Effect of random noises and inaccurate reflection angle estimation on the amplitude of 3D RTM angle gathers: A numerical study
Bibliographic record
Abstract
The amplitude of the true-amplitude RTM angle gather provides an estimate of the angle-dependent reflection coefficient. In other words, for RTM angle gathers, the peak amplitude on each reflector is proportional to the angle-dependent reflection coefficient at the specular incidence angle. However, the amplitude of the RTM angle gather is also affected by other factors such as different imaging conditions, complex overburden velocity, under-sampling artifacts, random noise, reflection angle estimation methods, source/receiver ghosts, transmission losses, attenuation, etc. In this paper, we first use 3D angle-domain correlation-type imaging conditions to generate 3D true-amplitude RTM azimuth-sectored angle gathers by using a small shot spacing and show the corresponding specular hitcount number for each angle bin. Then, by adding very strong Gaussian noise to the shot gather, we demonstrate that the Huygens summation process in the receiver wavefield backward propagation attenuates most of the random noiseThe SNR in the true-amplitude RTM shot image is lower in the deeper part of the image. The obtained angle gather has a much higher SNR than the shot image, due to the small shot spacing. By perturbing the reflection angle estimation, we show that the amplitude of near angle traces is more sensitive to errors in the reflection angle calculation. Presentation Date: Monday, October 17, 2016 Start Time: 4:35:00 PM Location: 174 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".