Exploration Of Mineral Resources Research Articles

The principal results of geological exploration for solid mineral resources in 2023 and the planned activities for 2024

Analysis is performed for the results of geological exploration for solid minerals in the Russian Federation in 2023, funded by both the federal sources and miners. The activities for 2024 are planned.

Application of Machine Learning to Research on Trace Elemental Characteristics of Metal Sulfides in Se-Te Bearing Deposits

This study focuses on exploring the indication and importance of selenium (Se) and tellurium (Te) in distinguishing different genetic types of ore deposits. Traditional views suggest that dispersed elements are unable to form independent deposits, but are hosted within deposits of other elements as associated elements. Based on this, the study collected trace elemental data of pyrite, sphalerite, and chalcopyrite in various types of Se-Te bearing deposits. The optimal end-elements for distinguishing different genetic type deposits were recognized by principal component analysis (PCA) and the silhouette coefficient method, and discriminant diagrams were drawn. However, support vector machine (SVM) calculation of the decision boundary shows low accuracy, revealing the limitations in binary discriminant visualization for ore deposit type discrimination. Consequently, two machine learning algorithms, random forest (RF) and SVM, were used to construct ore genetic type classification models on the basis of trace elemental data for the three types of metal sulfides. The results indicate that the RF classification model for pyrite exhibits the best performance, achieving an accuracy of 94.5% and avoiding overfitting errors. In detail, according to the feature importance analysis, Se exhibits higher Shapley Additive Explanations (SHAP) values in volcanogenic massive sulfide (VMS) and epithermal deposits, especially the latter, where Se is the most crucial distinguishing element. By comparison, Te shows a significant contribution to distinguishing Carlin-type deposits. Conversely, in porphyry- and skarn-type deposits, the contributions of Se and Te were relatively lower. In conclusion, the application of machine learning methods provides a novel approach for ore genetic type classification and discrimination research, enabling more accurate identification of ore genetic types and contributing to the exploration and development of mineral resources.

A multi-task learning network based on transformer network for airborne electromagnetic detection imaging and denoising

Abstract As an emerging geophysical exploration technology in recent years, airborne electromagnetic exploration has the advantages of adapting to diverse terrains, wide coverage, and providing a large amount of electromagnetic data, and can be applied to the rapid collection of large amounts of data. Scenarios are often used in fields such as deep geological structures, mineral resource exploration, and environmental engineering research. However, traditional airborne electromagnetic data inversion technology usually takes a long time to process a large amount of airborne electromagnetic data, and it is difficult to remove the noise in the later signals. Therefore, this paper proposes a multi-task learning network structure based on Transformer. By constraining the two network branches of imaging and denoising, a sub-network with simultaneous denoising and imaging is established to process aeronautical electromagnetic data. The noise test set is introduced for testing. This model achieved a 582.61% signal-to-noise ratio improvement in smooth Gaussian noise denoising, and a 129.69% and 112.74% signal-to-noise ratio improvement in non-smooth Gaussian noise and random impulse noise denoising, respectively. The method proposed in this article overcomes the shortcomings of traditional inversion imaging such as slow speed and low resolution, and at the same time eliminates the influence of noise in airborne electromagnetic data. This is of great significance for the application of deep learning in the field of geophysical exploration.

High-order finite-element forward-modeling algorithm for the 3D borehole-to-surface electromagnetic method using octree meshes

The borehole-to-surface electromagnetic (BSEM) method complements surface and airborne electromagnetic (EM) methods. The borehole is typically filled with conductive media, which significantly impacts the responses. To accurately simulate the responses, the borehole needs to be modeled. However, the size of the borehole is very small compared with the entire computational domain, making it challenging to discretize the borehole accurately. We develop a high-accuracy 3D finite-element forward-modeling algorithm for the BSEM method using octree meshes. The basic idea of octree meshes is to locally refine the mesh by dividing the hexahedral element into eight smaller elements. This approach has the advantage of being able to accurately discretize small geometric features while using coarse meshes in other areas, thus reducing the computational cost. In addition, high-order basis functions are used to improve the accuracy of numerical solutions. We develop a 3D forward-modeling code for BSEM using the C++ programming language. We verify the correctness and accuracy of our implementation by comparing the numerical responses with analytical solutions. Furthermore, we determine the influence of the borehole on the EM responses, showing that the borehole will cause a decrease of several orders of magnitude in the responses when the borehole is filled with highly conductive media. We also use two 3D models to demonstrate the efficacy of octree meshes in the discretization of complex structures and to discuss the effectiveness and sensitivity of BSEM data in the exploration of mineral and oil-gas resources. The code is open source under the Massachusetts Institute of Technology license and can be used as an accurate modeling tool for BSEM responses, providing useful guidance for field exploration. The code is designed with a modular architecture, ensuring ease of maintenance and the ability to extend to other EM methods and serve as the foundation for 3D inversion.

The development of China's extraterritorial mineral resources exploration and exploitation in the deep seabed and outer space: An evaluation from policy and legal perspectives

Extraterritorial mineral resources, located in the deep seabed and outer space, offer abundant prospects for development, wealth, and a sustainable future. Recent advancements in science and technology have brought us closer to the realization of mining these resources. When analyzing China's engagement in these activities, it becomes evident that China is undoubtedly one of the most active and capable states in exploring extraterritorial resources from the deep seabed and outer space. Different from the objectives of conventional mineral mining, China prioritizes broader strategic goals, aiming to establish itself as a maritime and space power rather than solely pursuing short-term economic gains. Given these aspirations, it's crucial to develop more coordinated, transparent, and well-defined policies and laws. This necessity arises not only to facilitate China's advancement in these fields but also to proactively tackle potential challenges and to foster a conducive external environment for further development.

High-Frequency and High-Current Transmission Techniques for Multiple Earth Electrical Characteristic Measurement Systems Based on Adaptive Impedance Matching through Phase Comparison.

With the increase in groundwater exploration, underground mineral resource exploration, and non-destructive investigation of cultural relics, high-resolution earth electrical characteristic measurement has emerged as a mainstream technique owing to its advantageous non-destructive detection capability. To enhance the transmission power of the high-frequency transmitter in high-resolution multiple earth electrical characteristic measurement systems (MECS), this study proposes a high-frequency, high-current transmission technique based on adaptive impedance matching and implemented through the integration of resonant capacitors, a controllable reactor, high-frequency transformers, and corresponding control circuits. A high-current precisely controllable reactor with a 94% inductance variation range was designed and combined with resonant capacitors to reduce circuit impedance. Additionally, high-frequency transformers were employed to further increase the transmission voltage. A prototype was developed and tested, demonstrating an increase in transmission current at frequencies between 10 and 120 kHz with a peak active power of 200 W. Under the same transmission voltage, compared to the transmission circuit without impedance matching, the transmission current increased to a maximum of 16.7 times (average of 10.8 times), whereas compared to the transmission circuit using only traditional impedance matching, the transmission current increased by a maximum of 10.0 times (average of 4.2 times), effectively improving the exploration resolution.

Hierarchical Semantic-Guided Contextual Structure-Aware Network for Spectral Satellite Image Dehazing

Haze or cloud always shrouds satellite images, obscuring valuable geographic information for military surveillance, natural calamity surveillance and mineral resource exploration. Satellite image dehazing (SID) provides the possibility for better applications of satellite images. Most of the existing dehazing methods are tailored for natural images and are not very effective for satellite images with non-homogeneous haze since the semantic structure information and inconsistent attenuation are not fully considered. To tackle this problem, this study proposes a hierarchical semantic-guided contextual structure-aware network (SCSNet) for spectral satellite image dehazing. Specifically, a hybrid CNN–Transformer architecture integrated with a hierarchical semantic guidance (HSG) module is presented to learn semantic structure information by synergetically complementing local representation from non-local features. Furthermore, a cross-layer fusion (CLF) module is specially designed to replace the traditional skip connection during the feature decoding stage so as to reinforce the attention to the spatial regions and feature channels with more serious attenuation. The results on the SateHaze1k, RS-Haze, and RSID datasets demonstrated that the proposed SCSNet can achieve effective dehazing and outperforms existing state-of-the-art methods.

Cooperative exploration model of coal–Ge deposit: A case study of the Wulantuga coal–Ge deposit in Shengli coalfield, Inner Mongolia, China

Germanium (Ge) is an excellent organophilic and dispersed semiconductor resource, which is irreplaceable in emerging industries such as new materials, new energy, aerospace, industrial smelting, national defense, military industry, and modern information technology, it has become an important direction of mineral resources exploration. Therefore, it is urgent to carry out research on the collaborative exploration model of coal and Ge deposits from a technical and economic perspective to lay a theoretical foundation for the collaborative exploration of coal and Ge. In this study, the Wulantuga coal–Ge deposit is taken as an example, according to the distribution characteristics of coal–Ge deposit and the response effect to the exploration technical means, the economical and effective exploration technical means, reasonable exploration project layout and resource estimation parameters of coal–Ge deposit are put forward, and the cooperative exploration mode of coal–Ge deposit is determined, it provides an important scientific basis for improving the strategic coordination ability of Ge mineral resources.

Ultra-high sensitivity weak magnetic field detecting magnetic fluid surface plasmon resonance sensor based on a single-hole fiber

An ultra-high sensitivity weak magnetic field detecting magnetic fluid surface plasmon resonance (SPR) sensor based on a single-hole fiber (SHF) is proposed for detecting weak magnetic fields. The sensor is constructed with a single-hole fiber in which an exclusive air hole in the cladding is embedded with a metal wire and filled with a magnetic fluid (MF) to enhance the magnetic field sensitivity. The effects of the structural parameters, embedded metals, and refractive index difference between the core and cladding on the magnetic field sensitivity and peak loss are investigated and optimized. The sensitivity, resolution, figure of merit (FOM), and other characteristics of the sensor are analyzed systematically. The numerical results reveal a maximum magnetic field sensitivity of 451,000 pm/mT and FOM of 15.03 mT-1. The ultra-high magnetic field sensitivity renders the sensor capable of detecting weak magnetic fields at the pT level for the first time, in addition to a detection range from 3.5 mT to 17 mT. The SHF-SPR magnetic field sensor featuring high accuracy, simple structure, and ease of filling has immense potential in applications such as mineral resource exploration as well as geological and environmental assessment.

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

Remote sensing satellite imagery consistently provides valuable and frequent information, enabling the exploration of mineral resources across immense, remote and harsh domains. Recent developments in spaceborne hyperspectral remote sensing have opened avenues to support diverse remote sensing applications, particularly in the realm of mineral exploration. This study evaluates the capabilities of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral satellite data for mapping alteration minerals using the Matched Filtering Unmixing (MFU) approach in the Sar-e-châh-e-shur, Birjand, Iran. Minerals such as richterite, augite, psilomelane, ilmenite, kaolinite, smectite, mirabilite, muscovite, and chlorite were identified using the vertex component analysis (VCA) technique. Subsequently, alteration mineral maps of the study area were generated using a matched filtering technique. Additionally, through the integration of X-ray diffraction (XRD) analysis, thin section examination, geochemical study of stream sediments, and interpretation of geological maps, potential alteration mineralization zones were delineated in the study area. Ultimately, the validation process, which included comparing the maps with the findings derived from the PRISMA remote sensing study, was conducted using the normal score equation. Thus, our results yielded a normalized score of 3.42 out of 4, signifying an 85.71% agreement with the regional geological characteristics of the study area. The results of this investigation highlight the substantial potential of the PRISMA dataset for systematic alteration mineral mapping and consequent exploration of ore minerals, specifically in challenging and inaccessible terrains.

Imaging technology based on the interaction between muon and material

Muon imaging technology, as an emerging detection method, is widely used in various fields. Muons can be classified into cosmic ray muons and accelerator muons based on their origins. Cosmic ray muons stand out for wide energy range, strong penetrating power and no artificial radiation. These characteristics make cosmic ray muon imaging technology adept at achieving nondestructive imaging of target objects. Presently, the commonly used imaging methods include scattering and transmission imaging technologies that leverage muon information for imaging. Additionally, muon and muonic secondary particle coincidence imaging technology utilizes information from secondary particles generated during the interaction between muons and target objects for imaging. The accelerator muon, distinguished by its high flux, strong monochromaticity, and adjustable energy, enables rapid and multidimensional imaging of target objects at various depths. Furthermore, it facilitates the analysis of material elements through muonic X-rays. This article provides insights into the production process and physical characteristics of muons, the fundamental principles of muon imaging technology, and its diverse applications across disciplines. It also explores the current development status and emerging trends in fields such as mineral resource exploration, archaeological studies, and nuclear safety.

Regional Geochemical Characteristics of Lithium in the Mufushan Area, South China

With the explosive growth in demand for lithium (Li) resources, the Mufushan area has been a hotspot for Li deposit exploration in China in recent years. Geochemical maps and geochemical anomaly maps are basic maps in the geochemical exploration of mineral resources. A fixed-value method to contour a Li geochemical map is presented here, in which Li concentrations are divided into 19 levels on 18 fixed values, ranging from 5 μg/g (corresponding to the detection limit) to 1858 μg/g (corresponding to the cut-off grade of Li deposit in hard-rock type) and illustrated in six color tones corresponding to Li areas of low background, high background, low anomaly, high anomaly, mineralization in clay-type, and mineralization in hard-rock type. The geochemical map of Li in the Mufushan area using the new fixed-value method indicates that the study area belongs to the high background area, and the known Li deposits are located in the high anomaly areas. In addition, the geochemical anomaly map of the Mufushan area is drawn using the method of seven levels of classification, and indicates that the known Li deposits are all in the anomaly areas, with anomaly levels not lower than the second level. Furthermore, four other areas are recognized for Li resource potential based on the geochemical map and geochemical anomaly map in the Mufushan area.

Inversion for 3D Conductivity and Chargeability Models Using EM Data Acquired by the New Airborne TargetEM System in Ontario, Canada

This paper introduces an original approach to the joint inversion of airborne electromagnetic (EM) data for three-dimensional (3D) conductivity and chargeability models using hybrid finite difference (FD) and integral equation (IE) methods. The inversion produces a 3D model of physical parameters, which includes conductivity, chargeability, time constant, and relaxation coefficients. We present the underlying principles of this approach and an example of a high-resolution inversion of the data acquired by a new active time domain airborne EM system, TargetEM, in Ontario, Canada. The new TargetEM system collects high-quality multicomponent data with low noise, high power, and a small transmitter–receiver offset. This airborne system and the developed advanced inversion methodology represent a new effective method for mineral resource exploration.

Picking of weak signal in seismic exploration of non-coal solid mines based on phase-locked technology

The exploration and development of mineral resources are of crucial significance to national economy, people's livelihood and national security. Therefore, in order to extract the weak signal in seismic exploration of non-coal solid mines in shallow surface layer to achieve high-resolution exploration, the mathematical model and physical model of multiplier are established by referring to the frequency point acquisition technology in wireless radar communication, that is, phase-locked technology. The weak effective seismic signals of different frequency points are picked out from the collected 120 dB dynamic range spectrum, and the information of stratigraphic structure under high noise background is obtained more effectively by using mixing detection and two-phase demodulation technology in the harmonic component spectrum, thereby improving the high resolution and high precision of mineral exploration. The lock-in amplifier is added to the front stage of the seismograph to obtain the effective seismic wave reflected by the wave impedance interface required in the exploration task. Experimental results show that this method significantly improves the SNR and protects the weak effective signal from loss. It adds new technology and method to seismic signal acquisition and processing, and provides a new way to obtain high-quality seismic data in the field of mineral geophysical exploration, and will be widely used after being promoted in the fields of mineral exploration, geological disaster prediction, military geophysics, and archaeology.

Tin (Sn) Geochemical Mapping Based on a Fixed-Value Method: A Case Illustration in Gejiu Area, Southwest China

Geochemical maps play an important role in mineral resource exploration. There are three traditional methods for creating geochemical maps: the cumulative frequency method, the logarithmic interval method, and the Avg±k∗Std (where Avg and Std are the abbreviations of average and standard deviation, and k is a multiple of Std) method. However, with the increasing scope of the study area and cumulative data, the limitations of traditional methods, which depend on the amount of data, are exposed. A fixed-value method for Sn geological mapping is proposed to overcome the limitations of traditional methods. In the fixed-value method, Sn concentrations are divided into 19 levels on 18 fixed values ranging from 1 μg/g (corresponding to the detection limit) to 1000 μg/g (corresponding to the cut-off grade of Sn in hard rocks). The 19 levels are mapped in six color tones. The first to fifth levels are the lowest background areas in blue tones, which correspond to Sn concentrations ranging from the minimum to 3.4 μg/g. The sixth to ninth levels are high background areas in yellow tones corresponding to concentrations less than 10 μg/g, the 10th to 12th are low anomaly areas in pink tones less than 28 μg/g, the 13th to 15th are high anomaly areas in red tones less than 200 μg/g (corresponding to the placer cut-off grade), the 16th to 18th in gray tones less than 1000 μg/g, and the 19th level is in black corresponding to Sn ores with Sn concentration not less than 1000 μg/g. The fixed-value method along with three traditional methods was used to contour the Sn geochemical maps in the Gejiu area in Southwest China. The illustration results of the presented fixed-value method and three traditional methods for geochemical mapping of Sn are all feasible for Sn deposit exploration in the Gejiu area, Southwest China. Compared to traditional methods, the presented fixed-value method overcomes the flaws of traditional methods and is also more meaningful in geochemistry.

Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions.

Deep Subsurface Pseudo-Lithostratigraphic Modeling Based on Three-Dimensional Convolutional Neural Network (3D CNN) Using Inversed Geophysical Properties and Shallow Subsurface Geological Model

Abstract Lithostratigraphic modeling holds a vital role in mineral resource exploration and geological studies. In this study, we introduce a novel approach for automating pseudo-lithostratigraphic modeling in the deep subsurface, leveraging inversed geophysical properties. We propose a three-dimensional convolutional neural network with adaptive moment estimation (3D Adam-CNN) to achieve this objective. Our model employs 3D geophysical properties as input features for training, concurrently reconstructing a 3D geological model of the shallow subsurface for lithostratigraphic labeling purposes. To enhance the accuracy of pseudo-lithostratigraphic modeling during the model training phase, we redesign the 3D CNN framework, fine-tuning its parameters using the Adam optimizer. The Adam optimizer ensures controlled parameter updates with minimal memory overhead, rendering it particularly well-suited for convolutional learning involving huge 3D datasets with multi-dimensional features. To validate our proposed 3D Adam-CNN model, we compare the performance of our approach with 1D and 2D CNN models in the Qingniandian area of Heilongjiang Province, Northeastern China. By cross-matching the model’s predictions with manually modeled shallow subsurface lithostratigraphic distributions, we substantiate its reliability and accuracy. The 3D Adam-CNN model emerges as a robust and effective solution for lithostratigraphic modeling in the deep subsurface, utilizing geophysical properties.

ICEEMDAN/LOESS: An Improved Vibration-Signal Analysis Method for Marine Atomic Interferometric Gravimetry

The marine atomic interferometric gravimeter is a vital precision instrument for measuring marine geophysical information, which is widely used in mineral resources exploration, military applications, and missile launches. In practical measurements, vibration disturbance is an important factor that affects measurement accuracy. This paper proposes the combination of improved complete ensemble empirical mode decomposition with adaptive noise and locally weighted regression for vibration characterization of gravimeter vibration data. Firstly, the original signal is added into a pair of white noise for adaptive noise-complete ensemble empirical mode decomposition to obtain multiple intrinsic mode functions. The efficient IMF components and noise components are filtered out under the dual indicators of correlation coefficient and variance contribution ratio, and then the LOESS filtering method is used for noise reduction to obtain useful signal detail information; finally, the noise-containing components are reconstructed with the effective components after the noise-reduction process. The experimental results of both simulated and measured vibration signals show that the proposed method can effectively decompose the different high- and low-frequency bands contained in the vibration signal and remove the noise of the original signal.

Cooperative Exploration Model of Coal–Lithium Deposit: A Case Study of the Haerwusu Coal–Lithium Deposit in the Jungar Coalfield, Inner Mongolia, Northern China

Lithium (Li) is an important strategic metal mineral resource, irreplaceable in the fields of modern industry, new energy technology, nuclear fusion, and energy storage devices. Li is an important supplement to traditional strategic metal mineral resources and has become an important avenue of mineral resource exploration. Therefore, there is an urgent need to establish a cooperative exploration model of coal and Li deposits to lay a theoretical foundation from the perspective of technical optimization and economic rationality. This study is based on the distribution characteristics of the Haerwusu coal–Li deposit, and the effectiveness of the response to exploration techniques, the economical and effective exploration techniques, the reasonable exploration engineering design, and resource estimation parameters is investigated. Therefore, the cooperative exploration model of the coal–Li deposit is established. The high-Li areas in the surface of the Haerwusu Li deposit is distributed near the B1 anticline or in the middle area between the X1 syncline and the B1 anticline, and the vertical distribution of Li content is irregular. The exploration techniques, exploration engineering design, and resource estimation are reviewed and optimized. According to the geological, geochemical, and geophysical conditions, a reasonable cooperative exploration model for coal–Li deposits is established from the two aspects of the coordination of multi-mineral exploration and the coordination of various exploration technologies. The determination of the coal–Li deposit cooperative exploration model has important practical significance for improving the resource security system.

Cooperative Exploration Model of Coal–Gallium Deposit: A Case Study of the Heidaigou Coal–Gallium Deposit in the Jungar Coalfield, Inner Mongolia, China

Gallium (Ga) is a typical scattered trace element that is irreplaceable in strategic sectors such as national defense, wireless communications, new materials, renewable energy, and healthcare. The coal–Ga deposit is an important complement to traditional Ga resources and has become a significant focus for Ga mineral resource exploration. Therefore, there is an urgent need to research the coal–Ga cooperative exploration model from both technical and economic perspectives. Taking the Heidaigou coal–Ga deposit as an example, the enrichment zone of coal–Ga is predominantly situated in the northern part of the exploration area, adjacent to the fault zone. The Ga concentration demonstrates a gradual decline from the north–central region towards the northeast and southeast. Similar vertical Ga distribution patterns are observed in adjacent drillings, with notably higher concentrations in the roof, floor, and parting layers. The cooperative exploration model for coal–Ga deposits is proposed based on the above features. The model employs a comprehensive set of cooperative technical methods, such as remote sensing, geological mapping, seismic exploration, drilling, petrogeochemistry, and well logging. The layout of exploration engineering and the concentration of Ga provide the basis for the estimation of Ga resources. Additionally, the model provides an important scientific basis for the improvement of the strategic coordination ability of Ga mineral resources.