Multicomponent seismic data registration by nonlinear optimization: Part 1
Bibliographic record
Abstract
Mapping PS-wave data to PP-wave time domain is a critical step before joint interpretation and pre-stack inversion. Multi-component seismic data registration is usually performed with provided Vp/Vs ratio, however, accurate information of velocity ratio is absent in most cases. One can solve the registration problem by minimizing the difference between PP-wave and warped PS-wave data with the constraints of a smooth Vp/Vs ratio field. In order to avoid undesirable foldings and rapid changes in warped PS-wave image, we generally require the warping function to be monotonic with respect to PP-wave travel time and smooth in both time and spatial direction, those requirements are extremely difficult to satisfy in common registration methods. We propose to use Vp/Vs ratios as the model parameters in the registration problem. so Vp/Vs ratios can be invereted directly instead of estimating it from warping functions. Seismic data registration is a highly non-linear optimization problem, all gradient-based solvers are likely to be trapped in the local minima of the cost function. In order to alleviate this problem, we propose to use cubic B-splines to represent the Vp/Vs ratio field, so the number of local minima in the cost function can be reduced by with the decreasing number of unknowns. Presentation Date: Wednesday, October 19, 2016 Start Time: 3:10:00 PM Location: 166 Presentation Type: ORAL
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".