An Accurate Acoustic Gaussian Beam Migration Method without Slant Stack for Complex Irregular Surface
Bibliographic record
Abstract
Summary In recent years, the focus of seismic exploration has transfered to the area with complex topography and complicated subsurface geological formations. Based on surface dip information, an accurate acoustic Gaussian beam migration method without slant stack for complex irregular surface is proposed in this paper. Compared to traditional beam migration methods, our method obtains higher imaging precision without the following processing: (1) elevation statics; (2) phase correction; (3) approximate substitution of velocity and take-off angle between receivers and the beam centers. We test our method by using synthetic datasets from the 2D Canadian Foothills model and Zhongyuan oilfield fault model, and results from these implementations are compared with those generated by traditional migration methods, from which we can learn that the imaging effect of our method is superior to those for traditional methods on near-surface, high-steep and overturned structures, and our method has potentially yielded a higher resolution and S/N profile.
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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".