# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 4 Data Scenario  
### Resampling  
The cornerstone of 3D geological implicit modeling lies in acquiring a substantial amount of standardized geological drilling data. Raw drilling data comprise planar coordinates, elevation, and stratigraphic depth of exploration points. These data can be processed to derive the spatial coordinates of different strata at each point, along with attribute information like stratigraphic age, geotechnical type, and other descriptions. The determination of the stratigraphy to which a point belongs is typically based on its age and geotechnical type, facilitating the identification of the geological body to which it pertains. However, recognizing the continuous area between the upper and lower boundaries of stratigraphy as strata with the same attributes presents a challenge, necessitating the resampling of drilling data. The original dataset also requires procedures such as deduplication, error checking, patching, encoding, and others to transform it into a series of standardized, continuous spatial point-cloud data that include spatial coordinates (x, y, and z) and stratigraphic attributes. The resampling of drilling data involves both depth-wise resampling within the drills and planar space resampling. Planar space resampling primarily aims to standardize and normalize the input features of both the drilled sample set and the sample space to be predicted. The study area is divided into a series of grids of uniform size, and this grid size plays a vital role in determining the precision of the 3D geological model. Essentially, the grid size represents the smallest cell within the 3D representation of the geological entity. Larger grid sizes significantly reduce the total cell count in the model, providing a substantial advantage in terms of computation and convergence speed for the deep neural network. However, this may result in multiple distinct geological features coexisting within the same cell, potentially reducing model accuracy. On the other hand, a smaller grid size results in an exponential increase in deep neural network computation and convergence speed. Therefore, the mesh size must be determined in conjunction with the characteristics of the sample data and the accuracy requirements for 3D modeling. Depth-direction resampling includes the decimal place normalization of elevations for stratigraphic divisions within the original drilling data, using standardized intervals of 0.5 m. These intervals align with the drill sampling intervals commonly employed in engineering. Subsequently, each stratum was subdivided at regular intervals to attain a more uniform set of sample data. Figure 2a illustrates that the study area is located in the urban region of Xi'an City, Shaanxi Province, China. It is important to note that the 7922 drills used in this study are not uniformly distributed across the approximately 120 km\({}^{2}\) study area. The sample data used in this research are obtained from engineering construction projects, including house construction, road construction, bridge construction, and more. The minimum spacing between drills is governed by specifications, typically ranging from 10 to 30 m. After analyzing the spacing between samples, it was determined that the minimum Euclidean distance between drills is 5 m. To meet the requirements of 3D modeling accuracy and the computational capabilities of hardware equipment, the standard grid size for planar sampling was set at 5 m by 5 m. As indicated in Figure 2b, within each standard grid, the coordinates and attribute features of the drills are mapped to the geometric center of the grid, simplifying attribute assignment to the standard grid. Also, the figure shows the assignment logic for the samples. If a single mesh contains only one drill, the attributes of that drill are assigned directly to the mesh. In cases where a mesh contains multiple drill samples, the criterion for selecting the assigned drill is based on the Euclidean distance from each drill point to the geometric center of the mesh.