Full Wave Form Inversion from Topography — The Husky Experience Revisited
Bibliographic record
Abstract
The process of migration velocity analysis has been transformed by the development of full wave form inversion (FWI) techniques. With the introduction of seismic tomography (Bording et al., 1987) for velocity analysis depth imaging took a significant step forward. Prior to the use of seismic tomography successful methods for the development of interval velocities was limited. A number of new methods of depth migration based on heterogeneous interval velocity models became popular including; reverse time migration (Baysal et al., 1987), Kirchhoff with eikonal travel times (Gray and May, 1994), and iterative depth migration by Whitmore, (1983). The technology changes in migration were paralleled by the development of wave form inversion research led by Tarantola's early work in 1984 soon followed by Pratt et al. (1998), Shin et al. (2001), and Shin et al. (2003) and others. The usefulness of FWI is enhanced if basic seismic data processing is kept to a minimum, particularly the notion of datuming to correct for surface topography, (Gray and Marfurt, 1995). Here we present a full wave form inversion result for field data with a shot/receiver line that has substantial topographic change.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".