Receiver-side near-surface corrections in the τ-p domain: A raypath-consistent solution for converted wave processing
Bibliographic record
Abstract
ABSTRACT Removing near-surface effects in the processing of 3C data is key to exploiting the information provided by converted waves. Particularly for the case of the PS-mode, converted energy travels back to the surface as S-waves. Therefore, S-wave static corrections are needed for the receiver side. This is often done under the assumption of surface consistency. This implies a constant correction for all the traces recorded at a fixed receiver location. However, if the velocity change between the near-surface layer and the medium underneath is gradual, the vertical raypath assumption that supports the surface-consistent approach is no longer valid. This property results in a nonstationary change of the near-surface traveltimes that need to be addressed to properly solve the problem. We have determined how the delays introduced by the presence of very low S-wave velocities in the near surface can introduce raypath-dependent effects. The magnitudes of these delays can be larger than what can be considered a residual static. In this study, a raypath-consistent approach is used to solve the problem. This is achieved by transforming the data, organized into receiver gathers, to the τ-p domain and performing crosscorrelation and convolution operations to capture and remove the near-surface delays from the data. We tested this processing technique on synthetic and field data. In both cases, removing near-surface time delays in a raypath-consistent framework improved the 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. This approach can be useful in the processing of wide-angle broadband data in which the kinematics of wave propagation are not consistent with vertical raypath approximations in the near surface.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".