Self-Supervised Learning for Anti-Aliased Seismic Data Interpolation Using Dip Information
摘要
Aliasing introduces a significant and enduring challenge in the field of seismic data interpolation. This issue arises when the spatial sampling rate is insufficient to accurately capture the intricate spatial variations present in the underlying seismic wavefield.