# Identification of Complicated Lithology with Machine Learning
###### Abstract
The crossplot method is an important technique in geophysical logging, primarily used for lithology identification and interpretation. This method involves using two or more logging parameters (natural gamma ray, resistivity, acoustic transit time, etc.) as the axes to plot a crossplot on a plane. This allows for a visual representation of the characteristics and differences in logging responses for different lithologies or formations [19]. Therefore, the crossplot method is one of the most commonly used methods in lithology identification. The crossplot method is effective when the lithology is relatively simple and the corresponding features of the logging curves show significant differences. However, when the lithology becomes complex and the differences in the logging curve characteristics of the rocks are not pronounced, it becomes difficult to achieve effective results. In order to deal with this issue, some scholars have proposed using statistical methods for lithology identification. This approach involves organizing and summarizing observational data, followed by classification, to overcome the limitations of the crossplot method. The mathematical and statistical method was first proposed by [PERSON] [20], and later, [PERSON] and others applied this method in practical production, thoroughly demonstrating its feasibility and effectiveness. This method was later introduced to China, where domestic scholars conducted extensive research. [PERSON] et al. [21] designed a corresponding program based on this method for lithology identification in the North China region. In 2007, [PERSON] et al. [22] developed a lithology identification model suitable for the study area using multivariate statistical methods. Practical verification has revealed that the multiple discriminant method can effectively identify lithology in most cases. However, it has significant errors when identifying lithology in transitional layers. Therefore, this method is not suitable when the geological features and lithology are complex. Based on the research progress of scholars both domestically and internationally, the mathematical and statistical method still reliesheavily on expert experience. However, with the continuous development of computer technology, artificial intelligence has become increasingly complementary to this approach. Combining mathematical and statistical methods with AI for lithology identification has also become a popular research area [23]. In 2013, [PERSON] et al. [24] compared the application effects of neural network and vector machine models and concluded that combining machine learning methods with other optimization algorithms could achieve lower computational costs and better stability. Later, [PERSON] et al. [25] further validated this by comparing the performance of support vector machines, artificial neural networks, self-organizing maps, and multi-resolution graph-based clustering algorithms in shale lithology identification. They demonstrated that the vector machine outperformed other methods in lithology classification and identification. In a global machine learning competition organized by Brendon Hall [26], participating teams used various machine learning algorithms for lithology identification. The comparison revealed that the XGBoost algorithm achieved the highest accuracy in lithology classification and identification. [PERSON] et al. [27] applied the rough set-random forest algorithm to train logging data and sample data, establishing a corresponding discriminant model. This approach was proven to have high accuracy in lithology identification. In 2018, [PERSON] et al. [28] based their work on the TensorFlow deep learning framework, integrating various techniques such as the Adagrad optimization algorithm, ReLU activation function, and Softmax regression layer into a neural network model. After training with sample data, this model demonstrated extremely high accuracy in conventional lithology identification. In 2019, [PERSON] et al. [29] developed a one-dimensional convolutional neural network model.