A model integration approach for stratigraphic boundary delineation based on local data augmentation

Identification of stratigraphic boundaries is a fundamental task in the seismic interpretation of oil and gas reservoir locations. When employing deep learning techniques to interpret stratigraphic boundaries, insufficient training data and sample imbalances are common challenges affecting model training. In regions with intricate geological structural changes, conventional deep-learning segmentation algorithms, such as U-Net often struggle to accurately capture the features of complex local structures. To address these limitations, we propose a model integration approach that incorporates global and local uneven-type stratigraphic data augmentation to enhance the accuracy of stratigraphic boundary identification in uneven-type regions. To address the problems of class imbalance and insufficient complex variation samples, we adopted a strategy of separately training global and local data and integrating predictions, thereby handling the disparity between uneven-type and flat-type stratigraphic data during model training. By testing the Netherlands F3 dataset with sparsely labeled profiles, it was demonstrated that the proposed method can effectively improve the delineation accuracy of stratigraphic boundaries compared to the benchmark U-Net model.