Dip-Informed Neural Network for Self-Supervised Anti-Aliasing Seismic Data Interpolation

Seismic data interpolation is a vital technology for improving seismic data density. In recent years, deep learning approaches have demonstrated significant potential in this field, yielding impressive results. Nonetheless, challenges still persist and have not been adequately addressed. First, the lack of reliable labeled training datasets induces concerns about the network's adaptiveness under supervised learning schemes. Additionally, due to inadequate spatial sampling, aliasing frequently poses considerable difficulties for deep neural networks. In this study, we tackle the issue of aliased seismic data interpolation through self-supervised learning. A novel dip-informed neural network (DINN) is introduced to explicitly integrate local dip information into the neural network and regularize the reconstruction of missing traces. To address the training challenges associated with regularly sampled seismic data interpolation under self-supervised learning schemes, a randomized mix training algorithm is developed. The experimental results along with comparisons to existing methods using both synthetic and field datasets demonstrate the effectiveness and robustness of our approach.