The mineral intelligence identification method based on deep learning algorithms
GUO Yanjun, ZHOU Zhe, LIN Hexun, LIU Xiaohui, CHEN Danqiu, ZHU Jiaqi, WU Junqi
1. School of Earth and Space Sciences, Peking University, Beijing 100871, China
2. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
3. School of Software & Microelectronics, Peking University, Beijing 102600, China
4. National Experimental Teaching Demonstrating Center of Earth Sciences(Peking University), Beijing 100871, China
5. National Virtual Simulation Experimental Teaching Center of Earth Sciences(Peking University), Beijing 100871, China
Mineral classification plays an important role in many research fields. Intelligent mineral identification based on deep learning brings a new development direction to these fields, it can effectively save labor costs as well as reducing classification errors. The purpose of this paper is to study an accurate, efficient and versatile intelligent mineral identification method by deep learning. We trained and tested this method on five kinds of minerals: quartz, hornblende, biotite, garnet and olivine. We used the convolution neural network, commonly applies to image analysis, to establish the model and designed the model structure based on residual network (ResNet). In order to support deep learning, we collected microscopic imaging data sets of five kinds of minerals independently, and used them to train, verify and test the model. Besides, we also expanded the data sets for training through reasonable data augmentation. In terms of structural design of the convolutional neural network, we selected ResNets-18 as the framework and finally trained a successful mineral identification model achieving 89% accuracy in the test.