Reducing under-sampling artifacts in 3D true-amplitude RTM angle gathers
Bibliographic record
Abstract
High resolution true-amplitude RTM Angle-Domain Common Image Gathers (ADCIGs), indexed by subsurface reflection and azimuth angles, can be used for:Evaluation of complex velocity models with multiple arrivalsMore reliable automatic picking of event curvature for tomographic inversionAngle-domain NMO de-stretchAzimuthal AVA analysisAzimuthal anisotropy analysisAngle-dependent subsurface illumination compensationOptimal adaptive stackingMultiple attenuation in subsalt areaImpedance and velocity inversionScattering-angle filtering for FWIRemoval of RTM backscattered low-frequency noise. In practice, the coarsely-sampled or irregularly-sampled shot and receiver locations on the surface leads to severe under-sampling artifacts in ADCIGs with small angle binning size. These under-sampling artifacts are worse for the shallow reflectors and small reflection angle in the case of 3D data. In this paper, we first derive that the theoretical number of hitcount for each angle bin is given by the determinant of the Jacobian matrix of transforming subsurface angle to surface shot coordinates. Then we illustrate that the under-sampling artifacts are linearly proportional to the percentage deviation of actual hitcount number with respect to the theoretical hitcount number. At last, we propose using relative hitcount compensation to reduce the under-sampling artifacts for RTM 3D ADCIGs, and demonstrate its effectiveness on 3D synthetic. Presentation Date: Monday, October 17, 2016 Start Time: 4:10:00 PM Location: Lobby D/C Presentation Type: POSTER
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".