Wave Equation Travel Time Tomography for High-Resolution Near-Surface Survey
Bibliographic record
Abstract
Summary Wave Equation Traveltime Tomography (WETT) is a promising method for high-resolution near-surface seismic imaging, addressing limitations of conventional ray-based methods like First Arrival Traveltime Tomography (FATT). By leveraging the full wavefield, WETT enhances resolution and robustness, making it a practical alternative to Full Waveform Inversion (FWI) for near-surface studies. Using the GPU-enabled Deepwave framework, this study implemented WETT and applied it to a seismic survey conducted at the Teil site in France, characterized by complex geological features including a fault zone. The initial velocity model was derived from first breaks using deep learning and refined through WETT. The resulting tomogram demonstrated sharper contrasts and improved resolution of lateral velocity variations and fault zones compared to FATT. The computational requirements were manageable, with a total runtime of 2.5 hours for 10 iterations on a single GPU, highlighting the practicality of WETT for dense surveys. WETT showed fast convergence and robustness, providing a straightforward path to high-quality velocity models, which can be further refined using FWI. Future work aims to incorporate surface wave dispersion for joint P- and S-wave inversion and extend the framework to advanced solvers for elastic and viscoelastic wavefield modeling, enabling broader applicability to 2D and 3D problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".