Automatic identification and classification in lithology based on deep learning in rock images

Ye Zhang, Mingchao Li, Shuai Han

Research output: Journal article publicationJournal articleAcademic researchpeer-review

42 Citations (Scopus)


It is important for geology analysis to make identification and classification in lithology. It is a new way to establish the identification model in machine learning. In this research, a transfer learning model of rock images was built based on the Inception-v3 model. It was adapted to process 173 granite images, 152 phyllite images and 246 breccia images to train the transfer learning model. Images in trained data set and in test data set were used to test the model, respectively. 3 images in each group from the trained data set were selected to test the model. There were no identification and classification errors and the all of the probabilities were more than 90%. 9 images in each group from the test data set were also selected to test the model. There were no identification and classification errors. The probabilities of phyllite group were more than 90%. While, the probabilities of 2 images in granite and 1 image in breccia group were less than 70%. It was thought that there were fewer images with similar pattern leading to the bad results. To verify the hypothesis, cut the images with low probabilities and added 3 images to the trained data set in each group to retrain the model. The 3 images with low probabilities were tested in the retrained model and their probabilities were more than 85 %. It showed the model had good robustness and generalization if there were enough images. Compared with the traditional machine learning, the proposed method has much strength. First, there is no need to do manual tuning and it processes the data in the model automatically. Second, there is no specific requirement in image pixel, distance and size. At last, the model can have a robust identification and classification result if a suitable trained data set is adopted.

Original languageEnglish
Pages (from-to)333-342
Number of pages10
JournalYanshi Xuebao/Acta Petrologica Sinica
Issue number2
Publication statusPublished - 2018


  • Automatic classification
  • Deep learning
  • Lithology identification
  • Rock images
  • Transfer learning

ASJC Scopus subject areas

  • Geochemistry and Petrology

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