A hybrid approach to compute seismic travel times in three‐dimensional tetrahedral meshes
Bibliographic record
Abstract
ABSTRACT We propose an optimized method to compute travel times for seismic inversion problems. It is a hybrid method combining several approaches to deal with travel time computation accuracy in unstructured meshes based on tetrahedral elementary cells. As in the linear travel time interpolation method, the proposed approach computes travel times using seismic ray paths. The method operates in two sequential steps: At a first stage, travel times are computed for all nodes of the mesh using a modified version of the shortest path method. The difference with the standard version is that additional secondary nodes (called tertiary nodes) are added temporarily around seismic sources in order to improve accuracy with a reasonable increase in computational cost. During the second step, the steepest travel time gradient method is used to trace back ray paths for each source–receiver pair. Travel times at each receiver are then recomputed using slowness values at the intersection points between the ray path and the traversed cells. A number of numerical tests with an array of different velocity models, mesh resolutions and mesh topologies have been carried out. These tests showed that an average relative error in the order of 0.1% can be achieved at a computational cost that is suitable for travel time inversion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".