Seismic Fault Delineation and Parameter Interpretation Using Dual U-Shaped Domain Fusion-Net

Seismic fault delineation and parameter prediction, including fault dip and strike, are vital for subsurface structure modeling, hazard assessment, and resource exploration. Although numerous deep learning (DL)-based methods have been developed for fault interpretation, there are few methods designed for fault parameter interpretation. The main reason is that extracting accurate fault parameter labels from the fault location label is a difficult task, especially for conjugate faults and dense fault systems. To address this issue, we propose a workflow for automatically generating 3-D synthetic seismic datasets with fault location, dip, and strike labels by using seismic reflection features from field data to enhance the realism of synthetic data. Afterward, we build the dual U-shaped domain fusion-net (DUDF-Net), which is embedded with a multiscale feature fusion block that integrates Haar wavelet transform and 3-D axial convolution to effectively fuse voxel and frequency-domain features, overcoming the limitations of spatial-domain-only approaches. Finally, we train the DUDF-Net using the generated synthetic dataset and apply the well-trained model to the blind test synthetic dataset and two field seismic surveys. Both quantitative and qualitative comparisons between our method and baseline models illustrate that the DUDF-Net generates more accurate and robust results for fault delineation and parameter interpretation, especially for conjugate faults and dense fault systems.