An analysis of the distribution of time delays on simultaneous source separation
Bibliographic record
Abstract
Simultaneous source acquisition has been recognized as an important way of improving the efficiency and quality of seismic data acquisition. Recently, several methods have been developed for separating simultaneous sources and to provide data that can be utilized in conventional processing streams. The key is to introduce randomness in time delays among simultaneously fired shots to make interferences appear incoherent in common receiver, common offset and common midpoint gathers. In this paper, we study the separability of simultaneous source data based on different distributions of fire time delays. We conduct Monte Carlo tests to analyze the relationship between different firing schemes and the quality of the separation via the iterative rank reduction (IRR) deblending method. We also adopt the fast simulated annealing (FSA) method to estimate an optimal empirical firing scheme by assuming a priori knowledge of the unblended data. Insights can be gained from these tests towards optimal acquisition design for simultaneous source acquisition.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".