Multi-Source Wavefield Reconstruction: a Technique for Recovering Broadband Seismic Data from Narrowband Shot Records
Bibliographic record
Abstract
Summary New acquisition strategies implementing diverse sources with different spectral characteristics have been proposed as potential solutions to optimize survey costs, reduce the environmental footprint, and increase the bandwidth of the seismic data acquired. All these strategies are generally based on variations of the Dispersed Source Arrays (DSA) concept. We present a multi-source wavefield reconstruction technique to recover broadband seismic data from narrowband shot records acquired through a more flexible and low-impact acquisition strategy named Non-uniform Dispersed Source Arrays (NU-DSA). The method exploits the incoherence generated in the common-receiver domain by the acquisition and poses an inverse problem with $\emph{lasso}$ structure similar to compressive sensing reconstruction and deblending. The difference, however, relies on the convolutional interaction between the linear operators involved. The source operator assumes the location for each type of source interleaved in the grid is known, and one can estimate the signature to some level of accuracy. Then, a sparse-promoting inversion procedure recovers broadband seismic shots by reconstructing the bandwidth at each location using the spectral information of adjacent shots. Numerical examples are used to validate the proposed method.
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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 | 0.001 | 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 | 0.001 |
| 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".