Error tolerances for ray tracing with adaptive step size control in strongly anisotropic media
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it