# Stratographic Division Method Based on the Improved YOLOv8
## 1 Introduction  
Stratographic division and correlation are the most important basic work for exploration and development [1]. In order to understand the geology of an entire area, each isolated borehole section is stratified, compared, and divided into the same or comparable strata, and each section is connected, so as to realize the distribution characteristics and change rules of sedimentary strata in the longitudinal and horizontal planes and to carry out the analysis of oil and gas generation and distribution [2; 3]. With the increasing degree of oilfield development, research precision is reaching higher and higher levels, and consequently, the workload and difficulty of the correlation work are becoming bigger and bigger. Tens of thousands of times of stratigraphic correlations lead to huge consumption of human resources [4]. Traditional stratigraphic automatic division methods primarily rely on signal processing techniques, analyzing well logging curves to extract cyclicity and interface information for automatic strata identification and division [5; 6]. In recent years, the application ofartificial intelligence and big data technology in the field of geology has become a new driving force for industry development. This includes new possibilities for stratigraphic division in oil field exploration and development [7]. [PERSON] et al. [8] developed a 3D river automatic recognition method based on improved codec convolutional neural networks. [PERSON] et al. [9] obtained a large number of 3D seismic sample data by using forward modeling technology. By optimizing and improving the 3D convolutional neural network model, the intelligent recognition effect of seismic breakpoints is obvious. [PERSON] et al. [10] proposed that based on dense well network logging data, and using spontaneous potential log and microelectrode log among three well logging curves, deep learning-based automatic stratigraphic division and comparison were achieved. [PERSON] [11] used a combination of machine learning and a cyclical sliding window technique to achieve intelligent subdivision of minor strata, demonstrating high precision and strong adaptability in the Gu Dong Qi Zone. [PERSON] et al. [12] combined convolutional neural networks and wavelet transform techniques to effectively predict seismic reservoir types. [PERSON] et al. [13] used principal component analysis, Hilbert-Huang transform, and other methods to effectively extract the rich geological information hidden in the depth and frequency domains of logging signals and finally used the extracted information to divide the stratigraphic sequence. [PERSON] et al. [14] proposed a well comparison framework model consisting of convolutional neural networks and error-correction algorithms, which was initially applied in the Shengli oil field and achieved favorable results. [PERSON] et al. [15] proposed a multi-task learning method for intelligent automatic stratigraphic correlation based on the SegNet architecture, which combines two types of loss functions in the network structure to create a more robust model, applied specifically to the Guantao formation in a part of the Shengli field. [PERSON] et al. [16] considered the multi-scale features of well logging curves and utilized a Feature Pyramid Network (FPN) model, proposing an edge-guided FPN (EFPN) model for automatic stratigraphic correlation, which outperformed the SegNet model in the stratigraphic correlation of logging data from the Daqing oil field, particularly at sequence interfaces and well logging curve boundaries. In the aforementioned methods, the vast majority of research focuses on using machine learning models, especially convolutional neural networks in deep learning [17]. These networks, mimicking the mechanisms of biological visual systems, can automatically and effectively learn hierarchical image features, a principle that parallels the feature extraction and analysis of well logging curves in manual stratigraphic division [18].