Identifying Rock Thin Section Based on Convolutional Neural Networks

This paper uses the convolutional neural networks to train and identify rock thin sections. There are three innovations in this method. Firstly the convolutional neural network is used in the field of geological experiment testing and it can automatically identify and classify the rock thin sections. Secondly the original image of rock thin sections are sliced and segmented, so that the convolutional neural network could learn more details of the rock mineral texture without damaging the original resolution of the image. Thirdly using other image enhancement techniques, such as random flipping and standardization, can expand the sample data set and enhance the robustness of the model. Finally the training model achieves the desired results.