The Use of Drones for Innovative Seismic Acquisition: A Change of Paradigm for HSE
Bibliographic record
Abstract
Abstract The use of drones in the oil and gas industry is still relatively recent, and is currently unlocking new methods and approaches of geophysical acquisition and subsurface imaging. METIS®, a disruptive and integrated research project, employs the use of drones to perform an innovative 3D high density geophysical acquisition, that targets hard-to-access acreage. The benefits associated with the use of drones are easily recognized: an increased efficiency, fewer man hours, reduced HSE risks, and a lower environmental footprint. However a number of new safety, security, regulatory, and public perception issues are raised and need to be better understood before the use of drones can become standard practice. The acceptability of drones and a new method to assess the risks associated to METIS® drone operations is investigated. This study presents how the use of drones is changing the HSE risks associated with an onshore geophysical acquisition, but also how this technology brings new solutions to reduce them.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".