A new qP-wave approximation in TTI media and its reverse time migration
Bibliographic record
Abstract
A pure qP-wave equation that is free of shear-wave artefacts is usually desired during imaging seismic data in tilted transversely isotropic (TTI) media, by which, crosstalks caused by the interference between different wave modes can be eliminated. However, an undesired SV-wave energy could be generated during modeling, even if an acoustic anisotropic wave equation is used. A new fully decoupled P-wave equation in TTI media is developed in this study to avoid unwanted energy during migration. During wavefield simulation, the finite difference (FD) and pseudospectral (PS) method are combined in order to accelerate the computation. The H-PML in second order wavenumber domain is also proposed and applied to eliminate the artificial boundary reflections, comparisons of different absorbing boundary layers are also illustrated to validate the wave number domain H-PML. The new algorithm is further applied in a new 3D anisotropic reverse time migration (RTM), which has been tested on 3D synthetic SEAM data and field data. As a result, seismic images with high resolution are produced. Benchmarks against commercial implementations are also demonstrated, which proves that the positioning of structure is more reliable and accurate with the new algorithm. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 8:30 AM Presentation Time: 11:25 AM Location: 361A 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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".