The limitations of time migration and trace stretch in the presence of lateral velocity gradients
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Bibliographic record
Abstract
This paper quantifies the imaging errors that result from the assumptions of time migration and a preliminary, laterally-variable "stretch" of the data, in the presence of lateral velocity gradients. The analysis is restricted to media that are approximately homogeneous in the vertical direction, with dip in the same direction as for the velocity gradient. It is shown that a time migration that explicitly recognizes the lateral variation in velocity results in underestimation of steep dips by only a few degrees, even for large gradients of 0.1 s"1. A preliminary stretch of the data, followed by time migration and stretch removal, however, results in a net overestimation of moderate dips by a few degrees and overestimation of steep dips by 15° or more. The errors increase with increasing gradient and decreasing average velocity.
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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