Effect of random noises and inaccurate reflection angle estimation on the amplitude of 3D RTM angle gathers: A numerical study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it