Seismic Channel Characterization Based on 3D DS-TransUnet

The structure and geomorphology of channel systems play a critical role in interpreting sedimentary processes and characterizing subsurface reservoir capacity. This study presents an innovative 3D DS-TransUnet model for seismic channel interpretation. The model incorporates a multi-scale Swin Transformer architecture capable of processing 3D data in both the encoder and decoder, and integrates a feature fusion module into the skip connections to effectively combine shallow detail features with deep semantic features, thereby enhancing the detectability of weak reflection signals. This design not only enables the network to capture global dependencies but also preserves fine-grained local details, allowing for more robust feature learning under complex geological conditions. In addition, a complete synthetic data generation workflow is proposed, through which 300 pairs of high-quality synthetic data were constructed for model training. During training, the proposed model achieved a significantly faster convergence speed compared with other selected models. Experimental results on both synthetic and field seismic datasets demonstrate that the proposed method yields substantial improvements in channel boundary delineation accuracy and interference suppression, providing an efficient and reliable approach for intelligent channel recognition.