GNP-WGAN: Generative Nonlocal A Priori Augmented Wasserstein Generative Adversarial Networks for Seismic Data Reconstruction

Interpolation and reconstruction of seismic data are critical steps in geophysical exploration, with results largely dependent on the performance of the interpolation techniques and the available feature information in the data. The task becomes particularly challenging when faced with complex data loss scenarios, such as high proportions of random discrete missing data and large amounts of random continuous missing data. To address this challenge, we propose a new method: generative nonlocal a priori augmented Wasserstein generative adversarial network (GNP-WGAN). The method uses a non local prior extraction (NLE) module improved by an edge detection algorithm to capture the structural information of seismic data, and a generative multidimensional attention restorer (GMAR) designed based on causal and axial attention to generate a smooth and accurate generative nonlocal prior (GNP). The Wasserstein GAN with gradient penalty is then augmented with GNP for finer and more accurate seismic data reconstruction. Finally, the MS-SSIM-
$L_{1}$
loss function is introduced to improve the quality of the generator reconstruction. Experiments on synthetic and field seismic datasets demonstrate the superior performance of GNP-WGAN in reconstructing seismic data with complex missing cases. In addition, subsequent experiments show that our GNP can be easily integrated as a plug-in into most of the currently popular reconstruction models to improve the accuracy and structural integrity of the reconstruction results and also exhibits enhanced robustness.