline.

ABSTRACT An objective understanding of the spatial distribution of Pb concentrations is essential for the rational exploitation of resources and the effective assessment of environmental pollution. The geochemical map is an indispensable tool in mineral exploration and environmental assessment. A 19‐level fixed‐value method for contouring the geochemical map of Pb is proposed, which is based on various typical values of Pb concentrations, including analytical detection limit, abundance values of soils and stream sediments in China, risk screening values and intervention values for agricultural soils in China, and the cut‐off grades of ore deposits. Based on 18 fixed values, Pb concentrations are divided into 19 levels and further categorised into six types. These six types are illustrated in the geochemical map in blue tone, yellow tone, pink tone, red tone, grey tone, and black colour, corresponding to low‐background zones (level 1–5), high‐background zones (level 6–9), maybe screening risk zones (level 10–12), surely screening risk zones (level 13–15), surely intervention risk zones (level 16–18), and mineralised zones (level 19), respectively. The method is applied in the Wenshan area of Yunnan Province, which is rich in Pb, Zn, and Sn metal resources. The results indicate that the Pb concentration in this area ranges from level 2 to level 19, with most belonging to the high‐background zones. Low‐background zones are closely related to the spatial distribution of low‐Pb rocks in the region, while the zones in maybe screening risk, surely screening risk, intervention risk, and mineralised types coincide spatially with proven Pb‐Zn deposits in the area. Geochemical maps generated by traditional cumulative frequency method rely on the existing Pb concentration data from samples, which limits the comparison across different elements and areas. In contrast, the proposed 19‐level fixed‐value method for Pb represents an objective and innovative approach that facilitates the comparison and provides robust support for mineral exploration and environmental assessment.

ABSTRACT The Lingshan area in Shangrao City, Jiangxi Province, China, constitutes a favourable prospecting zone for tungsten and polymetallic deposits. While multiple mineralised bodies have been identified within this region, its linear-ring structural features and the distribution of wall-rock alteration remain poorly understood. Utilising Landsat 8 OLI remote sensing imagery as the primary data source, a multi-method integrated approach was employed for remote sensing geological interpretation and alteration information extraction. Optimal spectral band combinations and image fusion enhanced recognition accuracy. During directional filtering, 30% of the original image was re-incorporated into the results, improving linear structural identification. A total of 34 linear structures and 3 ring structures were interpreted. Iron staining and hydroxyl alteration anomalies were extracted using PCA and principal component threshold density segmentation techniques. Integrating geological and surface anomaly data, three new prospecting target areas were delineated, providing a basis for further mineral exploration in this region.

Geological surveys provide important information for many sectors, from construction project planning to mineral exploration and environmental protection. The seismic cone penetration test with pore water pressure measurement (SCPTu) is a truly valuable tool in geological surveys. It provides detailed information on the subsurface conditions and geological characteristics of the area. The manuscript describes the methodology used to characterize the geological layers at the construction site and quantify the characteristics obtained from SCPT probing. The aim of the scientific study was to identify soft layers in the subsoil and to focus on the impact of pile driving technology on the foundation environment and SCPT probing results. The driving technology involved the implementation of 4.0 m long pyramidal precast concrete piles with pile head dimensions of 0.5 × 0.5 m and tip dimensions of 0.12 × 0.12 m. Probing of the SCPT before and after driving showed that the pile driving led to a significant increase in the velocity of shear waves in the soil at a distance of 0.5 m from the edge of the pile head, which was also reflected in the evaluation of the shear modulus Gmax derived directly from shear wave velocity.

Abstract Mineral deposits are a globally important resource. However, the supply of shallow minerals is close to depletion, forcing exploration activities to expand to deeper areas. Deep exploration has greater challenges compared to shallow exploration, and how to effectively extract the intrinsic connection between exploration data and deep concealment, and how to quickly and accurately locate target zones, remain urgent challenges to be solved. Mineral prediction and geophysical inversion are the core links in mineral exploration, and how to make up for the shortcomings of traditional methods in these links has become an important topic of current research. In the past decade, with the wide application of big data technology in the field of geological prospecting, more and more geological data have provided support for the application of machine learning (ML) in geophysical exploration and mineral prediction. ML overcomes the limitations of traditional methods to a certain extent, such as reducing human subjectivity and improving the ability to mine the laws among geological data, showing great potential. This study summarizes the progress of the application of ML, especially deep learning, in the field of mineral exploration in recent years, focuses on the two key aspects of geophysical inversion and mineral prediction, analyzes the advantages and limitations of the various methods, and makes concluding comments on the future direction of development, with the aim of providing valuable references for the on-site application of ML in mineral exploration and the direction of future research.

This study aims to enhance lithological mapping by employing Support Vector Machine (SVM) classification on integrated visible-near infrared (VNIR)-shortwave infrared (SWIR), and thermal infrared (TIR) datasets from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The study focuses on a part of Kerman Province, the Dehsard area, which was selected due to its diverse lithological units, including igneous, sedimentary, and metamorphic rocks, as represented in the 1: 100,000 geological map of Dehsard. SVM classification was applied on the three datasets using training areas derived from the geological map, along with principal component analysis (PCA) and minimum noise fraction (MNF). The results indicated that the SVM classification on the 14-band ASTER data yielded more accurate results compared to the 9 and 5 band datasets. This study underscores the effectiveness of integrating multiple spectral bands in improving the precision of lithological mapping, which is essential for mineral exploration and geological studies.

The oblique distribution of orebodies is a fundamental characteristic of the spatial arrangement of orebody groups in non-magmatic hydrothermal deposits and is closely related to shearing. The Daliangzi Pb–Zn deposit in the Sichuan–Yunnan–Guizhou Pb–Zn polymetallic metallogenic area is a typical representative of epigenetic hydrothermal deposits controlled by a strike-slip–fault-fold structure. However, the underlying ore-controlling mechanism of this strike-slip–fault-fold structure remains unclear; as a result, achieving breakthroughs in mineral exploration in the deposit’s deep and peripheral zones is directly hindered. This paper focuses on the Daliangzi Pb–Zn deposit. Based on the Theory and Methods of Ore-field Geomechanics, the hierarchical structural ore-controlling pattern of the deposit is clarified, identifying the NE-trending tectonic zone from the Middle-Late Indosinian to Early Yanshanian as the Pb–Zn metallogenic tectonic system. It proposes the spatial oblique distribution patterns of the deposits, ore sections, orebodies, and ore blocks, along with the mechanical mechanisms of multi-scale structural ore control. A compound negative flower structure–fault-fold–diapiric ore-controlling model was constructed for the Daliangzi Pb–Zn deposit. Finally, the locations of concealed orebodies at different scales within the Daliangzi Pb–Zn deposit and its surrounding areas were predicted; moreover, the locations of concealed orebodies at various depths within the deposit area were also predicted.

Abstract. This study presents a geospatial framework for earthquake risk assessment in Türkiye’s Marmara Region, one of the country’s most densely populated and hazard-prone areas. Integrating multi-source datasets within a GIS and Remote Sensing (RS) environment, the approach synthesizes hazard, exposure, and vulnerability layers into a composite risk index at 100 m spatial resolution. Hazard modelling was conducted using fault proximity data from the General Directorate of Mineral Research and Exploration (MTA) and lithological susceptibility maps, both normalized and weighted to reflect seismic amplification potential. Exposure was quantified through demographic and infrastructural density, combining LandScan Global 2023 population data and OpenStreetMap (OSM) building footprints processed via kernel density estimation. Vulnerability was represented using building density as a proxy for structural fragility. All layers were normalized into a 0–1 scale and spatially aligned using GDAL-based resampling. The resulting risk map identifies Istanbul, Kocaeli, Bursa, and Sakarya as high to very high-risk zones, aligning with historical earthquake events such as the 1999 İzmit earthquake. Findings confirm that risk is driven not only by seismic hazard but also by demographic exposure and urban vulnerability. The proposed workflow demonstrates the applicability of open and national geospatial datasets in disaster risk management and offers a reproducible methodology for smart city resilience planning.

The Baiyin district, situated within the northern Qilian orogenic belt, hosts the largest concentration of copper mineral resources in Gansu Province, Northwestern China. Geochemical anomaly patterns are crucial indicators for mineral exploration in this region; however, they are frequently concealed within complex high-dimensional geochemical datasets. Moreover, the scarcity of labeled samples often restricts the effectiveness of supervised machine learning methods for accurate geochemical pattern recognition. This study utilizes unsupervised manifold learning algorithms, including Uniform Manifold Approximation and Projection (UMAP), t-Distributed Stochastic Neighbor Embedding (t-SNE), Isometric Mapping (Isomap), and Locally Linear Embedding (LLE) for identifying low-dimensional features closely associated with mineralization from high-dimensional geochemical datasets. The manifold learning algorithms were optimized by adjusting their key parameters through Receiver Operating Characteristic (ROC) test analysis to achieve optimal performance. The analytical results demonstrate that: (1) manifold learning algorithms exhibited superior performance over conventional factor analysis in accurately capturing complex nonlinear geochemical patterns; (2) The ROC curve and Area Under the Curve (AUC) values for the manifold learning algorithms were UMAP (0.711), t-SNE (0.693), Isomap (0.691), and LLE (0.652), indicating that the UMAP algorithm is the most suitable for identifying geochemical anomaly patterns in the study area; the prediction-area(P-A) analysis further confirmed the UMAP-derived anomalies with a relatively higher prediction efficiency; (3) manifold learning-driven high-probability zones exhibit significant spatial correlations with known mineral deposits, fault structures, and ore-bearing volcanic rock formations. These results highlight the superior capability of manifold learning techniques in extracting meaningful non-linear geochemical anomalies for further exploration of mineral resources.

Deposit scale biogeochemical surveys have been conducted in many parts of Australia, but there have been few conducted at regional scales. The Cobar Basin in New South Wales hosts a range of mineral deposits and regolith–landform settings. Samples (3300) of Callitris glaucophylla (cypress pine) needles have been collected from road traverses within a 43 000 km 2 area with detailed sampling incorporated over various mineral deposits. Dried and milled samples were analysed by both inductively coupled plasma mass spectrometry following microwave-assisted aqua regia digestion, and directly by portable X-ray fluorescence. The concentrations of most elements in the needles largely reflect variations in underlying lithology including areas with alluvial cover. The concentration of ‘ballast' trace elements Au, Ag, Bi, Pb, As and W are typically higher over the Siluro-Devonian clastic sediments than felsic intrusives and volcaniclastic or Ordovician siliciclastic units, whereas the micronutrients Ni and Co are elevated over ultramafic units. For some major and nutrient trace elements, including Cu and Zn, the pines restrict uptake and limit the response to lithological changes, mineralization or contamination. Minor seasonal variations or site variability in element concentrations do not substantially alter the biogeochemical contrast between regional background and mineralization signatures. The needles display effects of dust or contamination from current or historical mining operations in some areas, but variation in the distance of samples from road edges or road type display no systematic relationship with needle composition, except for Fe and Al. C. glaucophylla needles are an effective sampling media for mineral exploration in the Cobar region and for environmental monitoring.

In geophysics, aeromagnetic surveying based on unmanned aerial vehicles (UAV) is a widely employed exploration technique, that can analyze underground structures by conducting data acquisition, processing, and inversion. This method is highly efficient and covers large areas, making it widely applicable in mineral exploration, oil and gas surveys, geological mapping, and engineering and environmental studies. However, during flight, interference from the aircraft’s engine, electronic systems, and metal structures introduces noise into the magnetic data. To ensure accuracy, mathematical models and calibration techniques are employed to eliminate these aircraft-induced magnetic interferences. This enhances measurement precision, ensuring the data faithfully reflect the magnetic characteristics of subsurface geological features. This study focuses on aeromagnetic data processing methods, conducting numerical simulations of magnetic interference for aeromagnetic surveys of UAVs with the Tolles–Lawson (T-L) model. Recognizing the temporal dependencies in aeromagnetic data, we propose a Transformer neural network algorithm for aeromagnetic compensation. The method is applied to both simulated and measured flight data, and its performance is compared with the classical Multilayer Perceptron neural networks (MLP). The results demonstrate that the Transformer neural networks achieve better fitting capability and higher compensation accuracy.