Simultaneous sources: The inaugural full-field, marine seismic case history from Australia
Bibliographic record
Abstract
SummarySimultaneous (blended) sources have attracted a great deal of attention recently because of their potential to increase significantly the rate at which seismic data can be acquired. The viability of the method was previously demonstrated through the use of small-scale tests on synthetic and field data. In this paper, we present a case history from Australia of the first field-development-scale use of this technology in the world.Concept studies involving simulations of simultaneoussource data from conventional data indicated that the proposed survey design would yield data that were separable into components for each source. The resultant data set contains twice as many traces as its conventional equivalent, and provides improved sampling for important processing steps such as coherent noise attenuation.Simultaneous-source acquisition requires quality control methods that are specific to the technique to ensure that the data are acquired as planned. New QC methods were developed specifically for this project, and showed that no problems related to the simultaneous-source technique were encountered.Data processing involved source separation at an early stage, after which a conventional processing sequence could be used on the resultant, densely-sampled data set. Separation was performed using a sparse inversion technique, which proved very effective. Very little signal leakage was observed, and the interference was almost completely suppressed.Through this case history, we demonstrate the viability of simultaneous sources as an effective marine seismic acquisition 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".