# Stratographic Division Method Based on the Improved YOLOv8
## 4 Discussion  
Since there are no other similar methods for comparison, this study verifies the validity of the method by comparing the predictive performance of the present model with its variants.  
The improvement of the YOLOv8x-CAMDP model concentrates on solving two core problems: first, the CA attention mechanism is added to the three effective feature layers outputted from the backbone feature extraction network, whose excellent performance in capturing spatial information and retaining the exact location effectively solves the problem of the difficult division of boundary similarity; second, the original CIOU loss function  
Figure 9: Comparison of division results between the YOLOv8x-CAMDP model and YOLOv8x model for stratigraphic intervals {4,5,6,7,8}: W4, W5, W6 are the names of the three wells used for the test. (a) the manual division results (the stratigraphic division label) and the predicted stratigraphic division results computed by using (b) the YOLOv8x model and (c) the YOLOv8x-CAMDP model. The red dotted boxes denote that the bottom and top of adjacent prediction boxes intersect, the black dotted boxes denote that a distance exists between the bottom and top of adjacent prediction boxes, and the green dotted boxes denote the error between the model-divided and manually divided boundaries of stratigraphic intervals.  
is replaced with the MDPIOU loss function, which integrates the essence of multiple loss functions and can measure the similarity of the bounding box more comprehensively, thus significantly improving the accuracy and efficiency of the bounding box regression, and making the delineation of the stratigraphic boundary more accurate.  
Based on the above experimental results, the effectiveness and superiority of the YOLOv8x-CAMDP model in the stratigraphic division task is confirmed, and its performance is superior to that of the YOLOv8-x model, which is more suitable to be applied in stratigraphic division research. Firstly, from the analysis of the objective evaluation metrics, mAP is the key metric to measure the performance of the target detection model. The YOLOv8x-CAMDP model is 0.9% higher than the YOLOv8x model in terms of the mAP value, and this enhancement indicates that the YOLOv8x-CAMDP model has a significant performance advantage in stratigraphic division tasks. Secondly, from the analysis of the classification results of the confusion matrix, the number of quantities corresponding to each category on the diagonal was analyzed, and the YOLOv8x-CAMDP model has about 50% more quantities compared to the YOLOv8x model, and this significant improvement reflects the improvement of the YOLOv8x-CAMDP model in terms of classification accuracy. The final analysis of the model's detection results compared with those of manual division shows that the YOLOv8x-CAMDP model is not only more accurate in defining the boundaries of each stratigraphic interval but also closer to the needs of practical work. This improvement makes the YOLOv8x-CAMDP model more reliable and effective in the practical application of stratigraphic division.  
The model was applied in project practice to stratigraphically divide all wells involved in the project with significant results: on the one hand, it significantly reduced the workload, and on the other hand, it effectively calibrated and optimized the results of manual delineation. These results not only confirm the practical value of the model but also show its prospect and potential for wider application.  
Although the YOLOv8x-CAMDP model has improved the accuracy of stratigraphic division, the model still has limitations in practical applications. In particular, the YOLOv8x-CAMDP model exhibits certain deficiencies in terms of leakage detection. The leakage detection problem may stem from several factors, including the representativeness of the model training data, the detection algorithm of the model itself, and so on.