Detecting subsea permafrost layers on marine seismic data: An appraisal from forward modelling
Bibliographic record
Abstract
ABSTRACT Detecting the top and base subsea permafrost from 2D seismic reflection data in shallow marine settings is a non‐trivial task due to the occurrence of strong free surface multiples. The potential to accurately detect permafrost layers on conventional 2D seismic reflection data is assessed through viscoelastic modelling. Reflection imaging of permafrost layers is examined through the evaluation of specific characteristics of the subsurface, acquisition parameters and their impact. Results show that limitations are related to the principles of the method, the intrinsic nature of the permafrost layers, and the acquisition geometry. The biggest challenge is the occurrence of free surface multiples that overprint the base permafrost reflection, with the worst‐case scenario the case of a thin layer of ice‐bonded sand. Wedge models suggest that if the base permafrost is dipping, it would intersect internal and free surface multiples of the seafloor and the top permafrost and be detected. Also, the amplitude ratio of the base permafrost reflection and the multiples decreases with the increasing thickness of permafrost. Therefore, the crosscutting relationship between the reflection at base permafrost reflection and the multiples might not be enough to detect the base permafrost for thicker permafrost layers. Finally, the experiment results show that, for partially ice‐bonded layers, the attenuation combined with the low reflectivity of the basal interface limits the likelihood to resolve the base permafrost, especially for thick permafrost layers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".