Applications of Deep Learning in Mineral Discrimination: A Case Study of Quartz, Biotite and K-Feldspar from Granite

Abstract

Mineral identification and discrimination play a significant role in geological study. Intelligent mineral discrimination based on deep learning has the advantages of automation, low cost, less time consuming and low error rate. In this article, characteristics of quartz, biotite and K-feldspar from granite thin sections under cross-polarized light were studied for mineral images intelligent classification by Inception-v3 deep learning convolutional neural network (CNN), and transfer learning method. Dynamic images from multi-angles were employed to enhance the accuracy and reproducibility in the process of mineral discrimination. Test results show that the average discrimination accuracies of quartz, biotite and K-feldspar are 100.00%, 96.88% and 90.63%. Results of this study prove the feasibility and reliability of the application of convolution neural network in mineral images classification. This study could have a significant impact in explorations of complicated mineral intelligent discrimination using deep learning methods and it will provide a new perspective for the development of more professional and practical mineral intelligent discrimination tools.

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