Self-Supervised Learning for Efficient Antialiasing Seismic Data Interpolation
Pengyu Yuan, Shirui Wang, Wenyi Hu, Prashanth Nadukandi, German Ocampo Botero, Xuqing Wu, Hien Van Nguyen, Jiefu Chen
Abstract
Reconstruction of seismic data is an important but challenging task in seismic data processing. Different machine-learning-based algorithms have been developed to solve this ill-posed problem and achieved great progress. However, most machine-learning-based methods rely on supervised learning where a good training dataset with many complete shot-gathers are required to train the model. Although the generative model has been used for unsupervised learning and reconstructing signals in a shot-gather, it fails to accurately resolve the fine features, especially when aliasing is the main concern. In addition, multiple shots’ interpolation problems have not been fully investigated by the unsupervised machine-learning-based approaches. In this work, we propose a self-supervised learning method using a blind-trace network and two antialiasing techniques (automatic spectrum suppression and mix-training) for seismic data reconstruction. The method is validated using challenging and realistic scenarios. Test results show that the method can be applied to single-shot or multiple shots’ cases and adapt well to different decimation patterns.