Can Deep-Learning Compensate the Sparse Shots in Imaging Domain? A Potential Alternative for Reducing the Acquisition-Cost of Seismic Data

Dense shots can improve the fold of subsurface imaging points, which is essential for the resolution of imaging results. However, dense shots significantly increase the cost of data acquisition, which is one of the major bottlenecks faced by seismic exploration. To address this issue, we speculate whether it is possible to construct an effective method to optimize the image made by stacking sparse shots and then generate an imaging result similar to the image made by stacking dense shots. In other words, we explore the possibility of using an optimization method to replace the dense shots in migration imaging, which is likely to reduce the acquisition cost of seismic data. Deep-learning can establish a non-linear and complex mapping relationship by using data-driven strategies. Inspired by this, we use the convolutional neural network to establish a novel mapping relationship from the sparse-shot image to the dense-shot image by constructing a suitable training dataset and designing a self-guided attention network architecture. We refer to this mapping relationship as shot compensation. We use the 2D Sigsbee2b model and the 3D SEAM (SEG Advanced modeling) model to demonstrate the potential application of shot compensation in reducing the acquisition cost of seismic data. Moreover, a real 2D marine seismic dataset is used to evaluate the effectiveness of shot compensation. Experimental results on both synthetic and real data show that this proposed shot compensation method can improve the quality of sparse-shot images and that the improved imaging results are similar to their corresponding dense-shot images.