Rock and mineral thin section identification based on deep learning

被引：0
|
作者：
Zhang L. [1 ,2 ,3 ]
Lu W. [2 ]
Zhang J. [2 ]
Peng G. [2 ]
Bu J. [1 ]
Tang K. [1 ]
Xie J. [1 ]
Xu Z. [1 ]
Yang H. [1 ]
机构：
[1] Hunan Remote Sensing Geological Survey and Monitoring Institute, Changsha
[2] Key Laboratory of Met allogenic Prediction of Nonferrous Metals and Geological Environment Monitor, Central South University, Ministry of Education, Changsha
[3] Hunan Key Laboratory of Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area, Hunan Natural Resources Affairs Center, Changsha
来源：
Earth Science Frontiers | 2024年 / 31卷 / 03期
关键词：
convolutional neural network; deep learning; image recognition; machine learning; rock identification;
D O I：
10.13745/j.esf.sf.2023.6.7
中图分类号：
学科分类号：
摘要：
Rock-mineral microscopic image identification is one of the basic means of rock and mineral identification, which is of great significance to the exploration of geological resources. Thin-section microscopic images are generally carried out in the laboratory. This work is tedious and time-consuming, requires a lot of human resources, and the accuracy is limited by the experience of the expert. Deep learning intelligent image recognition algorithm can extract the deep features of microscopic images by convolutional neural network, to achieve the purpose of fast and accurate classification and recognition of microscopic images. In this study, the PyCharm platform is used as the deep learning framework, and the data set that can be applied to the classification and recognition of rock-mineral microscopic images is made based on six data sets such as the teaching rock slice dataset of Nanjing University and the Carboniferous limestone microscopic image dataset of South North China on the China Science Data Network. We design a VGG convolutional neural network model. The model can analyze the feature information in the deep layer of the whole rock slice image and the single mineral image respectively, to achieve the purpose of identifying rock slices. The test results show that with the increase of model training times, the loss function between the predicted value and the real value is decreasing, and the recognition accuracy is increasing. After 50 and 30 cycles of training, the loss function and recognition accuracy of the model have been basically convergent. The recognition success rate of the model for the microscopic image test set is higher than 90%, indicating that the model has a good feature extraction effect for the image and can complete the task of rock-mineral microscopic image recognition. Through the research of this paper, it can be realized that deep learning has high efficiency and accuracy for dealing with such tasks as rock and mineral identification. Developing relevant models and applying them to front-end software can speed up the speed of mineral resources exploration and has important application significance for production practice. © 2024 Science Frontiers editorial department. All rights reserved.
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页码：498 / 510
页数：12
相关论文
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