Seismic interpolation via multi-scale HU-Net

Abstract

The recovery of the seismic data with missing traces is essential for seismic interpretation and reservoir characterization. The deep learning (DL) based models have shown the promising results for the challenging task of seismic data interpolation. However, interpolating the large gap missing traces is a big challenge for seismic interpolation. The holistically-nested edge detection (HED) model can learn the multi-scale and multi-level features for the holistic image training and prediction. The U-Net model has a large number of feature channels in the upsampling part, which allow it propagate the feature maps to the higher resolution layers. Utilizing the advantages of these two state-of-art DL models, we propose a novel convolution neural network (CNN) based model for seismic interpolation, named the HU-Net model, by integrating the holistically-nested module and the U-net model. To formalize this, we add five activation layers followed by the U-Net model to replace the origin sigmoid layer. We then use five side branches to connect the decoder with these activation layers and output multi-scale features. Thus, our proposed HU-Net cannot only recognize the objects in big images, but also reconstruct the multi-scale features. We investigate the ability of our proposed HU-Net for interpolating the missing seismic traces with big gaps and reconstruct the fining sequence texture simultaneously. The synthetic and field data examples illustrate the effectiveness and robustness of our proposed HU-Net model. The detailed comparisons also demonstrate the superiority of our proposed model over the conventional methods.