Horizon Picking Using Two-Branch Network with Spatial and Time-frequency Features

In seismic interpretation, horizon picking is a very essential but time-consuming and challenging task. Most existing auto-picking algorithms have been proposed to improve the horizon interpretation efficiency. Recently, deep learning approaches have shown promising performance in horizon identification. However, feeding directly seismic time series or images into a deep learning network only uses the amplitude information of seismic signal, which limits the classification accuracy. In this paper, we propose to learn more distinctive characteristics in the time-frequency domain from the continuous wavelet transform (CWT) coefficients. More importantly, we develop a novel Two-branch convolutional neural network (TB-CNN) for horizon picking: a CWT branch can mine the time-frequency features in 2D CWT coefficients of seismic time series. At the same time, a spatial branch further explores the local spatial features in seismic images. The features of the two branches are then fused to perform classification. The output is the class scores of voxels being horizon or background. Finally, we extract the horizon surface by finding all voxels with the highest score values of the horizon class in the vertical temporal direction. We conduct experiments on both synthetic and field data. The results show that the proposed method can effectively fuse the spatial features and time-frequency features to yield higher performance than the traditional 3D auto-tracking method.