# RockDNet: Deep Learning Approach for Lithology Classification
## 2 Related Work  
Several works have utilized machine learning technology for lithology classification. A preliminary study to classify lithology based on core images was presented by [PERSON] et al. in 2011 [4], employing an object-based image analysis approach. Their study utilized grayscale core images from a single well to categorize three lithological types: carbonate-cement, shale, and sandstone. The nearest-neighbor classifier was employed for classification with an accuracy of 94%. However, their method is not applicable for large databases and not fully automatic, as it requires manual selection of images in the testing phase. [PERSON] et al. [5] presented a method for lithology identification of thin images using image analysis and supervised machine learning. The training dataset included four classes: quartzites, siltstone, claystone, and carbonate. Nonetheless, the methodology employed a shallow model that lacked the capacity to be generalized. Furthermore, it depended on high-resolution thin section images and did not undergo evaluation using core tray images. [PERSON] et al. [6] used the AdaBoost classifier to analyze volcanic lithology with an accuracy of 90% using logging parameters, such as rock fabric and pore structure. Likewise, [PERSON] [7] applied the generalized boosted regression model to lithology data using well core permeability for the purpose of lithofacies classification. The findings indicated that the GBM algorithm demonstrated a superior performance compared to the conventional regression method. Moreover, in a number of studies, support vector machines (SVMs) are exploited in rock lithology [8; 9]. As a case in point, [PERSON] et al. [10] employed a SVM for lithofacies classification based on statistical features extracted from whole core CT images. On the other hand, several works employed deep learning approaches for lithology classification. For example, [PERSON] utilized deep learning and transfer learning in a number of works [11; 12; 13] to classify images of rocks based on rock thin section and borehole image logs and CT images. [PERSON] et al. [14] introduced an automatic rock classification method using a process flow that merges core CT images, optical core photographs, conventional well logs, and routine core analysis (RCA) data. In the context of this process flow, the relevant rock-fabric features are employed to identify the rock classes by the utilization of a clustering algorithm after being extracted from all core CT images and core photographs. Firstly, several rock classes were assumed by input image. Subsequently, the optimization of the number of assumed classes involves a recursive increase in the number of classes and a minimization of a permeability-based cost function under a specific threshold. Finally, an artificial neural network is employed to predict the classes from well-log data using the obtained rock classes. [PERSON] et al. [15] have utilized two classifiers, the gradient boosting and the random forest, for the purpose of classifying petrographic of log data from the Tarakan basin. The obtained results outperformed other machine learning methods. The authors of [16] discuss computing challenges in nature-oriented civil engineering, mentioning lithology classification and geophysical disaster prevention. It highlights the computational demands of Big Data and complex algorithms, suggesting AI, data filtration, and advanced technologies to reduce computational load. Recently, the attention of research has been shifted towards deep neural networks (Deep learning) [17]. Deep learning modalities, specifically CNNs [18], have been revisited in the last ten years due to the growth of computing power, dominating the field of computer vision. Well known CNN architectures, such as AlexNet [19], VGG [20], GoogLeNet [21], and DenseNet [22], etc., have enabled researchers to enter into the realm of deep networks for problem solving within their specialized areas.