Abstract
We demonstrate a machine learning model that effectively learns to predict a velocity model of the subsurface directly from trace data. We do this by first transforming the data from the shot gather domain to the tau-p domain, then predicting the time-domain velocity model using a U-Net, and finally converting from time to depth domain by integrating the predicted velocity model. This pipeline is capable of predicting a velocity model suitable for input to FWI and is capable of scaling to both the volume and complexity in real marine acquisitions.
| Original language | English |
|---|---|
| Pages (from-to) | 60-64 |
| Number of pages | 5 |
| Journal | SEG Technical Program Expanded Abstracts |
| Volume | 2024-August |
| DOIs | |
| State | Published - 1 Jan 2024 |
| Externally published | Yes |
| Event | 4th International Meeting for Applied Geoscience and Energy, IMAGE 2024 - Houston, United States Duration: 26 Aug 2024 → 29 Aug 2024 |
Keywords
- Radon transform
- acoustic
- deep learning
- inversion
- velocity model building
ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
- Geophysics
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