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

Xiangjin Ran, Linfu Xue, Yanyan Zhang, Zeyu Liu, Xuejia Sang and Jinxin He
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Xiangjin Ran: College of Earth Science, Jilin University, Changchun 130061, China
Linfu Xue: College of Earth Science, Jilin University, Changchun 130061, China
Yanyan Zhang: Jilin Business and Technology College, Changchun 130012, China
Zeyu Liu: College of Earth Science, Jilin University, Changchun 130061, China
Xuejia Sang: School of Environment Science and Spatial Informatics (CESI), China University of Mining and Technology, Xuzhou 221008, China
Jinxin He: College of Earth Science, Jilin University, Changchun 130061, China

Mathematics, 2019, vol. 7, issue 8, 1-16

Abstract: 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.

Keywords: deep learning; convolutional neural network; rock types; automatic identification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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