Diffraction imaging of subsea permafrost discontinuities
Bibliographic record
Abstract
Laterally discontinuous subsea permafrost is present in the Arctic along the Beaufort Sea margin. Discontinuities within the permafrost include unfrozen zones from which fluids are free to migrate vertically or laterally, potentially accelerating permafrost degradation. This process releases greenhouse gases that further contribute to global warming. Generally, because of its contrasting viscoelastic properties compared to unfrozen sediments, permafrost can be easily detected by seismic methods. A discontinuity in subsea permafrost corresponds to the termination of a frozen layer. At this termination, seismic energy is diffracted rather than reflected or refracted. This condition is well suited for diffraction imaging. Here, we present a processing workflow to identify subsea permafrost discontinuities using the diffracted wavefield. This workflow aims to extract diffractions from seismic data collected on the continental shelf of the Canadian Beaufort Sea. The shallow water environment combined with the occurrence of subsea permafrost generates highly energetic free surface multiples that overprinted diffractions. Thus, preliminary steps of the processing flow focused on multiple attenuation. A recursive velocity analysis, starting with a 100 common-midpoint (CMP) interval and ending with a 5 CMP interval, is also performed to better capture lateral permafrost discontinuities. Then, the full wavefield data are migrated, collapsing the energy distributed along the hyperbolic trajectory of the diffractions at their apexes. Afterwards, reflections are adaptively subtracted from the migrated data. Finally, demigration of the residuals (i.e., collapsed diffractions) is performed. The resulting image reveals several near-surface diffractions attributed to discontinuities at the top of the subsea permafrost. Diffractions present distinct amplitude, frequency and velocity characteristics suggesting that various permafrost conditions coexist across the continental shelf.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".