MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing. (arXiv:2204.03197v4 [physics.geo-ph] UPDATED)

The interpolation and reconstruction of missing traces is a crucial step in
seismic data processing, moreover it is also a highly ill-posed problem,
especially for complex cases such as high-ratio random discrete missing,
continuous missing and missing in fault-rich or salt body surveys. These
complex cases are rarely mentioned in current works. To cope with complex
missing cases, we propose Multi-Dimensional Adversarial GAN (MDA GAN), a novel
3-D GAN framework. It keeps anisotropy and spatial continuity of the data after …

3-d arxiv data gan physics