A Geologically Informed and Data-Driven AI Workflow for Fully Seismic Stratigraphic Interpretation of Sedimentary Basin

Seismic stratigraphic interpretation of clinothems is essential for regional basin stratigraphy analysis, hydrocarbon reservoir evaluation, and basin evolutionary history reconstruction. Traditional interpretation process remains a tedious, labor-intensive, expert-dependent, and highly subjective process, often resulting in high uncertainty and leaving large portions of seismic data uninterpreted. Data-driven AI approaches are promising to automate this process, but still face significant challenges, including the absence of training dataset, poor generalizability, weak interpretability, and physical inconsistency. To address these challenges, we develop a data-driven and geologically-informed AI workflow that integrates geological knowledge into both labeled dataset construction and AI model training. For the labeled dataset construction, we develop a geological and geophysical forward modeling workflow to simulate a massive-scale labeled synthetic dataset, encompassing diverse geological models. For the model training, we incorporate both labeled synthetic and unlabeled field datasets to jointly train a network utilizing a hybrid loss with labeled supervision and geologically-informed constraints, enabling high-precision, high-resolution, strong-robustness, and high-generalizability interpretation of all stratigraphic features within seismic data. This workflow significantly accelerates and simplifies the traditional interpretation process, achieving seismic pixel-level precision and providing detailed geological insights of the origin and architecture of sedimentary basins. Additionally, our workflow lays the groundwork for constructing a refined and quantitative sequence stratigraphic framework and can be easily extended to various geological scenarios, such as depositional system tract interpretation, shoreline or shelf-edge trajectory tracking, stratigraphic completeness analysis, sea-level curve prediction, and sedimentary evolutionary history reconstruction.