Improved resolution in depth imaging through reflection static corrections derived from model-based moveout
Bibliographic record
Abstract
When seismic data are used to image the subsurface, assumptions and calculations are made about the near-surface to overcome the uncertainty of the velocities in the low velocity layer. A near-surface velocity model is generated to calculate a time shift that is used to correct for velocity anomalies in the near-surface for time migration. Reflection statics are calculated because often the lack of detailed near-surface information leads to inaccuracies. A normal moveout (NMO) velocity field is picked and applied to stack the data in preparation for the reflection statics calculations. The NMO equation based on the assumption that the moveout can be approximated by a hyperbola. However, the accuracy of this assumption is valid when the moveout on data is near-hyperbolic and deviates when the moveout is more complicated. A few scenarios of non-hyperbolic moveout are when the topography isn't flat, strong lateral heterogeneity of velocity is present, and when there are variations in the seismic weathering thickness and velocities. Raytracing in depth migration has overcome many of the issues with the assumptions in time migration. Foothills datasets and other geologically complex environments compel us to look for ways to overcome these assumptions as they are violated. Using the depth migration velocity model, we apply an offset dependent traveltime summation as the moveout correction for reflection static calculations in depth imaging. Presentation Date: Wednesday, September 27, 2017 Start Time: 10:10 AM Location: 370C Presentation Type: ORAL
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".