| Title | Deep compressed learning for 3D seismic inversion |
| Publication Type | Conference Proceedings |
| Year of Conference | 2023 |
| Authors | Gelboim M, Adler A, Sun Y, Araya-Polo M |
| Conference Name | Third International Meeting for Applied Geoscience & Energy (IMAGE23) |
| Pagination | 1054 - 1058 |
| Date Published | 12/2023 |
| Abstract | We consider the problem of 3D seismic inversion from pre-stack data using a very small number of seismic sources. The proposed solution is based on a combination of compressed sensing and machine learning frameworks, known as compressed learning. The solution jointly optimizes a dimensionality reduction operator and a 3D inversion encoder-decoder implemented by a deep convolutional neural network (DCNN). Dimensionality reduction is achieved by learning a sparse binary sensing layer that selects a small subset of the available sources, then the selected data is fed to a DCNN to complete the regression task. The end-to-end learning process provides a reduction by an order-of-magnitude in the number of seismic records used during training, while preserving the 3D reconstruction quality comparable to that obtained by using the entire dataset. |
| URL | https://library.seg.org/doi/10.1190/image2023-3898594.1 |
| DOI | 10.1190/image2023-3898594.1 |
