Prestack migration concepts for AVO measurements on horizontal and dipping layers
Bibliographic record
Abstract
Amplitude versus offset (AVO) processing is typically based on a model of horizontal reflectors and common midpoint (CMP) gathers. Seismic prestack migration on constant offset sections has also been used to improve the signal to noise ratio (SNR) of the AVO effect on the migrated CMP gathers that are referred to as common reflection point (CRP) gathers.The use of prestack migration in AVO measurements is aided by the scatterpoint concept that assumes reflectors can be composed of many scatterpoints (or reflecting elements) that are aligned along the reflector. The energy scattered by each point reconstructs to match the specula reflected energy. The energy from each scatterpoint forms a surface in the prestack volume that is often referred to as Cheops pyramid. The location of specula energy from a scatterpoint is identified by the tangential area between the reflected specula energy and the surface of Cheops pyramid. In addition, the incident and reflection angles from a scatterpoint may be superimposed on Cheops pyramid to identify any smearing that may occur. These principles apply to both horizontal and dipping layering.Conventional constant offset prestack migration can map horizontal and dipping specula energy to corresponding CRP gathers for AVO analysis. This method, however, requires an accurate velocity model before AVO measurements can be obtained.The equivalent offset method (EOM) of prestack migration forms prestack migration gathers that are referred to as common scatterpoint (CSP) gathers with no time shifting of the input data. This process uses minimal velocity information to rapidly form CSP gathers that that are also well suited for AVO analysis. After the gathers are formed, accurate velocities can be estimated and AVO measurements made.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".