# RockDNet: Deep Learning Approach for Lithology Classification
###### Abstract  
Analyzing rock and underground layers is known as drill core lithology. The extracted core sample helps not only in exploring the core properties but also reveals the lithology of the entire surrounding area. Automating rock identification from drill cuttings is a key element for efficient reservoir characterization, replacing the current subjective and time-consuming manual process. The recent advancements in computer hardware and deep learning technology have enabled the automatic classification of various applications, and lithology is not an exception. This work aims to design an automated method for rock image classification using deep learning technologies. A novel CNN (Convolution Neural Network) is proposed for lithology classification in addition to thorough comparison with benchmark CNN models. The proposed CNN model has the advantageous of having very low complexity while maintaining high accuracy. Experimental results on rock mages taken from the \"digitalroksportal\" database demonstrate the ability of the proposed method to classify three classes, carbonate, sandstone and shale rocks, with high accuracy, and comparisons with related work demonstrated the efficiency of the proposed model, with more than 98% saving in parameters.  
drill core lithology; CNN; deep learning; transfer learning; lightweight CNN +
Footnote †: journal: Medical  
Recently, the rapid development of computer hardware and artificial intelligence (AI) technology has paved the way for automatic classification of different applications, and lithology is not an exception [1; 7]. Incorporation of artificial intelligence methodologies into the oil and gas sector has emerged as a recent development, presenting robust approaches for the efficient management of complex, multi-dimensional data systems. This integration has the potential to mitigate human bias and enhance the intelligence of operations throughout the entire oil and gas value chain. Despite the widespread use of deep learning and CNN models, the utilization of these methods for digital rock analysis, particularly in the context of lithology classification of micro-CT images, remains an ongoing area of development [8; 9]. Hence, this study aims to demonstrate the practical implementation of deep learning techniques for the purpose of identifying lithological characteristics for rock samples through the analysis of such images.  
The novelty inherent in our approach lies in its capacity to employ a novel CNN to recognize the complex connections between three-dimensional features extracted through convolution and the lithology classes derived by experts. Accordingly, we extend methods designed for two-dimensional imagery to construct a workflow that directly utilizes high-resolution three-dimensional CT images to be employed for lithology classification. This paper progresses in the following manner: the following section provides an overview of the work conducted in this regard. Section 3 presents the proposed method along with other performance evaluation metrics. In Section 4, results are listed and discussed in addition to a comparison with related work. Finally, Section 5 concludes this paper.