# Stratographic Division Method Based on the Improved YOLOv8
## 5 Conclusions  
In this paper, a stratigraphic layer segment detection algorithm, YOLOv8x-CAMDP, based on improved YOLOv8 is proposed, which applies the method of target detection to stratigraphic division and is able to detect the position corresponding to each stratum and also recognizes the category of the corresponding stratum, which provides the basis for the subsequent stratigraphic correlation, and has high efficiency in stratigraphic division and correlation. In order to improve the accuracy of the model for stratigraphic division, YOLOv8x was improved, based on which the CA attention mechanism was added to the three effective feature layers output from the backbone feature extraction network, and the CIOU loss function was replaced with the MDPIOU loss function. The effectiveness of the YOLOv8x-CAMDP model in the stratigraphic division task is jointly verified by the results of experimental analyses and project applications, demonstrating its superior performance. Compared with the YOLOv8x model, the YOLOv8x-CAMDP model is much better at accurately defining stratigraphic boundaries, and this improvement significantly enhances the usefulness and accuracy of the model, further confirming its popularization value in stratigraphic division studies and practical applications.  
Although the YOLOv8x-CAMDP model shows superior performance in stratigraphic division tasks, there are still some limitations, and the following directions of improvement can be considered in order to further enhance the model's detection capability:  
1. Data enhancement: Adding more diverse training samples, including stratigraphic data under different geological conditions can help the model to generalize better and reduce missed detections.  
Data fusion: Integration of multi-source and multi-scale seismic, logging, and core information.
* 3. Model structure optimization: Different network structures can be tried, such as introducing deeper or wider networks or exploring different attention mechanisms to improve the model's ability to capture detailed features.
* 4. Loss function adjustment: Continue to optimize the loss function, possibly by introducing new loss terms or adjusting the weights of the existing loss function to better reflect the complexity of the stratigraphic boundary.
* 5. Multi-task learning: Combining the stratigraphic division task with other related tasks (e.g., stratigraphic classification or attribute prediction) and sharing feature representations through multi-task learning may help to improve the accuracy of boundary detection.  
Conceptualization, L.T.; methodology, L.T.; writing--original draft, L.T.; data curation, T.L.; supervision, C.X.; writing--review and editing, T.L.; resources, T.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos.42172163).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
We are very grateful to [PERSON] for his outstanding contribution to the implementation of the experimental methodology ideas and [PERSON] for his meticulous proof-reading of the first draft. Meanwhile, we express our heartfelt gratitude to [PERSON], [PERSON], and [PERSON] for their hard work in the image labeling of the dataset. In addition, [PERSON]'s industry insights and valuable guidance provided great help and inspiration for our research.  
The authors declare no conflicts of interest.