Classification of plutonic rock types using thin section images with deep transfer learning

Abstract

Classification of rocks is one of the basic parts of geological research and is a difficult task due to the heterogeneous properties of rocks. This process is time consuming and requires sufficiently knowledgeable and experienced specialists in the field of petrography. This paper has a novelty in classifying plutonic rock types for the first time using thin section images; and proposes an approach that uses the deep learning method for automatic classification of 12 types of plutonic rocks. Convolutional neural network based DenseNet121, which is one of the deep learning architectures, is used to extract the features from thin section images of rocks; and the classification process is carried out with a single layer fully connected neural network. The deep learning model is trained and tested on 2400 images. AUC, accuracy, precision, recall and f1-score are used as performance measure. The proposed approach classifies plutonic rock images on the test set with an average accuracy of 97.52% and a maximum of 98.19%. Thus, the applied deep transfer learning is promising in geosciences and can be used to identify rock types quickly and accurately.