Automated classification analysis of geological structures based on images data and deep learning model

Ye Zhang, Gang Wang, Mingchao Li, Shuai Han

Research output: Journal article publicationJournal articleAcademic researchpeer-review

31 Citations (Scopus)


It is meaningful to study the geological structures exposed on the Earth's surface, which is paramount to engineering design and construction. In this research, we used 2206 images with 12 labels to identify geological structures based on the Inception-v3 model. Grayscale and color images were adopted in the model. A convolutional neural network (CNN) model was also built in this research. Meanwhile, K nearest neighbors (KNN), artificial neural network (ANN) and extreme gradient boosting (XGBoost) were applied in geological structures classification based on features extracted by the Open Source Computer Vision Library (OpenCV). Finally, the performances of the five methods were compared and the results indicated that KNN, ANN, and XGBoost had a poor performance, with the accuracy of less than 40.0%. CNN was overfitting. The model trained using transfer learning had a significant effect on a small dataset of geological structure images; and the top-1 and top-3 accuracy of the model reached 83.3% and 90.0%, respectively. This shows that texture is the key feature in this research. Transfer learning based on a deep learning model can extract features of small geological structure data effectively, and it is robust in geological structure image classification.

Original languageEnglish
Article number2493
JournalApplied Sciences (Switzerland)
Issue number12
Publication statusPublished - 4 Dec 2018


  • Convolutional neural networks
  • Geological structure images
  • Inception-v3
  • Machine learning
  • OpenCV
  • Transfer learning

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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