Robust refraction statics solution and near-surface velocity model building using feedback from reflection data
Bibliographic record
Abstract
ABSTRACT An accurate near-surface velocity model is critical for weathering statics correction and initial model building for depth migration and full-waveform inversion. However, near-surface models from refraction inversion often suffer from errors in refraction data, insufficient sampling, and over-simplified assumptions used in refraction algorithms. Errors in refraction data can be caused by picking errors resulting from surface noise, attenuation, and dispersion of the first-arrival energy with offset. These errors are partially compensated later in the data flow by reflection residual statics. Therefore, surface-consistent residual statics contain information that can be used to improve the near-surface velocity model. We have developed a new dataflow to automatically include median and long-wavelength components of surface-consistent reflection residual statics. This technique can work with any model-based refraction solution, including grid-based tomography methods and layer-based methods. We modify the cost function of the refraction inversion by adding model and data weights computed from the smoothed surface-consistent residual statics. By using an iterative inversion, these weights allow us to update the near-surface velocity model and to reject first-arrival picks that do not fit the updated model. In this nonlinear optimization workflow, the refraction model is derived from maximizing the coherence of the reflection energy and minimizing the misfit between model arrival times and the recorded first-arrival times. This approach can alleviate inherent limitations in shallow refraction data by using coherent reflection data.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".