2024/03/20 by Ben Lasscock, Lasscock, Ben, Altay Sansal +3
Earth and Planetary Sciences · #3D Surveying and Cultural Heritage #FOS: Physical sciences #Geological Modeling and Analysis #Geophysics (physics.geo-ph) #Seismic Imaging and Inversion Techniques
paper · pdf · doi:10.48550/arxiv.2403.13593
openalex publication_date 2024/03/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.