Bibliographic record
Abstract
ABSTRACT Determining the propagation direction of waves in a wavefield is important in several seismic imaging techniques and applications, including velocity analysis, amplitude variation with angle analysis, survey design, and illumination compensation. This can be achieved using the Poynting vector method, but this method performs poorly when waves overlap, returning incorrect wave amplitude and direction. An alternative, the local slowness method, is capable of separating overlapping waves, but it suffers from low angular resolution. We have developed modifications of these two approaches that improve the ability to extract the wave amplitude propagating in different directions. The primary modification is the addition of a wavefront orientation separation step. We have evaluated the methods’ ability to separate six overlapping waves with different phases in a constant velocity model, to accurately determine scattering angles in the construction of an angle domain image gather, and to determine the propagation directions of the back-propagated receiver wavefield for one shot in a 2D slice of the SEAM model. We have determined that in these examples, the proposed methods produce results that are generally superior to those of the Poynting vector and local slowness methods.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".