Velocity-Independent Layer Stripping of PP and PS Reflection Traveltimes
Bibliographic record
Abstract
We adapt the so-called ``PP+PS=SS'' method to devise an exact technique for constructing the interval traveltime-offset function in a target zone beneath a horizontally layered overburden. The algorithm is designed for arbitrarily anisotropic target layers, but the overburden is assumed to have a horizontal symmetry plane (i.e., up-down symmetry). Important advantages of this layer-stripping technique compared to the existing Dix-type equations include the ability to handle the asymmetric moveout of mode-converted waves and laterally heterogeneous target layers with multiple curved reflectors. Also, our method is entirely data-driven and does not require knowledge of the velocity field anywhere in the medium. The computed interval moveouts of PP- and PS-waves can be used to estimate the interval parameters of transversely isotropic layers with a tilted symmetry axis (TTI), which is essential for accurate imaging in fold-and-thrust belts (e.g., the Canadian Foothills) and near flanks of salt domes. Other applications include dip-moveout inversion for VTI media and stable computation of interval long-spread (nonhyperbolic) moveout for purposes of anisotropic velocity analysis.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".