The sensitivity of converted-phase extended images to P- and S-wavespeed variations
Bibliographic record
Abstract
Summary Converted-phase (CP) imaging produces high resolution images and can be used effectively for updating both P- and S-wave speed models with an optimization scheme that is formulated in the extended image domain. This optimization is referred to as source-independent converted-phase WEMVA (SICP-WEMVA). However, the convergence of the optimization scheme depends on the selection of parameters and the formulation of the objective functions and their gradients. In this study, we investigate the sensitivity of the extended images for SICP-WEMVA to the domain where the objective function is formulated. We derive analytically the behavior of the seismic energy (i.e., seismic moveout) in the extended horizontal and vertical subsurface space-lag images as a function of P- and S-wave speed variations, and compare them with numerical results. The results of the moveout analysis demonstrate that the extended vertical subsurface space-lag images have higher sensitivity to the background P- and S-wave speed variations than the horizontally extended images and thus may have significant implications on the resolution and convergence of the SICP-WEMVA.
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 | 0.005 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".