tailieunhanh - Classification of plutonic rock types using thin section images with deep transfer learning

Classification of rocks is one of the basic parts of geological research and is a difficult task due to the heterogeneous properties of rocks. This process is time consuming and requires sufficiently knowledgeable and experienced specialists in the field of petrography. This paper has a novelty in classifying plutonic rock types for the first time using thin section images; and proposes an approach that uses the deep learning method for automatic classification of 12 types of plutonic rocks. Convolutional neural network based DenseNet121, which is one of the deep learning architectures, is used to extract the features from thin section images of rocks; and the classification process is carried out with a single layer fully connected neural network. | Turkish Journal of Earth Sciences Turkish J Earth Sci 2021 30 551-560 http earth TÜBİTAK Research Article doi yer-2007-19 Classification of plutonic rock types using thin section images with deep transfer learning 1 2 3 Özlem POLAT Ali POLAT Taner EKİCİ 1 Department of Mechatronic Engineering Faculty of Technology Sivas Cumhuriyet University Sivas Turkey 2 Sivas Provincial Disaster and Emergency Directorate Sivas Turkey 3 Department of Geological Engineering Faculty of Engineering Sivas Cumhuriyet University Sivas Turkey Received Accepted Published Online Final Version Abstract Classification of rocks is one of the basic parts of geological research and is a difficult task due to the heterogeneous properties of rocks. This process is time consuming and requires sufficiently knowledgeable and experienced specialists in the field of petrography. This paper has a novelty in classifying plutonic rock types for the first time using thin section images and proposes an approach that uses the deep learning method for automatic classification of 12 types of plutonic rocks. Convolutional neural network based DenseNet121 which is one of the deep learning architectures is used to extract the features from thin section images of rocks and the classification process is carried out with a single layer fully connected neural network. The deep learning model is trained and tested on 2400 images. AUC accuracy precision recall and f1-score are used as performance measure. The proposed approach classifies plutonic rock images on the test set with an average accuracy of and a maximum of . Thus the applied deep transfer learning is promising in geosciences and can be used to identify rock types quickly and accurately. Key words Rock classification plutonic rocks deep transfer learning DenseNet121 convolutional neural networks 1. Introduction usually volatiles such as gases and steam. Since their Rocks in .

• Địa Lý
• Journal of Earth Sciences
• Rock classification
• Plutonic rocks
• Deep transfer learning
• Convolutional neural networks
• Vietnam Journal of Earth Sciences
• General characteristics of rare earth
• Radioactive elements in Dong Pao deposit
• The natural tap and thermal waters
• Liquid scintillation counter
• Dodecahedron trapezohedron crystal habits
• Garnet bearing xenoliths
• Greenish reddish dark brown colour
• Gemmological characteristics
• Light rare earth elements
• metamorphism
• earth sciences
• Research
• VNU Journal of Science
• scientific research
• scientific reports
• HydrodynamicEnvironmental
• Bathymetry Mapping
• Estimation
• DETERMINING FLOW
• ASTER Data
• HANOI BUS
• Characteristics
• GEOLOGICAL MAP
• evapotranspiration
• biosolids application
• Environmental
• Assessment
• Photochemical
• remote sensing
• Building land
• Classification
• Landscape
• Thunderstorm forecast
• submerged
• Cu concentration
• urban expansion