Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.13593 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909143709777920 |
|---|---|
| author | Lasscock, Ben Sansal, Altay Valenciano, Alejandro |
| author_facet | Lasscock, Ben Sansal, Altay Valenciano, Alejandro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_13593 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Encoding the Subsurface in 3D with Seismic Lasscock, Ben Sansal, Altay Valenciano, Alejandro Geophysics 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. |
| title | Encoding the Subsurface in 3D with Seismic |
| topic | Geophysics |
| url | https://arxiv.org/abs/2403.13593 |