Extraction of additional data using extended simultaneous multiple source inversion
Bibliographic record
Abstract
A new method that is a modification of a self-truncating vibroseis extended correlation uses an inversion method instead of a cross correlation to extract simultaneous multiple source (SMS) data beyond a listening time. The process of inverting data beyond the listening time is referred as extended SMS inversion. It produces exactly the same data as the traditional SMS data if the output time after the inversion is equal to the listening time. If the inverted output time is greater than the listening time, the reduced bandwidth in recorded data also decreases the bandwidth of inverted data. Fortunately, the frequency loss due to the intrinsic-earth attenuation usually decays faster than the reduced bandwidth in recorded data. The bandwidth of the extended data is often well above the data bandwidth required for seismic explorations. In general, the reduction of data bandwidth is not an issue for typical seismic explorations and the use of extended inversion beyond the listening time typically reconstructs geological structures extremely well. The extended inversion can also be used to minimize the listening time in designing the acquisition parameter. We demonstrate the effectiveness of this method with synthetic and real 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".