Angle-domain least-squares Kirchhoff migration with angle-dependent L1 regularization
Bibliographic record
Abstract
ABSTRACT Least-squares migration (LSM) is a highly ill-posed inverse problem because of undersampled seismic acquisition and the band-limited property of recorded data. The inverted solution from the iteration solvers may suffer from migration artifacts and low spatial resolution. Total variation (TV) and L1 regularization methods can be used to mitigate these undesired effects present in the inverted LSM solution. However, the LSM method formulated in the angle-domain, which is denoted as angle-domain LSM, will become a more ill-posed inverse problem than standard LSM, due to angle-dependent wavelet stretching effects and migration artifacts in the migrated angle-domain common-image gathers (ADCIGs). To mitigate these artifacts and improve the quality of ADCIGs, an angle-domain least-squares Kirchhoff migration with angle-dependent L1 regularization was developed. There are two key points in the proposed method. The first key point is that the angle-domain Hessian matrix was explicitly computed by angle-domain Kirchhoff migration. The second key point is that angle-dependent L1 regularization was used to mitigate the angle-dependent stretching effects in the migrated and inverted ADCIGs. Meanwhile, the TV regularization along the spatial direction and a smooth constraint along the angle direction are incorporated to mitigate the migration artifacts. The alternating direction method of multipliers was used to resolve this optimization problem. Through numerical experiments with synthetic and field data, the effectiveness of the proposed method was tested, and two key benefits were highlighted. First, the proposed angle-dependent L1 regularization can effectively mitigate the angle-dependent stretching effects in the inverted ADCIGs. Second, the proposed least-squares Kirchhoff migration method can efficiently and effectively recover high-resolution and high-fidelity ADCIGs in the case of inhomogeneous velocity. In addition, the proposed method remains effective even in the presence of migration velocity errors and sparse recorded 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".