Enhanced seismic data segmentation using an assembled scSE-Res-UNet deep neural network

Abstract

To enhance the accuracy and robustness of seismic data segmentation, we propose a novel deep-learning network, named scSE-Res-UNet. We select a U-Net as the backbone and augment it with residual blocks to form a Res-UNet, enabling multi-scale feature extraction while reducing edge loss between strata. Furthermore, we introduce Spatial and Channel Squeeze & Excitation (scSE) attention blocks in the decoder to selectively emphasize meaningful features and long-range dependencies in the data. The scSE blocks suppress unimportant features and noise while enhancing spatial context. We apply the proposed scSE-Res-UNet to a public F3 seismic dataset and compare performance against a CNN benchmark model. Experiments demonstrate the proposed architecture achieves superior data segmentation accuracy and robustness. The integration of residual learning and scSE attention mechanisms enhances the network's capacity to extract geologically meaningful representations from the seismic data. This leads to improved delineation of stratigraphic units with similar characters that are challenging to differentiate. The proposed scSE-Res-UNet effectively combines proven deep learning techniques to advance seismic interpretation.