Multisource wavefield reconstruction via nonuniform dispersed source arrays
Bibliographic record
Abstract
ABSTRACT New acquisition strategies that combine seismic sources emitting energy at different bandwidths are developed to reduce survey costs, minimize environmental impact, and enhance data quality. In general, all these strategies are variations in the dispersed source arrays (DSA) concept. We develop a new multisource wavefield reconstruction technique to recover broadband seismic data from data acquired by implementing nonuniform DSA (NU-DSA). Seismic data acquired through NU-DSA are multiple narrowband shot records with complementary bandwidth. Organized as common-receiver gathers, NU-DSA data display incoherent artifacts in the f-k domain. The technique exploits the aforementioned incoherence and poses an inverse problem with a least absolute shrinkage and selection operator structure similar to compressive sensing reconstruction and deblending via inversion. The sparsity-promoting inversion recovers broadband data at each location by exploiting information from multiple adjacent shots. Numerical experiments demonstrate that our method effectively reconstructs data bandwidth while mitigating incoherent artifacts created by the acquisition stage. Overall, the technique presented is an inversion-based alternative to conventional processing data and represents a step forward in DSA technologies.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".