Iterative modeling, migration, and inversion: Evaluating the well-calibration technique to scale the gradient in the full-waveform inversion process
Bibliographic record
Abstract
Iterative modeling, migration and inversion (IMMI) aims to incorporate standard processing techniques into the process of full waveform inversion (FWI). Within IMMI, depth migration method may be used to obtain the gradient, in contrast to standard FWI which uses a two-way reverse time migration (RTM). Another aspect of the IMMI approach is the use of well-calibration to scale the gradient, rather than applying a line search to find the scalar or an approximation of the inverse Hessian matrix. We examine with synthetic examples the performance of IMMI in circumstances of progressively increasing geological complexity. We find consistently low errors nearby the well-calibration location, even in the most complex settings. This suggests that the gradient obtained by applying a migration method other than RTM, though less wave-theoretically complete, points in the correct direction in order to minimize an FWI-like objective function, and that well-calibration provides a working approach for scaling. These refinements of FWI may be important enablers for application of waveform inversion in reservoir characterization, where we may have many control-wells, and we may wish to extend our approach to the determination of several elastic and/or rock properties. We find that well-calibration scales the updates properly up to what we refer to as moderate lateral velocity changes. Presentation Date: Tuesday, September 26, 2017 Start Time: 9:45 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".