Seafloor seismic acquisition using autonomous underwater vehicles
Bibliographic record
Abstract
ABSTRACT Although narrow‐azimuth towed‐streamer data provide good image quality for structural interpretation, it is generally accepted that for wide‐azimuth marine surveys seabed receivers deliver superior seismic reflection measurements and seismically derived reservoir attributes. However, seabed surveys are not widely used due to the higher acquisition costs when compared to streamer acquisition. In recent years, there have been significant engineering efforts to automate receiver deployment and retrieval in order to minimize the cost differential and conduct cost‐efficient seabed receiver seismic surveys. These engineering efforts include industrially engineered nodes, nodes on a rope deployment schemes and even robotic nodes, which swim to and from the deployment location. This move to automation is inevitable, leading to robotization of seismic data acquisition for exploration and development activities in the oil and gas industry. We are developing a robotic‐based technology, which utilizes autonomous underwater vehicles as seismic sensors without the need of using a remotely operated vehicle for deployment and retrieval. In this paper, we describe the autonomous underwater vehicle evolution throughout the project years from initial heavy and bulky nodes to fully autonomous light and flexible underwater receivers. Results obtained from two field pilot tests using different generations of autonomous underwater vehicles indicate that the seismic coupling, and navigation based on underwater acoustics are very reliable and robust.
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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 | 0.000 |
| 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) | 0.001 | 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".