Integrating streamer and ocean-bottom seismic data for sub-basalt imaging on the Atlantic Margin
Bibliographic record
Abstract
ABSTRACT Stacked basalt flows cover much of the NW European continental margin, including potentially prospective sediments of the Faroes shelf. Such flows attenuate seismic energy, hindering sub-basalt structural imaging which is critical for both exploration and tectonic studies. Low-frequency, long-offset reflection surveys have yielded improved images below top basalt, while coincident ocean-bottom seismometer (OBS) data have mapped low velocity zones (LVZ) from tomographic inversion. Image correlation in a common depth domain is challenged by low spatial resolution of the tomographic image, the absence of turning rays in a LVZ and the lack of wide-angle arrivals in the reflection data. We integrate densely sampled reflection data with deep velocities from OBS data to give a common velocity model in a new, iterated, pre-stack depth-migration workflow using complementary constraints from the two datasets. The matched velocity model and depth image enable interpretation of (i) a uniform flood basalt sequence, 2–4 km thick beneath the Fugloy Ridge, with velocities correlating with those in the Lopra 1/1A borehole and (ii) a sub-basalt LVZ with chaotic reflections corresponding to sill-intruded, probably syn-rift sediments. Such a workflow could be extended to targets beneath high velocity salt or basalt and could provide constraints for 3D datasets. Supplementary material: Overlays of modelled arrival times on all the OBS gathers, similar to Figure 4, can be found at http://www.geolsoc.org.uk/SUP18433
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.008 | 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".