A Comparison of Different Scaling Methods for Least-squares Migration/inversion
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Bibliographic record
Abstract
Summary The least-squares inverse strategy is a very important method in estimating the subsurface parameters (e.g. full waveform inversion (FWI)) through an iterative process. The gradient based method suffers from slow convergence rate for assuming the Hessian matrix as an identity matrix. However, direct calculation of the Hessian matrix is prohibitively expensive. Different Hessian approximations have been proposed for computational convenience. In this research, we analyzed and compared different Hessian approximations. And a chirp phase encoding strategy was introduced to construct the diagonal Hessian, compared to linear phase encoding method. These different Hessian approximations can be employed to precondition the gradient and increase the convergence rate of the least-squares inverse problem.
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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 | 0.000 |
| Open science | 0.000 | 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 it