# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 6 Conclusions  
This paper establishes the theoretical framework for deep neural networks and introduces a geological modeling approach founded on supervised deep neural networks. Our research objectives are segmented into three distinct submodels, each dedicated to one of the following attributes: geological age, geological genesis, and geotechnical type. The network architecture is individually crafted, with specific loss functions tailored to the unique characteristics of each task. Through algorithmic refinement and extensive experimentation, a set of hyperparameters that yield exceptional performance is identified. By training on standardized sample data, a highly adaptable deep neural network model is forged, facilitating the creation of a comprehensive three-dimensional geological model. The methodology demonstrates certain advantages and limitations in its ability to forecast unknown geological data:  
(1) Accurate shallow data classification: The network excels at precisely classifying the geotechnical types of shallow locations.  
(2) Robust prediction for continuous geological layers: In areas with extensive, uninterrupted geological bodies, the deep neural network effectively predicts the geotechnical type.  
(3) Challenges in predicting thin interlayers: The network encounters difficulties in predicting the geotechnical types of thin interlayers (e.g., sand and ancient soil) due to their limited thickness, often resulting in their omission.  
(4) Elevation-associated prediction errors: The deep neural network's prediction error rate increases as the elevation decreases. This suggests that data density strongly impacts the network's predictive performance, and enhancing the quantity of sample data is a direct approach to improving predictions.  
(5) Prediction errors in \"thin layers\": The network occasionally produces prediction errors for \"thin layers\". These errors occur because the network predicts geotechnical types in a coded format, and interpretations are based on the proximity of predicted values to specific codes. When predictions fall between two codes, interpretations lean toward one geotechnical type, leading to bias. To mitigate these errors, increasing the sampling interval in the depth direction can be considered, albeit at the cost of some accuracy.  
The results presented in this paper demonstrate that the implicit 3D modeling method, underpinned by rigorous data validation, signifies a noteworthy advancement in geological modeling. This approach shifts the modeling task away from empirical and subjective foundations toward a more mathematically rigorous and objective process. In comparison to conventional modeling methods, this approach showcases superior generalizability, convenience, and scalability. It underscores the feasibility and substantial potential of harnessing machine learning and data mining techniques to address geological challenges.  
Figure 6: Three-dimensional geological model of the study area. (**a**) Depth-direction sampling accuracy of 5 m. (**b**) Depth-direction sampling accuracy of 1 m.  
**Author Contributions:** Conceptualization, L.L. and T.L.; methodology, L.L.; software, L.L.; validation, L.L., T.L. and C.M.; formal analysis, L.L.; investigation, L.L.; resources, L.L.; data curation, L.L.; writing--original draft preparation, L.L.; writing--review and editing, L.L. and C.M.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is supported by the New Basic Surveying and Mapping Pilot of Xi'an, China (XCZX2021-M017).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data, models, and codes presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.