Least‐squares migration with dip‐field regularization: Application to 3D VSP data
Bibliographic record
Abstract
Least-squares migration purports to mitigate the impact of irregular acquisition geometries and limited aperture. These are both common problems with multi-offset VSP surveys, so it makes sense that a least-squares approach should be beneficial to VSP imaging. While iterative least-squares migration implementations are expensive for 3D surface seismic data volumes, even comparatively large 3DVSP surveys are small enough to consider a full, iterative least-squares migration. In this paper we review least-squares migration and include a regularization term containing information about the image dip field. This approach allows migration artifacts to be suppressed while fitting the data. The conjugate gradient algorithm is used to solve the inverse problem, and to speed up convergence a preconditioning is used that contains true amplitude weights such as geometrical spreading, constant frequency Q attenuation and angle-dependent scattering. We use a Kirchhoff-type vector implementation with a VTI ray trace kernel and demonstrate resolution improvement and artifacts suppression on synthetic 2D and 3D VSP data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".