## 3 Results
### Model Quality Assessment  
In order to conduct a rigorous and comprehensive assessment of the quality of the 3D geological structure model for Wuhan's MDA, this study adopted a stratified sampling approach. Specifically, 10% of the boreholes were proportionally selected from across the study region, taking into account the distribution density and heterogeneity of the modeling data. These boreholes served as validation points for assessing the model's accuracy.  
For each validation borehole, a corresponding virtual borehole was extracted from the same location within the 3D geological model. A depth-matched stratigraphic correlation was then performed between the virtual and validation boreholes, enabling a direct com  
Figure 12: Cross-sections of the 3D geological structure model of Wuhan’s MDA (vertical magnification 15\(\times\)).  
Figure 13: Three-dimensional spatial spreading characteristics of the faults in Wuhan’s MDA (vertical magnification 25\(\times\)).  
parison of their geological layers. This process was repeated for all validation boreholes, ensuring a comprehensive evaluation of the model's structural fidelity.  
Finally, combining the layer-matching results of all validation boreholes throughout the study area, the comprehensive model accuracy of the 3D geological structure model of Wuhan's MDA was determined to be 87.6%, as shown in Table 3. These validation results underscore the model's robust capability to meticulously portray the 3D spatial geological structure in the study area.  
Although 3D geological modeling is a quantitative geological analysis method, due to the scarcity of modeling data and the complexity of geological conditions, there is inevitably a varying degree of qualitative expert experience involved in the modeling process. On the one hand, this can supplement the constraint information for modeling; on the other hand, it can also verify the quality of the model.  
In this study, due to the extremely complex geological background conditions in the study area, combined with the relative scarcity and highly uneven distribution of modeling data, there is insufficient geological data available in the peri-urban and the northern hilly regions to constrain the spatial morphology of the stratigraphic structure in this region (Figure 1c). Consequently, expert experience must be incorporated as supplementary information to aid in the modeling process.  
Thus, the key to effectively utilizing expert experience in modeling lies in extracting valid geological information while eliminating interfering information. In this research, we primarily employed the creation of geological profiles and virtual drilling techniques to transform expert knowledge and experience into identifiable and analyzable information. This transformed information was subsequently cross-checked with actual drilling data and geological profiles to sieve out accurate information for incorporation into the modeling process.  
Nonetheless, there still exists a certain degree of uncertainty based on expert knowledge and experience, leading to a relatively low quality of modeling. As shown in Table 3, regardless of whether it is the Quaternary strata or the bedrock strata, the model quality in the peri-urban and the northern hilly regions, where expert experience is used as crucial modeling information, is generally low, typically ranging between 70% and 80%. Among the locations, the model accuracy in the northern hilly areas is the lowest, at only 67.3%.