Solving least squares Kirchhoff migration using multigrid methods
Bibliographic record
Abstract
In order to attenuate the migration artifacts and increase the special resolution of the subsurface reflectivity, conventional migration may be replaced by the least squares migration (LSM). However, this is a costly procedure. To reduce the cost, the feasibility of using the multigrid methods in solving the linear system of prestack Kirchhoff LSM equation is investigated. This study showed that the conventional method of multigrid is not viable to solve Kirchhoff LSM equation for at least two reasons. The main reason is that the Hessian matrix is not a diagonally dominant matrix. Therefore, the conventional iterative solvers of the multigrid are not effective. The performance of Conjugate Gradient (CG) multigrid is discussed. It is shown that since CG does not have a smoothing property, it should not be considered as an effective multigrid iterative solver. Using the CG as an iterative solver for the multigrid may slightly reduces the number of iterations for the same rate of convergence in the CG itself. However, it does not reduce the total computational cost.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".