Fast least-squares imaging with surface-related multiples: Application to a North Sea data set
Bibliographic record
Abstract
Abstract In marine seismic acquisition, surface-related multiples constitute a significant portion of the acquired data. Typically, multiples are removed during early-stage data processing because they can lead to phantom reflectors during migration that might result in erroneous geologic interpretations. However, if properly dealt with, multiples can provide valuable extra information and can complement primaries in illuminating the subsurface. Reverse time migration has limitations in imaging multiples. A computationally efficient inversion procedure is proposed which jointly maps primaries and multiples to the true reflectors and estimates the source function on the fly. As a result, high-quality, mostly artifact-free broad-band images are obtained in which the imprint of the source function is partly removed at a computationally affordable expense compared with the combined costs of wave-equation-based surface-related multiple elimination and reverse time migration. All this is achieved by including the total downgoing wavefields as areal sources in least-squares migration in combination with curvelet-domain sparsity promotion. The proposed method is applied to a shallow-water marine data set from the North Sea which contains abundant short-period surface-related multiples, and the efficacy of the method is shown in eliminating coherent imaging artifacts associated with multiples. The benefits of joint imaging of primaries and multiples are demonstrated compared with imaging these signal components separately.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".