Search Results (33)

Coal and rock recognition is one of the key technologies in mining production, but traditional methods have limitations such as single-feature representation dimension, insufficient robustness, and unbalanced performance in lightweight design under noise interference and complex feature conditions. To address these issues, an Efficient Channel Attention Reparameterized Network (ECA-RepNet) based on recurrence plot and Efficient Channel Attention mechanism is proposed. The one-dimensional vibration signal is mapped to the two-dimensional image space through a recurrence plot (RP), which retains the dynamic characteristics of the time series while capturing the complex patterns in the signal. Multi-scale feature extraction and lightweight design are achieved through the reparameterized large kernel block (RepLK Block) and the depthwise separable convolution (DSConv) module. The ECA module is introduced to embed multiple convolutional layers. Through global average pooling, one-dimensional convolution, and dynamic weight allocation, the modeling ability of inter-channel dependencies is enhanced, the model robustness is improved, and the computational overhead is reduced. Experimental results demonstrate that the ECA-RepNet model achieves 97.33% accuracy, outperforming classic models including ResNet, CNN, and MobileNet in parameter efficiency, training time, and inference speed. Full article

In the Yangquan coal mining region, China, muffled blasting sounds commonly occur in mine surrounding rocks resulting from instantaneous energy release following the elastic deformation of overlying brittle rock layers; they are related to fracture development. Although these events rarely cause immediate hazards, their acoustic signatures contain critical information about cumulative rock damage. Currently, conventional monitoring of muffled blasting sounds and surrounding rock stability relies on microseismic systems and on-site sampling techniques. However, these methods exhibit low identification efficiency for muffled blasting events, poor real-time performance, and strong subjectivity arising from manual signal interpretation and empirical threshold setting. This article proposes retentive depthwise gated network (RDGNet). By combining retentive network sequence modeling, depthwise separable convolution, and a gated fusion mechanism, RDGNet enables multimodal feature extraction and the fusion of acoustic emission sequences and audio Mel spectrograms, supporting real-time muffled blasting sound recognition and lithology classification. Results confirm model robustness under noisy and multisource mixed-signal conditions (overall accuracy: 92.12%, area under the curve: 0.985, and Macro F1: 0.931). This work provides an efficient approach for intelligent monitoring of coal mine rock stability and can be extended to safety assessments in underground engineering, advancing the mining industry toward preventive management. Full article

To address the challenges of small-sample rock fine classification—such as overfitting caused by limited sample size and the increased complexity resulting from high inter-class similarity—this study proposes a Stacking ensemble method tailored for small-sample rock image classification. Using a dataset of seven rock categories provided by the BdRace platform, 38 features were extracted across three dimensions—color, texture, and grain size—through grayscale thresholding, HSV color space analysis, gray-level co-occurrence matrix computation, and morphological analysis. The interrelationships among features were evaluated using Spearman correlation analysis and hierarchical clustering, while a voting-based fusion strategy integrated Lasso regularization, gray correlation analysis, and variance filtering for feature dimensionality reduction. The Whale Optimization Algorithm (WOA) was employed to perform global optimization on the base learners, including Random Forest (RF), K-Nearest Neighbors (KNN), Naive Bayes (NBM), and Support Vector Machine (SVM), with Logistic Regression serving as the meta-classifier to construct the final Stacking ensemble model. Experimental results demonstrate that the Stacking method achieves an average classification accuracy of 85.41%, with the highest accuracy for black coal identification (97.16%). Compared to the single models RF, KNN, NBM, and SVM, it improves accuracy by 7.27%, 8.64%, 6.79%, and 6.94%, respectively. Evidently, the Stacking model integrates the strengths of individual models, significantly enhancing recognition accuracy. This research not only improves rock identification accuracy and reduces exploration costs but also advances the intelligent transformation of geological exploration, demonstrating considerable engineering application value. Full article

To address the current challenges of low single-sensor recognition accuracy for coal and rock cutting states, redundant channel feature responses, and poor performance in traditional neural network models, this paper proposes a new multi-sensor coal and rock cutting state recognition model based on a graph neural network (GNN). This model, consisting of a feature encoder, an information exchange module, and a feature decoder, enhances the communication of feature responses between filters within the same layer, thereby improving feature capture and reducing channel redundancy. Comparative, ablation, and noise-resistance experiments on multi-sensor datasets validate the effectiveness, versatility, and robustness of the proposed model. Experimental results show that compared to the baseline models, CNN3, ResNet, and DenseNet achieve improvements of 2.47%, 2.78%, and 1.50%, respectively. With the addition of the ACI block, the ResNet model achieves the best noise-resistance performance, achieving an accuracy of 93.27% even in 6 dB noise, demonstrating excellent robustness. Embedded deployment experiments further confirmed that the proposed model maintains an inference time of less than 216.1 ms/window on the NVIDIA Jetson Nano, meeting the real-time requirements of actual industrial scenarios and demonstrating its broad application prospects in resource-constrained underground working environments. Full article

This study investigates the relationship between anchor cable drilling parameters and roadway roof strata properties. The goal is to enable rapid and accurate rock type identification. Field-measured drilling data were processed using data cleaning and wavelet transform noise reduction. Four recognition models were developed and compared: LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), DBO-LSTM (Dung Beetle Optimizer), and DBO-BiLSTM. The results demonstrate a strong correlation between vibration, pressure signals and rock strength, enabling the effective differentiation of rock types. All models performed exceptionally for coal seams with distinct features, achieving 100% accuracy, precision, recall, and F1 scores. Model performance improved with increased complexity for strata with subtle differences, such as sandstone and mudstone. The DBO-BiLSTM model outperformed others, showing significant improvements in accuracy, recall, and F1 score compared to LSTM, BiLSTM, and DBO-LSTM models. Specifically, accuracy improved by up to 9%, recall by 12.48%, and F1 score by 13.06%. These findings highlight the DBO-BiLSTM model’s superior recognition capability for roof strata drilling signals. This method provides a robust technical foundation for lithology identification in Measurement While Drilling (MWD) systems. It supports more precise and efficient roadway design in complex geological conditions. Full article

The accurate identification of coal and rock at the mining face is often hindered by adverse underground imaging conditions, including poor lighting and strong reflectivity. To tackle these issues, this work introduces a recognition framework specifically designed for underground environments, leveraging joint migration and enhancement of multidimensional and full-scale visual representations. A Transformer-based architecture is employed to capture global dependencies within the image and perform reflectance component denoising. Additionally, a multi-scale luminance adjustment module is integrated to merge features across perceptual ranges, mitigating localized brightness anomalies such as overexposure. The model is structured around an encoder–decoder backbone, enhanced by a full-scale connectivity mechanism, a residual attention block with dilated convolution, Res2Block elements, and a composite loss function. These components collectively support precise pixel-level segmentation of coal–rock imagery. Experimental evaluations reveal that the proposed luminance module achieves a PSNR of 21.288 and an SSIM of 0.783, outperforming standard enhancement methods like RetinexNet and RRDNet. The segmentation framework achieves a MIoU of 97.99% and an MPA of 99.28%, surpassing U-Net by 2.21 and 1.53 percentage points, respectively. Full article

Implementing precise and advanced early warning systems for rock bursts is a crucial approach to maintaining safety during coal mining operations. At present, FEMR data play a key role in monitoring and providing early warnings for rock bursts. Nevertheless, conventional early warning systems are associated with certain limitations, such as a short early warning time and low accuracy of early warning. To enhance the timeliness of early warnings and bolster the safety of coal mines, a novel early warning model has been developed. In this paper, we present a framework for predicting the FEMR signal in deep future and recognizing the rock burst precursor. The framework involves two models, a guided diffusion model with a transformer for FEMR signal super prediction and an auxiliary model for recognizing the rock burst precursor. The framework was applied to the Buertai database, which was recognized as having a rock burst risk. The results demonstrate that the framework can predict 360 h (15 days) of FEMR signal using only 12 h of known signal. If the duration of known data is compressed by adjusting the CWT window length, it becomes possible to predict data over longer future time spans. Additionally, it achieved a maximum recognition accuracy of 98.07%, which realizes the super prediction of rock burst disaster. These characteristics make our framework an attractive approach for rock burst predicting and early warning. Full article

This article presents an example of the treatment of rock mass disturbed by shallow mining of hard coal in the Małopolska voivodeship, Poland. Considering various methods of rock mass recognition and ways of eliminating shallow voids, recipes for sealing slurries containing mainly liquefiers were developed and used in drilling and injection works in a 10 m-long hole. The course and intensity of rock layer deformation phenomena depend on both natural conditions and the mining method used. At a small depth of hard coal mining (up to 100 m below ground level), the fracture zone may reach the ground surface. In such conditions, sinkholes of various sizes may form on the ground surface. The proposed recipes for sealing slurries, as well as the presented technology for carrying out backfilling works, can be very useful at the stage of selecting the method for liquidation of shallow-lying voids in the carboniferous rock mass. Full article

Coal structures exhibit considerable differences in rock properties and adsorption capacities. The physical properties of coal rocks are fundamental to understanding oil and gas reservoirs, while adsorption capacity directly impacts the gas content in coal seams. The accurate recognition of coal structures is essential for evaluating productivity and guiding coalbed methane well development. This study examines coal rocks of Benxi Formation in Ordos Basin. Using core photographs and logging curves, we classified the coal structures into undeformed coal, cataclastic coal, and granulated-mylonitized coal. AC, DEN, CAL, GR, and CN15 logging curves were selected to build a coal structure recognition model utilizing a long short-term memory (LSTM) neural network. This approach addresses the gradient vanishing and exploding issues often encountered in traditional neural networks, enhancing the model’s capacity to handle nonlinear relationships. After numerous iterations of learning and parameter adjustments, the model achieved a recognition accuracy of over 85%, with 32 hidden units, a minimum batch size of 28, and up to 150 iterations. Validation with independent well data not involved in the model building process confirmed the model’s effectiveness, meeting the practical needs of the study area. The results suggest that the study area is predominantly characterized by undeformed coal, with cataclastic coal and granulated-mylonitized coal more developed along fault trends. Full article

Previous research results preliminarily indicated that the Coal Failure Events (CFEs) that occurred during the process of Underground Pressure Relief Drilling (UPRD) represented the phenomenon of coal fracture and energy release. The research results had excellent value for the monitoring and response of pressure relief drilling while drilling, but there were still some special situations that needed to be analyzed and studied in actual on-site testing. So, through on-site testing and data statistical analysis, the study investigated the applicability of the innovative external Monitoring-While-Drilling (MWD) method for UPRD with more coal failure events and made a quantitative statistic of the CFEs and their relationship with abutment pressure to reveal the applicability of the external MWD method and characteristic of CFEs. The results showed that hundreds of CFEs were produced in the UPRD process, which must be removed to ensure the accuracy of the MWD method. Although CFEs bring recognition difficulties, they also provide conditions for studying their own distribution and characteristics. Results showed that more CFEs were produced in the depth of difficult drilling, which indicated that there was a positive correlation between the degree of difficulty in drilling and the number of CFEs. In addition, spectrum analysis showed that the depths with more CFE occurrence were more likely to produce high-frequency events. When the surrounding stress of drilling rocks is high, the occurrence of small fractures with a higher main frequency may become more frequent and consistent; more fractures with similar failure forms would occur, which may have a lower fractal dimension and promote the generation of more failure. The research results were of great significance for the MWD method for UPRD, a quantitative study of CFEs and their generation characteristics during UPRD construction. Full article

Open-pit coal mining offers high resource recovery, excellent safety conditions, and large-scale production. However, the process generates significant dust, leading to occupational diseases such as pneumoconiosis among miners and adversely affecting nearby vegetation through dust deposition, which hinders photosynthesis and causes ecological damage. This limits the transition of open-pit mining to a green, low-carbon model. Among these processes, blasting generates the most dust and has the widest impact range, but the specific amount of dust generated has not yet been thoroughly studied. This study integrates indoor experiments, theoretical analyses, and field tests, employing the Split Hopkinson Pressure Bar (SHPB) system to conduct impact loading tests on coal–rock samples under pressures ranging from 0.13 MPa to 2.0 MPa. The results indicate that as the impact load increases, the proportion of large-sized blocks decreases while smaller fragments and powdered samples increase, signifying intensified sample fragmentation. Using stress wave attenuation theory, this study translates indoor impact loadings to field blast shock waves, revealing the relationship between blasting dust mass fraction and impact pressure. Field tests at the Haerwusu open-pit coal mine validated the formula. Using image recognition technology to analyze post-blast muck-pile fragmentation, the estimated dust production closely matched the calculated values, with an error margin of less than 10%. This formula provides valuable insights for estimating dust production and improving dust control measures during open-pit mine blasting operations. Full article

Until the late 20th century, the idea of identifying wildfires in deep time was not generally accepted. One of the basic problems was the fact that charcoal-like wood fragments, so often found in sedimentary rocks and in coals, were termed fusain and, in addition, many researchers could not envision wildfires in peat-forming systems. The advent of Scanning Electron Microscopy and studies on modern charcoals and fossil fusains demonstrated beyond doubt that wildfire residues may be recognized in rocks dating back to at least 350 million years. Increasing numbers of studies on modern and fossil charcoal assemblages from the 1970s through the 1990s established the potential importance of wildfires in the fossil record, using Carboniferous examples in particular. Since the 1990s, extensive progress has been made in understanding modern wildfires and their byproducts. New techniques to study ancient charcoals have allowed considerable progress to be made to integrate modern and ancient fire studies, both before and after the evolution of mankind. Four important developments have made a reassessment of Carboniferous wildfires necessary: the recognition of the role of atmospheric oxygen in controlling the occurrence of wildfire; the development of new microscopical techniques allowing more detailed anatomical data to be obtained from charcoal; the integration of molecular studies with the evolution of fire traits; and new developments in or understanding of post-fire erosion/deposition systems. Full article

The recognition of coal–rock interface is very crucial for research in the intelligent production of coal mines. To this end, the study investigated the application of terahertz time-domain spectroscopy in the recognition of coal–rock interface, including the identification of coal–rock and coal–rock mixtures, as well as the accurate characterization of coal seam thickness. Terahertz detection was used to obtain the optical parameter information of pressed pellets prepared by mixing two different kinds of coal and two kinds of rock. Based on the experiment’s results, a database was established for the identification of coal–rock interfaces for coal mining machines. The terahertz detection was performed on 10 different kinds of sheet anthracite with different thicknesses, and the terahertz spectra of coal seams with different thicknesses were simulated and calculated using simulation software. By comparing the two effective mining thicknesses, parameters can be provided for coal seam mining. The experiment and simulation show that the terahertz time-domain spectroscopy technology has a promising application prospect in the identification of coal–rock interface. Full article

In the process of coal mining, one of the main reasons for the high labor intensity of workers and the frequent occurrence of casualties is the low level of intelligence of coal mining equipment. As the core equipment in the process of coal mining, the intelligence level of shearers directly affects the safety production and mining efficiency of coal mines. Coal and rock recognition technology is the core technology used to realize the intelligentization of shearers, which is an urgent technical problem to be solved in the field of coal mining. In this paper, coal seam images, rock stratum images, and coal–rock mixed-layer images of a coal mining area are taken as the research object, and key technologies such as the construction of a sample image library, classification and recognition, and semantic segmentation are studied by using the relevant theoretical knowledge of artificial neural network models. Firstly, the BP neural network is used to classify and identify coal seam images, rock stratum images, and coal–rock mixed-layer images, so as to distinguish which of the current mining targets of a shearer is the coal seam, rock stratum, or coal–rock mixed layer. Because different mining objectives will lead to different working modes of a shearer, it is necessary to maintain normal power to cut coal when encountering a coal seam, to stop working when encountering rock stratum, and to cut coal along the boundary between a coal seam and rock stratum when encountering a coal–rock mixed stratum. Secondly, the DeepLabv3+ model is used to perform semantic segmentation experiments on the coal–rock mixed-layer images. The purpose is to find out the distribution of coal and rocks in the coal–rock mixed layer in the coal mining area, so as to provide technical support for the automatic adjustment height of the shearer. Finally, the research in this paper achieved a 97.16% recognition rate in the classification and recognition experiment of the coal seam images, rock stratum images, and coal–rock mixed-layer images and a 91.2% accuracy in the semantic segmentation experiment of the coal–rock mixed-layer images. The research results of the two experiments provide key technical support for improving the intelligence level of shearers. Full article

Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is labor-intensive and significantly impacts the final outcome. Deep learning (DL) offers an effective approach to automatically extract features from raw data. Among the various DL techniques, convolutional neural networks (CNNs) have proven to be particularly effective. In this paper, we propose an intelligent method for recognizing coal–rock by fusing multiple preprocessing techniques applied to near-infrared spectra and employing dual attention. Initially, a signal-to-RGB image conversion method is applied to fuse three types of preprocessing data, namely first-order differential, second-order differential, and standard normal transform, into an RGB image representation. Subsequently, we propose a neural network model (CBAM-VIT) that integrates the convolutional block attention mechanism (CBAM) and Vision Transformer (VIT). When evaluated on the coal–rock dataset, this model achieves an accuracy of 98.5%, surpassing the performance of VIT (95.3%), VGG-16 (89%), and AlexNet (82%). The comparative results clearly demonstrate that the proposed coal–gangue recognition method yields significant improvements in classification outcomes. Full article