Seismic Reflector Dip Integrated Spatial–Spectral UFormer for Fault Detection

Seismic fault detection is one of the key steps for seismic structure interpretation, potential reservoir prediction, and geological hazard forecasting. Currently, the most prevalent methods involve training deep learning (DL) models to accelerate seismic fault detection. Although many DL-based fault detection methods have been proposed, most rely solely on seismic amplitude to train convolutional neural networks (CNNs). Consequently, they often struggle to accurately interpret faults in regions with low data quality or complicated structures. We propose the Dip integrated Spatial-Spectral UFormer (DSSUF) with the Auxiliary Semi-DSSUF Module (ASM) to improve the fault detection performance by integrating seismic reflector dip as an extra input. The proposed DSSUF is embedded with the Spatial-Spectral Augmentation Transformer (SSAT) block, the pixel shuffle and unshuffle, and a Dual-Dimension Reduction Module (DRM). By integrating DSSUF with ASM, our model can effectively extract features from seismic data and dip. The proposed method fuses features extracted from two input data by defining the hybrid loss function containing a supervised constraint and a feature constraint. We then evaluate the proposed DSSUF with ASM by applying it to synthetic blind test data and two 3-D field data. Quantitative and qualitative comparisons between baseline methods and our method indicate that the DSSUF with ASM provides accurate and continuous fault detection results. Applications on two field data also illustrate the excellent generalization performance of the proposed method.