An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction...An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.展开更多
In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as constru...In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as construction and decoration, this classification makes sense and fully plays its role. However, this classification is slow, approximate and subjective. Automatic classification curbs this subjectivity and fills this gap by offering methods that reflect human perception. We propose a new approach to rock classification based on direct-view images of rocks. The aim is to take advantage of feature extraction methods to estimate a rock dictionary. In this work, we have developed a classification method obtained by concatenating four (4) K-SVD variants into a single signature. This method is based on the K-SVD algorithm combined with four (4) feature extraction techniques: DCT, Gabor filters, D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor, K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a classification method obtained by concatenating four (4) variants of K-SVD. The performance of our method was evaluated on the basis of performance indicators such as accuracy with other 96% success rate.展开更多
Based on the safety coefficient method,which assigns rock failure criteria to calculate the rock mass unit,the safety coefficient contour of surrounding rock is plotted to judge the distribution form of the fractured ...Based on the safety coefficient method,which assigns rock failure criteria to calculate the rock mass unit,the safety coefficient contour of surrounding rock is plotted to judge the distribution form of the fractured zone in the roadway.This will provide the basis numerical simulation to calculate the surrounding rock fractured zone in a roadway.Using the single factor and multi-factor orthogonal test method,the evolution law of roadway surrounding rock displacements,plastic zone and stress distribution under different conditions is studied.It reveals the roadway surrounding rock burst evolution process,and obtains five kinds of failure modes in deep soft rock roadway.Using the fuzzy mathematics clustering analysis method,the deep soft surrounding rock failure model in Zhujixi mine can be classified and patterns recognized.Compared to the identification results and the results detected by geological radar of surrounding rock loose circle,the reliability of the results of the pattern recognition is verified and lays the foundations for the support design of deep soft rock roadways.展开更多
基金financially supported by the National Science and Technology Major Project——Deep Earth Probe and Mineral Resources Exploration(No.2024ZD1003701)the National Key R&D Program of China(No.2022YFC2905004)。
文摘An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.
文摘In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as construction and decoration, this classification makes sense and fully plays its role. However, this classification is slow, approximate and subjective. Automatic classification curbs this subjectivity and fills this gap by offering methods that reflect human perception. We propose a new approach to rock classification based on direct-view images of rocks. The aim is to take advantage of feature extraction methods to estimate a rock dictionary. In this work, we have developed a classification method obtained by concatenating four (4) K-SVD variants into a single signature. This method is based on the K-SVD algorithm combined with four (4) feature extraction techniques: DCT, Gabor filters, D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor, K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a classification method obtained by concatenating four (4) variants of K-SVD. The performance of our method was evaluated on the basis of performance indicators such as accuracy with other 96% success rate.
基金provided by the National Natural Science Foundation of China(Nos.51322401,51309222,51323004,51579239 and 51574223)the Opening Project Fund of Shandong Provincial Key Laboratory of Civil Engineering Disaster Prevention and Mitigation(No.CDPM2014KF03)+2 种基金the State Key Laboratory for GeoMechanics Opening Project Fund of Shandong Provincial Key Laboratory of Civil Engineering Disaster Prevention and MitigationDeep Underground Engineering,China University of Mining&Technology(No.SKLGDUEK1305)China Postdoctoral Science Foundation(Nos.2014M551700and 2013M531424)
文摘Based on the safety coefficient method,which assigns rock failure criteria to calculate the rock mass unit,the safety coefficient contour of surrounding rock is plotted to judge the distribution form of the fractured zone in the roadway.This will provide the basis numerical simulation to calculate the surrounding rock fractured zone in a roadway.Using the single factor and multi-factor orthogonal test method,the evolution law of roadway surrounding rock displacements,plastic zone and stress distribution under different conditions is studied.It reveals the roadway surrounding rock burst evolution process,and obtains five kinds of failure modes in deep soft rock roadway.Using the fuzzy mathematics clustering analysis method,the deep soft surrounding rock failure model in Zhujixi mine can be classified and patterns recognized.Compared to the identification results and the results detected by geological radar of surrounding rock loose circle,the reliability of the results of the pattern recognition is verified and lays the foundations for the support design of deep soft rock roadways.
