Multicomponent seismic data registration by nonlinear optimization
Bibliographic record
Abstract
Mapping PS-wave data to the PP-wave time domain is a critical step before joint PP- and PS-wave data interpretation and inversion. Registration techniques are often constrained by having access to a known [Formula: see text] ratio. When an accurate [Formula: see text] ratio is not provided, one can solve the problem of seismic data registration by minimizing the difference between the PP-wave and the warped PS-wave data with a smoothing constraint applied on the warping function. To avoid undesirable foldings or rapid changes in the warped PS-wave image, we require a warping function that is monotonic and smooth. We invert the [Formula: see text] ratio directly from PP- and PS-wave data instead of estimating it from the warping function. Seismic data registration is posed as a constrained nonlinear optimization problem. Furthermore, we represent the [Formula: see text] ratio by spline functions and adopt a parameterization that guarantees monotonic warping functions. Our parameterization in terms of splines significantly reduces the number of unknowns of our problem, and the convergence to a smooth monotonic solution is guaranteed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".