Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network

Xiangjin Ran,Jinxin He,Yanyan Zhang,Linfu Xue,Zeyu Liu,Xuejia Sang

doi:10.3390/math7080755

Xiangjin Ran, Jinxin He + Show 4 more

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The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The proposed approach can identify six common rock types with an overall classification accuracy of 97.96%, thus outperforming other established deep-learning models and a linear model. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.

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Rocks are a fundamental component of Earth
The results show that the convolutional neural networks (CNNs) model outperforms advantage of Rock Types deep CNNs (RTCNNs) model will be more obvious
The results show that the CNNs the linear Support Vector Machine (SVM) model in terms of classifying rocks from field images

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Rocks are a fundamental component of Earth. They contain the raw materials for virtually all modern construction and manufacturing and are indispensable to almost all the endeavors of an advanced society. In addition to the direct use of rocks, mining, drilling, and excavating provide the material sources for metals, plastics, and fuels. Natural rock types have a variety of origins and uses. The three major groups of rocks (igneous, sedimentary, and metamorphic) are further divided into sub-types according to various characteristics. Rock type identification is a basic part of geological surveying and research, and mineral resources exploration. It is an important technical skill that must be mastered by students of geoscience

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