# RockDNet: Deep Learning Approach for Lithology Classification
## 2 Related Work
Deep level features can be automatically extracted by the kernel of CNN compared to manual feature extraction [23]. In the field of microcomputed tomography (\(\upmu\)CT) images of rocks, CNN yielded promising results. [PERSON] et al. [24] employed a CNN for the prediction of rock physical properties in \(\upmu\)CT images involving specific surface area, average pore size, and porosity. This procedure yielded error rates below 7%. Similarly, in Karimpouli and Tahmasebi [25], a CNN based segmentation is conducted on sandstone \(\upmu\)CT images; this procedure achieved better segmentation compared to the multi-thresholding approach and yielded a 96% accuracy in testing set of images. [PERSON] et al. [26] introduced a method for enhancing the resolution of \(\upmu\)CT images of rocks and removing noise by utilizing a super resolution CNN. Their suggested CNN achieved a 70% reduction in relative error when compared to conventional approaches according to their results. In the work of [PERSON] et al. [27], a CNN model under the name of Rock Type deep CNNs (RTCNNs) is proposed; this model is developed for lithology classification using patches of images. The utilized six types of rocks, i.e., granite, conglomerate, mylonite, limestone, shale, and sandstone, were classified, and an accuracy of 97% was achieved. [PERSON] et al. [28] classified 20,000 drill core images collected from Russia into six lithologies: massive, siltstone, laminated sandstone, granite, shale, and limestone. A number of well-known CNN-based architectures were utilized for lithology classification based on the optical core images. Notably, GoogleNet achieved accuracy of 72% on new core images, while attaining 70% on ResNet [29]. In [30], image patches were unitized and classified using a pre-trained CNN model ResNetXt-50. Later, the obtained results were compared against the results of [29] utilizing ResNet-18, and Inception-v3 [31] models. Comparison results showed that their model outperformed the previous results, achieving an accuracy of 93.12% with new core images. Additionally, [PERSON] et al. [32] utilized a deep learning technique in favor of lithological patterns identification in carbonate rocks based on microtomographic images. In [PERSON] et al. [33], three lithologies were classified, i.e., conglomerate, sandstone, and shale, using various CNNs on a 1500 gray-scale images dataset, with a size of 64\(\times\)64. The obtained accuracy is reported to be 95%. [PERSON] et al. [27] employed the micro-resistivity borehole and ultrasonic logs for inputs, and a deep residual CNN is also employed for feature extraction. Later, the determination of the lithology of each sample is conducted. For blind sample testing, an average accuracy is 81.45%. In the aforementioned publications, the features are either from pore scale micro-CT images, or they are extracted from two-dimensional cross-section images or from two-dimensional image slices taken from three-dimensional stacks of micro-CT or whole core CT images. [PERSON] et al. [34] introduced a method named ConvXGB that combines both the XGBoost and CNN to provide accurate classification results. The CNN is utilized as a trainable feature extractor, while XGBoost is employed for labels prediction. [PERSON] et al. [35] introduced a two-layer XGBoost model using ZY1-02D hyperspectral images for lithology classification. The reported results showed good accuracy and adaptability to lim ited datasets compared to traditional methods through field validation. The XGBoost model is also reported to be effective in lithology prediction in both studies [34; 35]. Other works [36; 37] employed the extraction of thin-section image features of rocks for the purpose of lithology classification and good results were also reported.