Iterative modeling migration and inversion (IMMI): Combining full waveform inversion with standard inversion methodology
Bibliographic record
Abstract
Summary Full Waveform Inversion (FWI) employs full waveform information to estimate subsurface properties through an iterative process by minimizing the difference between the observed data and synthetic data. This inversion method has been widely studied in recent years but still cannot be practiced effectively in industry. The gradient in FWI is similar to an ungained Reverse Time Migration (RTM) image with the cross-correlation imaging condition. The estimation of the velocity update from the migrated section for FWI is analogous to the common process of impedance inversion. We show that the poorly scaled gradient can be improved by preconditioning with an approximate Hessian matrix (source illumination) forms the gradient or image based on deconvolution imaging condition which can be used to estimate the approximate reflectivity section directly. Thus, the deconvolution based gradient can be employed to estimate the impedance perturbation using the standard inversion methodology, denoted as SM. By examining the key concepts in FWI and SM, a hybrid inversion strategy is proposed by combining FWI with SM, namely, Iterative Modeling Migration and Inversion (IMMI). The IMMI method keeps the step of creating reflectivity image tying to wells control in SM and incorporates the concepts of imaging the data residuals and iteration from FWI. Furthermore, to reduce the computation cost for constructing the gradient and source illumination, the phase-encoding technique is introduced. In this paper, we practice the proposed strategies on a modified Marmousi model and the inversion results show that the IMMI method can reconstruct the velocity model efficiently and stably.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".