A comparison of iterative methods performance to exact and pseudo adjoint operators in least-squares migration
Bibliographic record
Abstract
Least-squares migration is often used to attenuate migration artifacts that arise in conventional migration due to, for instance, data incompleteness and limited recording aperture. It uses iterative methods to obtain a model that best fits the data, and it requires a forward/adjoint operator pair to do so. These methods use the gradient of the cost function to estimate search directions and update the current model. The gradient is obtained by migrating the residuals between estimated and observed data at each iteration. Migration operators are regarded as adjoint of forward operators but only those which pass the dot-product test are exact adjoints. These operators estimate more accurate search directions, enhancing the convergence rates of iterative methods. We test the performance of adjoint and pseudo-adjoint operators in LSM based on three different iterative methods (steepest descent, conjugate gradients and limited-memory BFGS) in order to assess their sensitivity to the adjointness of the migration operator. We then compare the performance of each method.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".