  This article presents a self-supervised generative AI approach to seismic
data processing and interpretation using a Masked AutoEncoder (MAE) with a
Vision Transformer (ViT) backbone. We modified the MAE-ViT architecture to
process 3D seismic mini-cubes to analyze post-stack seismic data. The MAE model
can semantically categorize seismic features, demonstrated through t-SNE
visualization, much like large language models (LLMs) understand text. After we
fine-tune the model, its ability to interpolate seismic volumes in 3D showcases
a downstream application. The study's use of an open-source dataset from the
"Onward - Patch the Planet" competition ensures transparency and
reproducibility of the results. The findings are significant as they represent
a step towards utilizing state-of-the-art technology for seismic processing and
interpretation tasks.
