Full shot and receiver deghosting for Broadband and Conventional streamer 4D studies: How close can we get?
Bibliographic record
Abstract
Summary In this paper, we focus on new methods to address the spectral phase and amplitude differences between conventional and broadband streamer surveys in a 4D study. Instead of downgrading the broadband monitor data, we perform “full” deghosting, source-side for the broadband data, and both source- and receiver-side for the legacy data. This broadens the spectra of both vintages to an equal bandwidth and removes the differences due to source and receiver depth variations, to immediately produce very good 4D repeatability indicators. We show the deghosted vintages can be used simultaneously in the SRME modeling step to improve signal to noise and to ameliorate offset sampling issues. We see better SRME results by using this 4D modeling technique which can be very important for 4D's where multiples are issues.
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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 | 0.000 |
| 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".