Deformable Convolution Kernel and Residual Learning Assisted Irregular Seismic Data Interpolation

The enhancement of seismic migration and inversion processes critically depends on the precise interpolation of seismic data. In recent years, the rapid advancements in deep learning (DL) have led to the widespread adoption of convolutional neural networks (CNNs) in seismic interpolation applications. Nonetheless, the inherent limitations of traditional CNNs, due to the fixed structure of their convolution kernels, impede the extraction of high-level features of seismic data. The accuracy of CNN-based seismic data characterization and interpolation is open to improvement. Therefore, we introduce deformable convolution kernels and design a novel deformable convolution residual-U-Net (DRU-Net) for a more nuanced characterization and interpolation of seismic data. The proposed DRU-Net allows a deformable convolution kernel with adaptive shape adjustments for the receptive field by using learnable offsets to effectively extract advanced features from the training data. In conjunction with a residual learning approach, the DRU-Net significantly refines the network’s learning process and boosts interpolation precision. Through numerical experiments on synthetic and field data with irregular traces missing, the proposed DRU-Net achieves superior precision in seismic data interpolation compared with traditional U-Net and U-Net with the squeeze and excitation block (SE-block).