Least-squares Migration via a Gradient Projection Method - Application to Seismic Data Deblending
Bibliographic record
Abstract
Summary We propose a gradient projection method that is applicable to least-squares migration for separation of simultaneous source seismic data. Using shot-profile split-step migration and de-migration operators, we notice that, in shot-index image domain, the simultaneous source interferences appear random whereas the desired signal is coherent. The latter is used as a coherency constraint for least-squares migration. We incorporate a projection operator, which is the Singular Spectrum Analysis (SSA) filter in shot-index domain, into gradient projection method to solve for a volume of artifacts-reduced shot-index gathers that honors the observed data. The method effectively suppresses simultaneous source crosstalk and improves the quality of shot-index image gathers. The outputs of our method can be a crosstalk-free migrated image and the deblended data set that can be used in conventional processing workflows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".