<i>Q</i> -compensated reverse time migration in tilted transversely isotropic media
Bibliographic record
Abstract
ABSTRACT Anisotropy and absorption are critical to the modeling and analysis of seismic amplitude, phase, and traveltime data. Neglecting any of these phenomena, which are often both operating simultaneously, degrades the resolution and interpretability of migrated images. However, a full accounting of anisotropy and anelasticity is computationally complex and expensive. One strategy for accommodating these aspects of wave propagation, while keeping the cost and complexity under control, is to do so within an acoustic approximation. We have set up a procedure for solving the time-domain viscoacoustic wave equation for tilted transversely isotropic (TTI) media, based on a standard linear solid model and, from this, develop a viscoacoustic reverse time migration (Q-RTM) algorithm. In this approach, amplitude compensation occurs within the migration process through a manipulation of attenuation and phase dispersion terms in the time-domain differential equations. Specifically, the back-propagation operator is constructed by reversing the sign only of the amplitude loss operators, but not the dispersion-related operators, a step made possible by reformulating the absorptive TTI equations such that the loss and dispersion operators appear separately. The scheme is tested on synthetic examples to examine the capacity of viscoacoustic RTM to correct for attenuation and the overall stability of the procedure.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".