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Rapid coal‐rock identification is one of the key technologies for intelligent and unmanned coal mining. Currently, the existing image recognition algorithms cannot satisfy practical needs in terms of recognition speed and accuracy. In view of the evident differences between coal and rock in visual attributes such as color, gloss and texture, the complete local binary pattern (CLBP) image feature descriptor is introduced for coal and rock image recognition. Given that the original algorithm oversimplifies local texture features by ignoring imaging information from higher‐order pixels and the concave and convex areas between adjacent sampling points, this paper proposes a higher‐order differential median CLBP image feature descriptor to replace the original CLBP center pixel gray with a local gray median, and replace the binary differential with a second‐order differential. Meanwhile, for the high dimensionality of CLBP descriptor histogram and feature redundancy, deep learning perceptual field theory is introduced to realize data nonlinear dimensionality reduction and deep feature extraction. With relevant experiments conducted, the following conclusion can be drawn: (1) Compared with that of the original CLBP, the recognition accuracy of the improved CLBP algorithm is greatly improved and finally stabilized above 94.3% under strong noise interference; (2) Compared with that of the original CLBP model, the single image recognition time of the coal rock image recognition model fusing the improved CLBP and the receptive field theory is 0.0035 s, a reduction of 71.0%; compared with the improved CLBP model (without the fusion of receptive field theory), it can shorten the recognition time by 97.0%, but the accuracy rate still maintains more than 98.5%. The method offers a valuable technical reference for the fields of mineral development and deep mining. Highlights This paper proposes a higher‐order differential median CLBP image feature descriptor to replace the original CLBP center pixel gray with a local gray median, and replace the binary differential with a second‐order differential. For the high dimensionality of CLBP descriptor histogram and feature redundancy, deep learning perceptual field theory is introduced to realize data nonlinear dimensionality reduction and deep feature extraction. In view of the evident differences between coal and rock in visual attributes such as color, gloss, and texture, the complete local binary pattern (CLBP) image feature descriptor is introduced for coal and rock image recognition, and the original algorithm oversimplifies local texture features by ignoring imaging information from higher‐order pixels and the concave and convex areas between adjacent sampling points and proposes a higher‐order differential median CLBP image feature descriptor replacing the original CLBP center pixel gray with a local gray median, and replacing the binary differential with a second‐order differential. Meanwhile, for the high dimensionality of CLBP descriptor histogram and feature redundancy, deep learning perceptual field theory is introduced to realize data nonlinear dimensionality reduction and deep feature extraction.

A fact-filled guide to common rocks and minerals includes each mineral's chemical formula, where it can be naturally found, and activities to help explore different proerties of rocks and minerals.

Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment, the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work. In this study, based on the UAV remote sensing technology in acquiring refined model and quantitative parameters, a semi-automatic dangerous rock identification method based on multi-source data is proposed. In terms of the periodicity UAV-based deformation monitoring, the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud. Taking a high-steep slope as research object, the UAV equipped with special sensors was used to obtain multi-source and multi-temporal data, including high-precision DOM and multi-temporal 3D point clouds. The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass, realizes the closed-loop of identification and accuracy verification; changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy. The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification, and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.

Classifying and recognizing the reflection spectrum of coal–rock is an innovative method for coal–rock identification in coal mining process. Herein, a classification and recognition method of coal-rock reflection spectrum based on wavelet scattering transform (WST) and bidirectional long–short-term memory (BiLSTM) network was proposed to improve the recognition speed and accuracy. First, the reflection spectra of coal–rock samples were obtained using the coal–rock reflection spectrum information acquisition platform, and two spectral databases with different coal–rock states and different sampling parameter combinations were established to train the network model. Second, the original data were preprocessed by Gaussian filtering and randomly divided into the training set and test set. The wavelet scattering network was used to effectively extract spectral features from the reflection spectrum and generate a feature matrix. Finally, the training set feature matrix was input into the BiLSTM network model for training to obtain the WST–BiLSTM model. The effectiveness of the proposed network model was verified using the test set. The experimental results showed that the WST–BiLSTM model can classify and identify the coal–rock reflection spectrum more accurately than other related models in literature, and the recognition accuracy for the two databases reached 99.4% and 100%. Based on the constructed multi-state and multi-parameter combination spectral database, the proposed coal–rock recognition model has good adaptability to the reflected spectrum collected by different parameters. Hence, this model can provide a theoretical basis and technical premise for automatic and intelligent coal mining.HighlightsA reflection spectrum database is established with different coal-rock states and sampling parametersA coal-rock reflection spectrum recognition model is developed using wavelet scattering transform feature extraction method.Training speed and recognition accuracy of the model are improved by changing the sampling parameters.

Real-time coal–rock identification is essential for intelligent mining, enabling hazard prevention and geological modeling. However, existing methods often suffer from unclear bit–rock interaction mechanisms, signal distortion, sensor remoteness, or delayed data acquisition, limiting their effectiveness in continuous operations. This study proposes a novel approach for real-time coal–rock identification based on multi-source near-bit drilling data. A near-bit data acquisition system was developed and positioned directly behind the drill bit, integrating sensors to capture high-fidelity parameters—including weight on bit (WOB), torque, rotational speed, rate of penetration (ROP), natural gamma ray, and borehole trajectory—thereby eliminating frictional interference from the drill string. A data-driven theoretical model was established to derive a near-bit drillability index (NDI) for rock strength and to correlate gamma ray responses with lithology. Field trials were conducted in a coal mine in northern Shaanxi, involving over 30 boreholes and systematic core validation. The results demonstrate that the method enables continuous, high-resolution identification of coal–rock interfaces and strength variations along the borehole trajectory, with interpreted results aligning well with core logs and achieving approximately 85% accuracy in strength estimation. By ensuring compatibility with conventional drilling rigs and supporting real-time data transmission and 3D geological updating, this study offers a practical and robust technical pathway for achieving geological transparency and real-time steering in underground coal mining.

Learn how to identify rocks and minerals and appreciate the beauty of the natural world with Rocks & Minerals: An Illustrated Field Guide. Expert geologist Dr. Evelyn Mervine takes you through 50 profiles of these natural materials, including their characteristics, chemical compositions, occurrences, and key identifiers.

During roadway excavation, the presence of weak interlayers and fractured rock masses significantly affects roof stability. To achieve timely and effective roadway support, it is crucial to identify and predict different rock types based on drilling signals from roof bolters. This study combines theoretical analysis, laboratory tests, and numerical simulation to investigate the intrinsic relationship between drilling signals and rock properties. A hybrid denoising method called “Grey Wolf Optimizer‐Variational Mode Decomposition‐Wavelet Threshold Denoising (GWO‐VMD‐WTD)” is proposed, along with a lithology identification model based on an SSA‐BP neural network. The results demonstrate that: rotational speed, torque, thrust, and vibration acceleration show significant correlations with rock properties; the GWO‐VMD‐WTD method effectively restores authentic signals with notable noise reduction; the SSA‐BP neural network model, trained using time‐domain and frequency‐domain feature parameters, exhibits strong generalization capability, robustness, and reliability, achieving 95.8333% accuracy in rock classification. This provides critical references for real‐time monitoring and control of roof deterioration zones. Drilling detection test platform.