###### Abstract  
Identifying lithofacies types from core drilling data presents significant challenges, especially given the limited number of physical drilling characteristics available for analysis. Traditional machine learning methods often face issues with poor training and testing due to these limitations. Addressing this, we propose a new method for processing core drilling data to improve the accuracy of deep artificial neural networks (DANNs) in lithofacies recognition. Our approach transforms torque, weight on bit (WOB), and rotational speed data into three square matrices, creating a novel three-channel lithofacies image. This method allows for the application of DANNs by converting the complex lithofacies recognition task into a more standard image recognition problem. The developed method dramatically increases the input vector dimensions, enhancing the richness of the data input. The validation of results revealed that the DANN model trained for merely 3000 iterations successfully predicted lithofacies types of all eight testing samples in a mere 2.85 ms, showcasing superior accuracy. The innovative drilling data processing method proposed in this study enables DANNs to identify lithofacies with increased speed and accuracy. This offers a new direction for other DANNs utilizing drilling data. artificial neural networks; DANN; core drilling data processing; lithofacies identification; image recognition; granite and concrete samples +
Footnote †: journal: Medical  
drilling data that include low-frequency or high-frequency noise [7], or abrupt changes in drilling data due to variations in formation strength [8]. Therefore, conventional methods mostly rely on empirical formulas to identify geological formations based on core drilling data. However, the use of these formulas is decidedly inconvenient because the accuracy of empirical formulas depends entirely upon the similarities between the current geological setting and the setting under which the empirical formulas were derived. Alternatively, lithofacies types can be identified based on a manual analysis of the physical core materials produced during the drilling process [9]. However, this method is not always reliable because the results are influenced by geological expertise [10]. Hence, this is an impractical and tedious task for on-site engineers. Accordingly, these conventional methods are fundamentally inconvenient, tiresome, and expensive, which has increasingly led to the development of more advanced machine learning methods to identify lithofacies types from rock core drilling data [3]. Currently, advanced machine learning methodologies have increasingly supplanted traditional analysis approaches. These innovative techniques have been applied in the calculation of geomechanical parameters [11; 12; 13], optimization and prediction of ROP [7; 14; 15; 16], and the identification of drilling conditions [17; 18], aiding geologists in obtaining a more accurate understanding of subsurface operations. At the same time, some progress has also been made in the use of advanced machine learning for lithofacies identification. For example, [PERSON] et al. [19] demonstrated how artificial neural networks (ANNs) can be employed to identify lithofacies in subsurface studies using conventional wireline logs, providing a more consistent and objective approach than traditional methods. [PERSON] et al. [20] subsequently identified lithofacies from core drilling data using the Kohonen self-organizing map (SoM) algorithm, which was demonstrated to provide higher prediction accuracy compared to the use of backpropagation neural networks (BPNNs) and standard SoM neural networks. [PERSON] et al. [21] conducted geostratigraphic unit simulations based on a recursive neural network (RNN) to establish a stratigraphic sequence algorithm for predicting stratigraphic sequences from stratigraphic sequence models and stratigraphic thickness models as input. The work of [PERSON] et al.