Estimation of Near Surface Shear Wave Velocity Using CMP Cross-Correlation of Surface Waves (CCSW)
Bibliographic record
Abstract
One of the challenges of converted wave processing is to estimate a good near surface shear wave velocity model for static corrections. To this end, we have enlarged upon the idea of CMP Cross-Correlation of Surface Waves (CCSW Hayashi and Suzuki, 2004) to increase lateral resolution. Our approach is faster than the conventional CCSW and we believe it is more robust in the presence of variable source wavelet and noise. We cross-correlate each trace of a shot record is with a reference trace that is selected from within the shot gather based on high signal to noise ration. This step removes source effect, and converts traces to zero-phase. New midpoints that relate to the correlated traces are then calculated. We calculate the phase velocity for each CMP gather, and finally, we convert the resulting dispersion curve to a vertical shear wave velocity by an inverse procedure. Putting together all the vertical shear wave velocity profiles of all the CMP gathers, a 2D image of shear wave velocity is obtained for the data set. In this study, we invert for a 2D shear wave profile for a receiver line that was extracted from a 3D seismic survey. The S velocity model obtained from the method shows a well coherent match to the P velocity model obtained from turning wave tomography. We have used the model to compute converted wave receiver statics, and our results illustrate the potential use of this method for computing converted wave receiver static corrections.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".