Seismic fault identification of deep fault-karst carbonate reservoir using transfer learning

Abstract

Seismic fault identification is a critical step in structural interpretation, reservoir characterization, and well-drilling planning. However, fault identification in deep fault-karst carbonate formations is particularly challenging due to their deep burial depth and the complex effects of dissolution. Traditional manual interpretation methods are often labor intensive and prone to high uncertainty due to their subjective nature. To address these limitations, this study proposes a transfer learning–based strategy for fault identification in deep fault-karst carbonate formations. The proposed methodology began with the generation of a large volume of synthetic seismic samples based on statistical fault distribution patterns observed in the study area. These synthetic samples were used to pretrain an improved U-Net network architecture, enhanced with an attention mechanism, to create a robust pretrained model. Subsequently, real-world fault labels were manually annotated based on verified fault interpretations and integrated into the training dataset. This combination of synthetic and real-world data was used to fine-tune the pretrained model, significantly improving its fault interpretation accuracy. The experimental results demonstrate that the integration of synthetic and real-world samples effectively enhances the quality of the training dataset. Furthermore, the proposed transfer learning strategy significantly improves fault recognition accuracy. By replacing the traditional weighted cross-entropy loss function with the Dice loss function, the model successfully addresses the issue of extreme class imbalance between positive and negative samples. Practical applications confirm that the proposed transfer learning strategy can accurately identify fault structures in deep fault-karst carbonate formations, providing a novel and effective technical approach for fault interpretation in such complex geological settings.