Validating land data quality of simultaneous multiple vibrator acquisition
Bibliographic record
Abstract
In 2007, ConocoPhillips conducted field experiments designed to evaluate the data quality of multi‐offset VSPs acquired by a single vibrator and simultaneous multiple vibrators. To check the repeatability of vibrator sources, we recorded 8 repeated sweeps at the same source location for both acquisitions. The data quality is consistent from sweep to sweep at the same source location showing good repeatability of vibrator sources. Inverting 8 repeated sweeps simultaneously by a least‐squares approach produces a solution that is very comparable to an average solution derived from inverting each sweep separately. In some cases, the least‐squares solution tends to handle the ambient noise better and gives a slightly better solution than the average solution. The analyses of downgoing and upgoing VSPs demonstrates that simultaneous multiple vibrator acquisition yields equivalent data quality when compared with a single vibrator and cross‐talk artifacts generated by simultaneous multiple vibrators are minimal in this case.
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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".