## 1 Introduction
[11] applied four methods to make a classification of microscopic rock images automatically. The result of the nearest neighbor (NN) analysis showed high recognition level with 99.8%. [PERSON] et al. [12] also proposed a transfer learning method for sandstone identification automatically based on microscopic images. [PERSON] et al. [13] used an unsupervised machine learning method to classify rock texture images. The experimental results indicated the outstanding performance of self-taught learning. Geological structures identification has many similarities with rock classification, which indicates what features we should extract from geological structures images. The color and texture are both critical in rock images to both micro and regular images. In some cases, the rock mineral was able to be classified just by color. While the geological structures data has unique characters. The texture is addressed more by the geomorphometric analysis [14]. Deep learning is prominent in image processing. It was proposed by [PERSON] [15] and was further developed recently [16]. Because of the positive performance, it was used to analyze unstructured data in many areas, such as image classification [17] and semantic analysis [18]. In medicine, deep learning is also popular [19; 20]. However, [PERSON] et al. [21] thought the input was massive in deep learning. Deep learning was also applied in remote-sensing image scene classification [22] and terrain features recognition [23]. It was able to extract features from unstructured data and make a classification with high accuracy. The convolutional neural network (CNN) is an essential method in deep learning. Scholars were able to build different CNN models by adding different kernels, pooling layers, and fully connected layers. [PERSON] et al. [24] used different CNN architectures in detecting landforms on Mars which proved convenient to extract features from images. [PERSON] et al. [25] also used convolutional neural networks to extract features of images then built a support vector machine (SVM)linear model based on the features. [PERSON] et al. [26] also used transfer learning to predict geo-tagged field photos based on convolutional neural networks. All the results showed good performance of the convolutional neural network in extracting images features. While we should consider that the CNN model depends on a large dataset to avoid overfitting. In this research, we established a transfer learning model based on Inception-v3 for geological structures with 12 labels. The test result showed a high accuracy of the model. Then we made a comparison between the identification model and the other four models, namely K nearest neighbors (KNN), artificial neural network (ANN), extreme gradient boosting (XGBoost) and CNN. The result showed the transfer learning model had a high accuracy on a small dataset. The machine learning method's accuracy was poor because it is hard to extract accurate features of images from a pixel vector or histogram. CNN was overfitting strongly. Transfer learning based on deep learning model was an effective method for geological structure images classification. Moreover, [PERSON] et al. [27] applied the UAV and recognition model to detect rail surface defects. The retrained model in this research can also be combined with a UAV, which can be an assistant tool in the geological survey in further study.