Converted-wave receiver statics in the tau-p domain
Bibliographic record
Abstract
Removing near-surface effects in the processing of 3C data is key to exploiting the information provided by converted-waves. Receiver-side corrections are more challenging than source-side corrections due to the low velocities of S-waves in the near-surface. Analysis of PS-traveltimes in space-time domain shows that near-surface effects have a non-stationary expression in the data. This effect is enhanced when the near-surface is structurally complex. Modeling results show that transforming the data to the τ-p domain moves the problem to a stationary state. Field data from a fairly complex geological setting are processed to assess the benefits of addressing near-surface effects in the τ-p domain. For this purpose, converted-wave data are sorted into receiver gathers and a τ-p transform is applied. Then, crosscorrelation and convolution operations are performed to capture and subtract the near-surface effects from the data. Results show that this processing improves coherency and stacking power of shallow and deep events simultaneously. Shallow events benefited most from this processing due to their wider range of reflection angles. Presentation Date: Wednesday, October 19, 2016 Start Time: 1:55:00 PM Location: 166 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".