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Open AccessArticle

Automated Classification Analysis of Geological Structures Based on Images Data and Deep Learning Model

1
State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300354, China
2
Chengdu Engineering Corporation Limited, PowerChina, Chengdu 610072, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2493; https://doi.org/10.3390/app8122493
Received: 1 November 2018 / Revised: 24 November 2018 / Accepted: 29 November 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Intelligent Imaging and Analysis)
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. View Full-Text
Keywords: OpenCV; machine learning; transfer learning; Inception-v3; geological structure images; convolutional neural networks OpenCV; machine learning; transfer learning; Inception-v3; geological structure images; convolutional neural networks
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Zhang, Y.; Wang, G.; Li, M.; Han, S. Automated Classification Analysis of Geological Structures Based on Images Data and Deep Learning Model. Appl. Sci. 2018, 8, 2493.

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