GeoSAE: A 3D Stratigraphic Modeling Method Driven by Geological Constraint

Deep learning outperforms traditional interpolation methods in 3D geological modeling due to its ability to model nonlinear relationships and its flexibility in incorporating diverse geological data. However, acquiring geological data for practical applications is challenging, and the quality of the data can vary significantly, which limits the effectiveness of purely data-driven deep learning models in 3D geological modeling. To address this challenge, this paper introduces GeoSAE, a geoconstraint-driven 3D geological modeling method. GeoSAE improves potential field prediction by employing a stacked autoencoder network (SAE) and incorporating geological constraints as a loss function during model training. This approach generates a geologically consistent, smooth, and continuous 3D stratigraphic model. To validate the method, this study applies it to a 60-square-kilometer region in Jiangdong new district, Haikou city, China. Stratigraphic interface points were utilized to predict the 3D potential field, with PyVista (version 0.44.2) enabling the accurate extraction of stratigraphic interfaces. Model quality was evaluated through comprehensive assessments of loss function analysis, data fitting, and the verification of stratigraphic smoothness constraints. Results indicate that the stratigraphic model generated by GeoSAE closely aligns with the actual data, accurately capturing stratigraphic geometry. Additionally, incorporating smoothness constraints enhances model smoothness, minimizes irregular stratigraphic fluctuations, and produces a more natural and continuous stratigraphic morphology.