Viscoacoustic VTI and TTI wave equations applied in constant-Q anisotropic reverse time migration
Bibliographic record
Abstract
We investigate the simulation of viscoacoustic wave propagation and reverse time migration (RTM) in transversely isotropic (TI), vertical TI (VTI) and tilted TI (TTI) media, within a constant Q approximation. Such wave propagation can be modelled with a finite difference scheme by introducing a series of standard linear solid (SLS) mechanisms, and it can be carried out within a tractably small computational region by making use of perfectly-matched layer (PML) boundary conditions. A viscoacoustic wave equation for VTI and TTI media can be derived as in the non-attenuative case using the wave equation in anisotropic media as a starting point and setting the shear wave velocity to zero. Using the TI approximation and ignoring all spatial derivatives in the anisotropic symmetry axis direction leads to instabilities in areas of the model with the rapid variations in the symmetry axis direction. A solution to this problem is proposed that involves selectively equating anisotropic parameters within the model to reduce Thompson parameter differences in problem areas. To eliminate high-frequency instabilities, we apply a regularization operator, which results in a stable viscoacoustic wave propagator in TI media. After correcting for the effects of anisotropy and Q, RTM images are shown with synthetic examples to carry higher resolution information than VTI RTM images alone. Presentation Date: Wednesday, October 17, 2018 Start Time: 1:50:00 PM Location: 205A (Anaheim Convention Center) 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".