A seismic horizon identification method based on multi-scale attention UNet++

Common horizon identification methods based on deep learning primarily focus on seismic amplitude without sufficient attention to the spatial relationship among horizons of different scales, resulting in discontinuous and even inaccurate interpretation. To address this problem,we propose a method based on the multi-scale attention UNet++, termed MR_CBAM_UNet++, which involves MultiResBlock to extract a broader spectrum of horizon scale features, CBAM to reduce the amplitude interference of non-target signals, and a UNet++. A joint loss function composed of Focal Loss and Dice Loss is utilized for network training, and the uniqueness constraint is incorporated to refine the results of horizon identification.According to its application to actual seismic data, the MR_CBAM_UNet++ model shows significantly improved capabilities compared to traditional models in suppressing non-horizon information and identifying horizons in complex subsurface conditions.A mean pixel accuracy rate of 86.19% is achieved for the test dataset,indicating more accurate horizon interpretation with better continuity. Additionally, the results of horizon identification are more geologically significant by using the uniqueness constraint.