Accurate acoustic and elastic beam migration without slant stack for complex topography
Bibliographic record
Abstract
Recent trends in seismic exploration have led to the collection of more surveys, often with multi-component recording, in onshore settings where both topography and subsurface targets are complex, leading to challenges for processing methods. Gaussian beam migration (GBM) is an alternative to single-arrival Kirchhoff migration, although there are some issues resulting in unsatisfactory GBM images. For example, static correction will give rise to the distortion of wavefields when near-surface elevation and velocity vary rapidly. Moreover, Green’s function compensated for phase changes from the beam center to receivers is inaccurate when receivers are not placed within some neighborhood of the beam center, that is, GBM is slightly inflexible for irregular acquisition system and complex topography. As a result, the differences of both the near-surface velocity and the surface slope from the beam center to the receivers and the poor spatial sampling of the land data lead to inaccuracy and aliasing of the slant stack, respectively. In order to improve the flexibility and accuracy of GBM, we propose accurate acoustic, PP and polarity-corrected PS beam migration without slant stack for complex topography. The applications of this method to one-component synthetic data from a 2D Canadian Foothills model and a Zhongyuan oilfield fault model, one-component field data and an unseparated multi-component synthetic data demonstrate that the method is effective for structural and relatively amplitude-preserved imaging, but significantly more time-consuming.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".