Error tolerances for ray tracing with adaptive step size control in strongly anisotropic media
Bibliographic record
Abstract
Seismic ray tracing with adaptive step size control is an efficient alternative to standard ray tracing algorithms with constant step size. We apply the Cash-Karp method to calculate ray trajectories for P-, SH- and SV-waves in a strongly anisotropic velocity model from the Horn River Basin in Canada. The accuracy of the ray trajectories is controlled by location and slowness error tolerances. We analyze their influence on global travel time errors and formulate a general recommendation for error tolerances in typical microseismic settings. The results show that decreasing slowness error tolerances lead to poorer efficiency. We therefore recommend to neglect slowness error toler-ances and perform ray tracing with adaptive step size control only with location error tolerances in the order of 10−6 m, which results in travel time errors in the order of 10−5 s. Presentation Date: Tuesday, October 16, 2018 Start Time: 1:50:00 PM Location: 205A (Anaheim Convention Center) Presentation Type: Oral
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".