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Underground mining has historically occurred in surface and near-surface (shallow) mineral deposits. While no universal definition of deep underground mining exists, humanity's need for non-renewable natural resources has inevitably pushed the boundaries of possibility in terms of environmental and technological constraints. Recently, deep undergro...
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... Subsurface sampling primarily makes use of exploratory drilling, which variably: (a) assesses mineralisation potential; (b) collects samples for geological, geochemical, geomechanical and petrophysical analyses and correlation; (c) allows for estimation of the boundaries and sizes of mineral resources and reserves; and (d) maps various structures (Fig. 1). Two common drilling methods include core drilling that yields a cylindrical sample of the ground to a given depth, and percussion drilling that yields crushed samples that consist of cuttings from a given depth. These Table 3 The main geophysical methods, description and application characteristics for deep underground mining ( Moon ...
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Review Article "The Sustainable Development Goals (SDGs)", are a universal call for action, covering targets to be fulfilled by the end of 2030. Many global problems such as global climate change and drought, environmental pollution, depletion of natural resources and biodiversity, inclusive education have evolved into the greatest collective probl...
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... In the coming decades, coal resources will continue to be a major global energy source, and the safety and efficiency of coal mining will be the core focus of mining management 1,2 . In recent years, with the development of artificial intelligence technology, the application in coal mining has shown great potential by assisting or replacing humans in completing high-risk tasks, thereby enhancing safety monitoring and operational efficiency 3 . ...
The underground drilling environment in coal mines is critical and prone to accidents, with common accident types including rib spalling, roof falling, and others. High-quality datasets are essential for developing and validating artificial intelligence (AI) algorithms in coal mine safety monitoring and automation field. Currently, there is no comprehensive benchmark dataset for coal mine industrial scenarios, limiting the research progress of AI algorithms in this industry. For the first time, this study constructed a benchmark dataset (DsDPM 66) specifically for underground coal mine drilling operations, containing 105,096 images obtained from surveillance videos of multiple drilling operation scenes. The dataset has been manually annotated to support computer vision tasks such as object detection and pose estimation. In addition, this study conducted extensive benchmarking experiments on this dataset, applying various advanced AI algorithms including but not limited to YOLOv8 and DETR. The results indicate the proposed dataset highlights areas for improvement in algorithmic models and fills the data gap in the coal mining, providing valuable resources for developing coal mine safety monitoring.
... With the exploitation of coal resources, shallow coal seams are being progressively depleted, driving coal mining operations to extend from superficial strata to deeper geological horizons [1][2][3]. The intensification of mining activities has led to increased occurrences of strata pressure manifestations and related geomechanical hazards [4][5][6]. ...
... In the equation, EI is the stiffness of the main roof beam (where E denotes the elastic modulus of the main roof, I represents the moment of inertia of the main roof, I = bh 3 12 , b is the rock beam width, and h is the thickness of the main roof), MN/m; where k ′ -foundation stiffness (defined as the load per unit deformation applied to a unit beam), MN/m; q-uniform force on the base top, MN/m 2 ; m-mass per unit length of the main roof; ...
This study investigates the exacerbated strata pressure manifestations induced by high-intensity mining in medium-deep weakly cemented coal seams in western China. An integrated research methodology combining theoretical analysis, field measurements, and numerical simulation was employed to develop a mechanical model of overburden fracture structures in weakly cemented mining faces, systematically revealing the dynamic effects of face advance rate on strata pressure behavior. The results demonstrate that the advance rate not only significantly governs the evolutionary patterns of roof caving and weighting intervals but also exhibits nonlinear correlations with the distribution characteristics of abutment pressure. Furthermore, microseismic parameters effectively characterize the response of strata pressure intensity to advance rate variations. The proposed dynamic control methodology provides both theoretical foundations for safe mining in weakly cemented strata and innovative technical solutions for ground control in deep high-intensity mining operations.
... With the rise of geodata science, alternatives to classical statistical, and therefore, geostatistical methods have been created for interpolation. Such a direction seems inevitable given (1) the peopleÕs insatiable appetite for raw materials (e.g., Michaux, 2024), which is promoting the rise of (2) extraction and exploration in inhuman environments, necessitating a more indirect, and therefore, data-driven feedback-style of operation (e.g., deep mining, Ghorbani et al., 2023a); and (3) the general rise of bigger (e.g., higher velocity) geodata (e.g., Bourdeau et al., 2023Bourdeau et al., , 2024. One general method based on machine learning (ML) was proposed and evaluated in Nwaila et al. (2024b), colloquially called Ômi-croblockingÕ. ...
Spatial models are fundamental across the mineral value chain, forming the basis for exploration and extraction. Geodata science and increasingly bigger data permit alternatives to traditional mineral resource estimation methods, particularly in spatial data interpolation. Interpolation has been formulated as a machine learning (ML) task, providing new capabilities, such as automated deployment and remote real-time monitoring. However, a significant gap exists regarding how uncertainty propagates through ML workflows. This paper introduces an uncertainty propagation method to a ML-based interpolation method called microblocking that propagates epistemic uncertainty. Our method adheres to the data science framework and is fully ML-based. Epistemic uncertainty is the dominant uncertainty in geosciences, because data sparsity is created by both complex dynamics of physical systems and sampling limitations. Our uncertainty estimates are block-specific and can guide sampling and other activities. Biasing sampling toward blocks with high economic potential and high uncertainty enables the most cost-effective sequencing of sampling. A rapid, ML-based uncertainty quantification method provides a modern data-driven (feedback-based) framework to extraction guidance, built on big data, geodata science, and real-time mineral resource modeling. We compare our method with typical kriging uncertainty estimates and demonstrates that our results are more block-specific and broader in scope (more comprehensive). In an industry where financial stakes are significant, a thorough understanding of uncertainty can improve investor confidence. The method not only improves scientific rigor, but is also engineered to fit increasingly bigger data across the mineral value chain, and caters to the conservative nature of the mineral industry, where method validation occurs at a slower pace.
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... Also, some environmental impacts of placer mining have been reported such as adverse impacts through interaction with the aquatic ecosystem, leading to sedimentation, and disruption of activities [32]. Room and pillar mining, along with longwall mining, are notable methods for underground mining in regions with high-depositional-depth radionuclides [33,34]. In the room and pillar method, the initial step involves dividig the ore body by creating intersecting drives through excavation work, leaving a sufficient number of pillars to support the roof. ...
Radionuclide mining provides essential resources for energy production and industrial applications; however, unregulated activities pose significant environmental and public health risks. This review examines various radionuclide mining methods based on their physical and chemical properties and evaluates existing regulatory frameworks. It highlights key environmental monitoring techniques, including gamma spectrometry, radon detector, and remote sensing, for ensuring occupational and ecological safety. The study also explores remediation and rehabilitation strategies, such as radioactive waste disposal methods, site restoration, and health monitoring programs for workers and local communities. Additionally, it emphasizes the role of artificial intelligence (AI) and other risk management tools in enhancing transparency, waste management, and community engagement. The findings underscore the necessity of integrating sustainable mining practices that balance economic benefits with environmental and human health protection. Ultimately, this review concludes that prioritizing environmental and public health safeguards is critical before the commencement of radionuclide mining activities.
... The precise identification of geological formations is a key issue for understanding tectonic structures, planning mining activities, and sustainably managing natural resources. This field goes beyond scientific boundaries to play a strategic role in economic development, environmental preservation, and population security [1]. Due to the difficult access conditions and the • We have created a complete and representative dataset of different types of geological formations. ...
Accurate identification of geological formations is essential for understanding tectonic structures, planning mining activities, and sustainably managing natural resources. It goes beyond the scientific framework to play a key role in economic development, environmental preservation, and population security. This article proposes a study using machine learning to analyze different parameters from various sources of satellite imagery: multispectral optics (Landsat-8), radar (ALOS PALSAR), and soil and morphometric parameters (soil, altitude, slope, curvature, and shady). The data were preprocessed to remove atmospheric biases and harmonize spatial resolutions. Techniques such as principal component analysis, band ratios, and image fusion have made it possible to enrich imagery by highlighting spectral and textural characteristics. Finally, classifiers such as Random Forest, Gradient Boosting, and XGBoost (version 1.6.2) were used to evaluate the impact of each parameter on the classification. The results show that geographic parameters combined with PCA provide the best overall performance with Random Forest, achieving an accuracy of 55.29% and an MCC of 45.12% while ensuring a rapid training speed (3.6 s). The geographic parameters associated with the OLI spectrometric data show a good balance, with XGBoost achieving a slightly higher MCC (40.3%) with a moderate training time (7.9 s). On the other hand, the OLI spectrometric parameters coupled with PCA display significantly lower performance, with an accuracy of 45.05% and an MCC of 31.81% for Random Forest. These observations highlight the potential of geographic and geological parameters associated with suitable models to improve classification. The multi-source approach thus proves optimal for more robust and precise results.
... Deep rock masses are typically exposed to extreme storage conditions characterized by high ground stress, elevated gas content, high temperatures, and high water pressure. These conditions have led to increasingly severe dynamic disasters, such as rock bursts, coal and gas outbursts, and water inrush, posing serious threats to mining safety [1][2][3]. During coal mining, excavation disturbances subject coal-rock masses to repeated loading and unloading cycles [4,5]. ...
In deep underground engineering applications, such as coal mining, coal–rock masses are frequently subjected to repeated loading and unloading conditions. Understanding the evolution mechanisms of their internal three-dimensional fracture fields has become a critical scientific challenge. This study utilized X-ray Microscopy (XRM) to observe changes in internal fractures of coal samples after each loading–unloading cycle, reconstructing the internal fractures and mineral particles. Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) were employed to analyze the surface morphology and mineral composition of coal sample cross-sections. The experimental results revealed that: (1) With an increasing number of loading–unloading cycles, the samples’ volumes initially decreased and then expanded, with the expansion accompanied by rapid propagation of CT-scale fractures; (2) During the linear elastic phase, micro-fractures developed progressively but remained small, while sustained stress caused these fractures to interconnect, eventually leading to macroscopic failure; (3) Hard mineral particles within the coal samples, such as iron ore, acted as barriers to crack propagation. These findings indicate that the evolution characteristics of the internal fracture fields in coal–rock masses are influenced by stress state, pre-existing fractures, and the distribution of mineral particles.
... As shallow resources are depleted, the development and utilization of mineral resources in the deep layers of the Earth have become the norm. 1,2 Deep underground projects, including deep foundation pits, tunnels, and underground mines, are confronted with complex geological environments and materials exhibiting diverse lithological structures and mechanical properties. 3 These projects are often associated with dynamic disasters, such as rock bursts, tunnel collapses, and seismic events. ...
Acoustic emission (AE) technology is a crucial approach for ensuring the safe operation and maintenance of underground spaces. Traditional mechanical and piezoelectric AE sensors often fail to meet the monitoring requirements of complex underground environments. In this paper, a novel AE monitoring system based on a Michelson interferometer is designed and applied to monitor the tensile fracture processes of common materials (coal, concrete, quartzite, and granite) in various underground engineering projects. Signal noise reduction and feature parameter extraction of the waveform data collected by the optical fiber AE sensor are performed. The time series, frequency domain characteristics, and crack fracture modes of the optical fiber AE signals were analyzed, and the precursor characteristics of tensile fractures were extracted. The results indicate that the optical fiber AE system possesses high sensitivity, a wide frequency band response, and a large dynamic range, enabling it to effectively identify AE waveform characteristics and primary frequency differences generated in various rock fracture experiments. The AE count and energy evolution patterns are in good agreement with the material stress curves, effectively quantifying the material damage evolution processes. The proportional distribution of tensile and shear cracks in different materials was successfully classified using fiber AE waveforms. Based on the changing trends of the b-value, variance, and autocorrelation coefficient, a failure early warning mechanism was constructed, categorizing failure early warnings into three time levels: trend early warning, initial early warning, and critical early warning. This study provides a new and effective monitoring method for the safety development of deep engineering.
... The global development of deep resource exploitation and underground space utilisation has resulted in increasingly complex and severe challenges in deep underground engineering [1][2][3]. For example, deep geothermal reservoir, tunnelling, mining, and largescale underground cavern construction processes invariably require the study of rock mechanical behaviour in high-stress environments [4,5]. ...
In the context of advancements in deep resource development and underground space utilisation, deep underground engineering faces the challenge of investigating the mechanical behaviour of rocks under high-stress conditions. The present study is based on a gold mine, and the bulk ore taken from the mine perimeter rock was processed into two sets of specimens containing semicircular arched roadways with half and full penetrations. The tests were carried out using a true triaxial rock test system. The results indicate that the true triaxial stress–strain curve included stages such as compression density, linear elasticity, yielding, and destructive destabilisation following the peak; the yield point was more pronounced than that in uniaxial and conventional triaxial tests; and the peak stress and strain of the semi-excavation were higher than those of the full excavation. Furthermore, full excavation led to greater deformation along the σ3 direction. The acoustic emission energy showed a sudden increase during the unloading stage, then fluctuated and increased with increasing stress until significant destabilisation occurred. Additionally, increased burial stress in the half-excavation decreased the proportion of tension cracks and shear cracks. Conversely, in semi-excavation, the proportion of tensile cracks decreased, while that of shear cracks increased. However, the opposite was observed in full excavation. In terms of fractal dimension, semi-excavation fragmentation due to stress concentration followed a power distribution, while the mass fragmentation in full excavation followed a random distribution due to uniform stress release. Furthermore, the specimen strength was positively correlated with fragmentation degree, and primary defects also influenced this degree. This study provides a crucial foundation for predicting and preventing rock explosions in deep underground engineering.
... The demand for these materials is anticipated to experience a significant surge in the forthcoming years [1,4,5]. The expected increase in global metal production will accelerate the depletion of shallow and easily accessible mineral resources, driving the need to explore and exploit deeper mineral reserves in the porphyry-epithermal mineral system [6]. Like for exploration, in the μ-LIBS to quantify mineral abundances (in wt%) including andalusite, kyanite, quartz, micas, epidote, plagioclase, and iron oxides. ...
... All samples comprise molybdenite (MoS 2 ), as the main ore mineral, accompanied by pyrite (FeS 2 ), quartz (SiO 2 ), hematite (Fe 2 O 3 ), magnetite (FeO), pyrophyllite (Al 2 Si 4 O 10 (OH) 2 ), alunite (KAl 3 (SO 4 ) 2 (OH) 6 ), white micas, and scarce occurrences of chalcopyrite (CuFeS 2 ) and covellite (CuS). In detail, the mineral characterization is as follows (Fig. 1d-f, Table 1 ...
Quantification of modal mineralogy in drill-core samples is crucial for understanding the geology and metal deportment in a mining operation. This study assesses conventional procedures to quantify modal mineralogy, that includes an initial drill-core logging, followed by petrographic descriptions and SEM-based automated mineralogy analyses performed in selected regions of interest, against a novel approach using laser-induced breakdown spectroscopy (LIBS). Our proposed methodology aims to quantify the modal mineralogy directly in a drill-core sample, avoiding previous stages of selection and preparation of samples. The novelty of our methodology lies in the simultaneous selection of spectral signals corresponding to a group of elements that are interrelated within a mineral species. The resulting signal combination is strongly correlated with a mineral found in the sample. Our proof of concept combines previously described mineralogy with a detailed spectroscopic and principal component analysis. The selected spectral signals are defined as “mineralogical patterns”, which are processed using supervised chemometrics methods, such as artificial neural networks, to enable an automated mineral classification. We implemented our workflow in three molybdenite-bearing drill-core samples, yielding results comparable to operational characterization, based on petrographic studies, and validated by QEMSCAN analyses, for a suite of ore and gangue minerals, including molybdenite, pyrite, hematite/magnetite, quartz, and aluminosilicates. In brief, we demonstrate how the LIBS-ANN technique can perform automated mineral quantification directly in selected drill-core regions of interest, minimizing previous sample preparation and without expert judgment.
... One of the vital procedures in the mineral exploration process is to use geological map to analyze and visualize features that are linked with the mineral deposits. These maps include basic data that is relevant to many different fields, such as the study of earthquakes, the development of infrastructure, and the search for groundwater and deep Earth resources [1][2][3][4]. Mineral perspective mapping and geological mapping have experienced significant transformations due to the advancements in remote sensing techniques, satellite imagery utilization, approach for visually detecting carbonates and other geological formations. This has prompted an imperative need to enhance the utilization of satellite imagery for more effective mapping of geological features [9]. ...
Automating mineral delineation and rock type analysis using remote sensing imaging data is a critical application of machine learning. Traditional machine learning methods often struggle with accuracy and precise map generation. This study aims to enhance performance through a refined deep learning model. In this work, we present a deep learning pipeline to map the mineral deposits in the study area. Initially, we apply a deep convolutional neural network (CNN) to a specialized mineral dataset to map mineral deposits within the study area. Subsequently, we build a hybrid model combining deep CNN layers with a support vector machine (SVM). This merger significantly improves classification accuracy from an initial 92.7% to 95.3%. In our approach, CNN layers function as feature extractors while the SVM serves as the classification model. Moreover, we conduct an evaluation of the SVM using polynomial kernels of degrees 3, 6, 9, and 12. The results indicate that the SVM with a degree of 12 achieved the highest classification accuracy, followed by degrees 9, 6, and 3. Experimental results demonstrate the effectiveness of our proposed method for classifying remote sensing imaging data, showcasing its potential for advancing mineral delineation and rock type analysis.
