Full-waveform inversion without tears: A forward modeling-free gradient
Bibliographic record
Abstract
Full waveform inversion (FWI) is a machine learning algorithm with the goal to find the Earth's model parameters that minimize the difference of acquired and synthetic shots. In this work, we are introducing a new interpretation of the gradient as the residual impedance inversion of the acquired data. Its estimation is forward modeling and wavelet free, reducing its costs drastically, as the inverted model could be obtained on a personal laptop without the need of parallel processing. The new method was applied, with great success, on the acoustic Marmousi simulation. The inverted model, when using the same starting point, is comparable to the results when using the migrated residuals. This approximation also opened the possibility to change the order of migration and the stack steps, during the gradient estimation, to use a post-stack depth migration, and results are promising. In the end, we are proposing a new FWI approximation that is cheap and stable, and could be applied on a real seismic survey in a processing center that has enough computer power to run a PSDM or even just a post-stack depth migration. Presentation Date: Tuesday, September 26, 2017 Start Time: 10:10 AM Location: Exhibit Hall C, E-P Station 3 Presentation Type: EPOSTER
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".