Accelerating True-amplitude RTM of Deepwater Data Using Analytic Redatuming
Bibliographic record
Abstract
Summary Reverse-Time Migration (RTM) is routinely used for depth velocity model building and imaging in deepwater exploration around the world. In this study, we employed analytical redatuming to improve the efficiency of true-amplitude RTM for deepwater Earth model. This method analytically redatums both the source and receiver wavefields from surface to water bottom and injects the redatumed wavefield into the computational domain while preserving true-amplitude in the migrated common-shot image. By using analytic redatuming in deepwater area, the total runtime, memory and disk space requirements are significantly reduced. In addition, the image quality is improved by overcoming the singularity of point-source injection in the finite difference method, accurate positioning of source and receivers, and the reduction of numerical dispersion noise. The underlying theories developed in this paper can be extended to some other important applications such as Full-Waveform Inversion (FWI), true-amplitude layer-stripping RTM/FWI.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".