Elastic-wave reverse-time migration based on decoupled elastic-wave equations and inner-product imaging condition
Bibliographic record
Abstract
Polarity reversal in converted wave images of elastic reverse time migration destructs the reflection events after stacking multi-shot migration profile. We derive a new imaging method for elastic reverse-time migration to automatically circumvent polarity reversal. Instead of obtaining scalar P-wave and vector S-wave potentials from the wavefield by using Helmholtz decomposition as in conventional methods, we obtain vector P- and S-wave displacement wavefields based on a decoupled elastic wave equation, which denotes the displacement component along the propagation direction and perpendicular to the propagation direction, respectively. Based on this decomposition method, the vector P- and S-wavefields preserve the amplitude and phase attributes of the original wavefield. As for the vector wavefields (vector P- and S-wave displacement wavefields), the inner-product imaging condition is proposed to extract reflectivity of specified wave modes at interfaces. The analysis of the imaging kernel demonstrates this imaging condition is valid not only for pure-mode imaging (PP and SS), but also for converted wave imaging (PS and SP) of ground-based seismic exploration. With this new method, we do not have to correct the polarity reversal in converted wave images, which is an essential step in the conventional method with expensive computation costs. Numerical examples with synthetic data have shown that the inner product imaging method works and the quality of the imaging events is effectively improved.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".