Geohazard detection using 3D seismic data to enhance offshore scientific drilling site selection
Bibliographic record
Abstract
Abstract. A geohazard assessment workflow is presented that maximizes the use of 3D seismic reflection data to improve the safety and success of offshore scientific drilling. This workflow has been implemented for International Ocean Discovery Program (IODP) Proposal 909 that aims to core seven sites with targets between 300 and 1000 m below seabed across the north-western Greenland continental shelf. This glaciated margin is a frontier petroleum province containing potential drilling hazards that must be avoided during drilling. Modern seismic interpretation techniques are used to identify, map and spatially analyse seismic features that may represent subsurface drilling hazards, such as seabed structures, faults, fluids and challenging lithologies. These hazards are compared against the spatial distribution of stratigraphic targets to guide site selection and minimize risk. The 3D seismic geohazard assessment specifically advanced the proposal by providing a more detailed and spatially extensive understanding of hazard distribution that was used to confidently select eight new site locations, abandon four others and fine-tune sites originally selected using 2D seismic data. Had several of the more challenging areas targeted by this proposal only been covered by 2D seismic data, it is likely that they would have been abandoned, restricting access to stratigraphic targets. The results informed the targeted location of an ultra-high-resolution 2D seismic survey by minimizing acquisition in unnecessary areas, saving valuable resources. With future IODP missions targeting similarly challenging frontier environments where 3D seismic data are available, this workflow provides a template for geohazard assessments that will enhance the success of future scientific drilling.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".