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Data and model hard constraints to physics-informed neural networks for near-surface tomography
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 84th EAGE Annual Conference & Exhibition, Jun 2023, Volume 2023, p.1 - 5
Abstract
Summary
Traveltime (eikonal) tomography is a widely used tool for estimating the Earth’s velocity structures. Though in most situations the method provides a good initial model for subsequent velocity model building applications, it has a fundamental weakness in handling velocity reversals. To overcome this limitation, several recent works incorporate additional information from the recorded data (not only the most energetic waves) to invert for the low velocity layers. Most of these works, however, require heavy computational cost as they require either wavefield simulations or additional machine learning training prior to the inversion process. Thus, we promote a new physics-informed neural networks (PINNs) framework as a replacement to classical eikonal tomography. We enforce the model (well information) and data (traveltime picks) as hard constraints in the eikonal equation. Our preliminary results show the method performs well on 2-D complex near-surface models.
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