## 2 Data and Methods
### 3D Geological Modeling Scheme
#### 2.3.2 Layered Modeling Scheme  
The current research on 3D geological modeling primarily centers on investigating and developing methodologies for extracting geological information from various geological datasets and effectively presenting it through advanced 3D visualization techniques. Consequently, the accuracy of a 3D geological model is inherently dependent on the quality and quantity of the modeling data. For urban 3D geological modeling, especially on a large scale, the utilization of various types of geological data is often necessary to construct high-precision models. This, however, leads to the challenge of multi-source and heterogeneous modeling data, which poses a significant limitation in 3D geological modeling research. In order to effectively utilize diverse types of data in modeling and subsequently improve modeling accuracy, this study, considering the multi-source and heterogeneous characteristics of modeling data, proposes a technical approach for layered modeling, as detailed in Figure 6.  
Modeling with Global Discrete Constrained Points for Quaternary Strata  
As shown in Figure 6B, the main idea of global discrete constrained points modeling involves utilizing the unevenly distributed discrete modeling data to perform uniform interpolation and densification based on ordinary kriging (Equation (1)), with appropriate reduction in overly dense areas, in order to obtain a global uniformly distributed constrained point set. Finally, by triangulating these points, a global modeling constraint surface for the stratum across the entire study area can be obtained. \[z(x,y)=\sum_{i=1}^{n}\lambda_{i}\cdot z(x_{i},y_{i}) \tag{1}\]  
where \(z(x,y)\) is the unknown value at location \((x,y)\), \(i\) denotes the sample point, \(n\) denotes the number of samples, \((x_{i},y_{i})\) denotes the coordinates of point \(i\), \(\lambda_{i}\) denotes the weight of point \(i\), and \(z(x_{i},y_{i})\) denotes the known value of sample point \(i\). Figure 4: Automated strata standardization scheme based on multi-scale stratigraphic sequences. Figure 5: Stratigraphic sequence and lithology of the study area. This methodology, through the application of global uniform interpolation and densification techniques, serves two primary purposes. Firstly, it provides data constraints for regions where modeling data are relatively scarce, thereby safeguarding the modeling accuracy in these areas. Secondly, the global discrete constrained points enable the generation of a regular triangular mesh, and the resultant modeling constraint surface of the stratum significantly reduces computational demands during the modeling process, leading to a substantial enhancement in modeling efficiency. In this study, the primary focus is on utilizing this methodology to construct a modeling constraint surface for the Quaternary unconsolidated sedimentary strata, as specifically illustrated in Figure 7.  
Figure 6: Layered modeling scheme; (**A**) represents a highly abstracted geological section based on the stratigraphic structural characteristics of the study area, where each color represents a stratum; (**B**) represents the scheme of modeling with global discrete constrained points for Quaternary strata; (**C**) represents the scheme of global layered modeling of bedrock. Figure 7: Construction of the constraint surface for the Quaternary unconsolidated sedimentary strata based on global discrete constrained points. Global Layered Modeling of Bedrock  
Except for the Gong'anzhai strata (K\({}_{2}\)-E\({}_{1}\)g), which lie flat within the fault subsidence zone, the dip angles of other bedrock strata in the study area are basically above 65\({}^{\circ}\). The tops of these strata have been eroded due to the uplift movement of the neotectonics, resulting in a high-angle unconformable contact with the overlying Quaternary loose sedimentary strata. Most of the strata that constitute the fold structures in the study area are very thin.