High-precision estimation of split PS-wave time delays and polarization directions
Bibliographic record
Abstract
ABSTRACT The measurement of split PS-wave time delays and polarization directions is notoriously difficult in field data, partly because the signals are small and overprinted by competing mechanisms. This contribution describes a new processing method that suppresses many of the overprinted traveltime and amplitude anomalies, allowing depth-averaged split PS-wave time delays and polarization directions to be measured simply and precisely. These depth-averaged properties are then inverted using a simple forward model, allowing an earth model of split PS-wave time delays and polarization azimuths to be estimated without the need for layer stripping. In the field data used as an example, the processing and inversion methods are used to estimate split PS-wave time delays and polarization directions for ten layers spanning about 500 m depth from the seabed downward. Inversions using data-error covariances estimated from prestack data show model uncertainties less than 0.3 ms of time delay and 3° of polarization azimuth. However, it is clear that if the data-error covariances cannot be estimated from prestack data, due to low fold for example, model uncertainties would rise considerably. Repeating the inversions using data-error covariances of a postulated form leads to a range of maximum-likelihood models. When the data-error covariances cannot be estimated from prestack data, it seems reasonable to report precision levels implied by the spread of maximum-likelihood models, which in this case is up to 0.5 ms of time delay and 20° of polarization azimuth. The principal achievement of this processing and inversion scheme is to constrain a relatively large number of depth layers with similar levels of model uncertainty. The depth resolution available to this new method may have important implications for the development of tight-gas and shale-gas plays, in which variations of stress, strain, and fracture properties in discrete layers are important.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".