Integrating FWI with Surface-wave Inversion to Enhance Near-surface Modelling in a Shallow-water Setting at Eldfisk
Bibliographic record
Abstract
Summary Following a ‘noise becomes signal’ philosophy, we have successfully integrated surface-wave inversion and early-arrival full-waveform inversion on a multicomponent ocean-bottom cable dataset, thus extracting more information from the recorded field data. In the near-surface above the Eldfisk Field, Norwegian North Sea, two perpendicular sets of Pleistocene subglacial tunnel valley systems have been resolved at two depth ranges between the seabed and 300m depth of the updated Vp model by this integrated inversion scheme. This indicates that the integrated near-surface Vp model is of high resolution both laterally and vertically and explains surface-waves and the early arrivals, diving waves, or both. We have demonstrated that surface-wave inversion complements full-waveform inversion by providing a near-surface (0–150 m) Vp model in a depth range where full-waveform inversion techniques typically produce suboptimal results due to null-space issues, vertical resolution limitations and errors in source wavelet, density approximations, multiple modelling, and acoustic assumptions. The combined full-waveform inversion and surface-wave inversion Vp model update in the near-surface significantly flattens the common-image gather events between 0–1000 m and deeper. This confirms the validity of these near-surface updates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".