# GeoSAE: A 3D Stratographic Modeling Method Driven by Geological Constraint
## 5 Conclusions  
Three-dimensional geological modeling is essential for advancing our understanding of the formation and evolution of subsurface structures. However, incorporating geological knowledge directly into the modeling process is challenging, often leading to discrepancies between the generated 3D geological models and actual geological conditions. To address this issue, this work presents a geological constraint-driven 3D geological modeling approach, GeoSAE, which effectively integrates multiple geological constraints--such as stratigraphic sequence relationships, interface consistency, attitude point orientation, and interface smoothness--into the deep learning training process. The core contributions of this work are the construction of a geological constraint loss function that incorporates stratigraphic geometric features and smoothing effects to guide the training process; the design of a stacked autoencoder (SAE) structure that enables the simultaneous prediction of multiple potential field values; and the development of a comprehensive framework for geological constraint-driven 3D geological modeling, which is validated through modeling experiments.  
The proposed method utilizes a deep learning approach with integrated geological constraints, overcoming the limitations of traditional implicit interpolation techniques. The experimental results show that GeoSAE accurately predicts stratigraphic surfaces and fits stratigraphic points with high precision. Additionally, the stratigraphic smoothness constraint effectively mitigates abrupt transitions in potential field modeling, enhancing the quality of the generated model.  
This method's key advantage lies in its ability to incorporate geological constraints directly into the modeling process, improving both model accuracy and geological relevance. Future work may focus on extending the method to model more complex geological structures, such as faults and overturned strata. Additionally, incorporating more geological constraints and expanding the dataset could further refine the framework's ability to handle complex geological challenges. Exploring more flexible network architectures and integrating multivariate geological data may also improve the model's generalization and performance.  
Conceptualization, Y.Y. and J.Z.; methodology, M.R. and H.X.; software, M.R. and W.H.; validation, Y.Y., J.Z. and M.R.; formal analysis, Y.Y.; investigation, J.Z.; resources, H.X.; data curation, W.H.; writing--original draft preparation, Y.Y. and J.Z.; writing--review and editing, W.W.; visualization, M.R. and H.X.; supervision, Y.Y. and J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hainan Marine Geological Resources and Environment Key Laboratory Open Fund (HNHYDZZYHJKF001, HNHYDZZYHJKF002); the Central Geological Survey Fund Project (DD20190304 Haikou Jiangdong New District Underground Space Development and Utilization Potential Investigation and Evaluation); and the Hainan Geological Survey Fund Project (HNDZDC-2023-2 Danyang Economic Circle Comprehensive Geological Survey, HNDZDC-2023-3 \"Haicheng Wending\" Comprehensive Geological Survey).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.