The mineral intelligence identification method based on deep learning algorithms

被引：0
|
作者：
Guo Y. [1 ,4 ,5 ]
Zhou Z. [2 ]
Lin H. [3 ]
Liu X. [1 ,4 ,5 ]
Chen D. [1 ,4 ,5 ]
Zhu J. [1 ,4 ,5 ]
Wu J. [1 ,4 ,5 ]
机构：
[1] School of Earth and Space Sciences, Peking University, Beijing
[2] School of Electronics Engineering and Computer Science, Peking University, Beijing
[3] School of Software & Microelectronics, Peking University, Beijing
[4] National Experimental Teaching Demonstrating Center of Earth Sciences(Peking University), Beijing
[5] National Virtual Simulation Experimental Teaching Center of Earth Sciences(Peking University), Beijing
来源：
Earth Science Frontiers | 2020年 / 27卷 / 05期
关键词：
Computer vision; Convolutional neural network; Deep learning; Mineral classification; Residual neural network;
D O I：
10.13745/j.esf.sf.2020.5.45
中图分类号：
学科分类号：
摘要：
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. © 2020, Editorial Office of Earth Science Frontiers. All right reserved.
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页码：39 / 47
页数：8
相关论文
共 28 条
  • [1] LU Y, YANG K, XIU L C, Et al., Identification of hydrocarbon and clay minerals based on near-infrared spectroscopy and its geological significance, Geological Bulletin of China, 36, 10, pp. 1884-1891, (2017)
  • [2] WANG R S, YANG S M, BAI K., A Review of mineral spectral identification methods and models with imaging spectrometer, Remote Sensing for Land Resources, 19, 1, pp. 1-9, (2007)
  • [3] LIU S W, GAN F P, YAN B K, Et al., Application of the imaging spectroscopic technique in mineral identification and mapping, Geology in China, 33, 1, pp. 178-186, (2006)
  • [4] MENG J K, HAO G, ZHOU Z L, Et al., Application of multi-features matching decision tree to mineral identification, Scientific and Technological Management of Land and Resources, 29, 6, pp. 93-96, (2012)
  • [5] HINTON G E, OSINDERO S, TEH Y W., A fast learning algorithm for deep belief nets, Neural Computation, 18, 7, pp. 1527-1554, (2006)
  • [6] LECUN Y, BENGIO Y, HINTON G., Deep learning, Nature, 521, 7553, pp. 436-444, (2015)
  • [7] ZHOU Y Z, WANG J, ZUO R G, Et al., Machine learning, deep learning and Python language in field of geology, Acta Petrologica Sinica, 34, 11, pp. 3173-3178, (2018)
  • [8] GIRSHICK R., Fast R-CNN, Proceedings of 2015 IEEE international conference on computer vision (ICCV), pp. 1440-1448, (2015)
  • [9] HE K, GKIOXARI G, DOLLAR P, Et al., Mask R-CNN, Proceedings of 2017 IEEE international conference on computer vision (ICCV), pp. 2980-2988, (2017)
  • [10] 5, (2008)
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