###### Abstract
[22] conducted lithofacies identification based on core image data processed using a deep convolutional neural network (CNN) trained with millions of core images. [PERSON] et al. [23], using a multiclass SVM framework based on well logs as predictor variables, demonstrating the superiority of multiclass SVMs over other conventional SVMs in terms of classification accuracy, improved the prediction accuracy to 84.5%. [PERSON] [3] proposed a heterogeneous ensemble method (HEM) approach for lithofacies identification, which has the capability to handle complex, nonlinear, multidimensional, and imbalanced drilling data, achieved an accuracy of 88.32% on the testing set. However, even advanced machine learning methods struggle with the difficulty of detecting significant changes in core drilling data features due to the limited dimensionality of input vectors, which usually consist of no more than 10 physical core drilling characteristics, such as the torque, drilling pressure, rotational speed, WOB, and ROP parameters discussed above, and hinder the neural network from extracting important features from the data, resulting in poor training and testing performance. Specifically, this issue causes prediction models to fall easily into local optima, and the stochastic gradient descent algorithm employed in the optimization process may even suffer from gradient disappearance, making it unable to minimize the objective function. These conditions produce high levels of both bias and variance in the trained model, which affect its accuracy and reliability. Here, a relatively high level of bias may lead to the underfitting of the model, which produces large training and testing errors, while a relatively high level of variance may lead to overfitting, which produces very small training errors and large testing errors. Hence, balancing these two features is beneficial for increasing the overall learning effectiveness of the model. Typically, deeper neural networks and more training iterations are used to reduce bias, while increasing the volume of training data and data regularization are applied to reduce variance. In addition, modifying the structure of the neural network can, at times, reduce bias and variance simultaneously. Other studies have demonstrated that the design of the optimization algorithm employed during training can have a pronounced effect on the levels of bias and variance. For example, [PERSON] et al. (2016) proposed an improved iterative bat algorithm, which is a heuristic optimization algorithm inspired by the natural behavior of bats, that applies iterative local search and random inertial weights to enhance the global exploration ability and optimization stability of the algorithm. This algorithm was updated by [PERSON] et al. (2017) in subsequent research, and the improved algorithm was demonstrated to provide enhanced global optimization performance by solving the problems of local optima and unstable optimization results. Moreover, the algorithm was applied in actual core drilling operations, and was demonstrated to increase the drilling rate by as much as 34.84%. In addition, other machine learning algorithms such as BPNNs have poor fitting capabilities when dealing with unbounded and nonlinear problems. The above-discussed issues associated with lithofacies identification based on advanced machine learning algorithms can be potentially addressed via the use of deep convolutional neural networks (DANNs), which have been applied in fields such as speech recognition, visual object recognition, object detection, drug discovery, and genomics ([PERSON] et al., 2016). Here, applying an optimal number of hidden layers and neuron units is a crucial consideration in neural network design. A deeper neural network can transform each input feature into a higher and more abstract representation through simple and nonlinear calculations, making the machine learning model more sensitive to subtle changes in data between different samples.