Efficient pseudo-Gauss-Newton full-waveform inversion in the τ-p domain
Bibliographic record
Abstract
ABSTRACT Full-waveform inversion (FWI) seeks to estimate subsurface elastic properties by iterative minimization of the difference between synthetic and observed data. Its industrial application suffers from several well-defined obstacles, including high computational cost, slow convergence rate, and the phenomenon of cycle skipping. We have developed an efficient τ-p domain waveform inversion aimed at reducing the computational burden of FWI with a phase-encoding technique. The gradient is constructed in the τ-p domain using linear phase-encoding, and a slant update strategy further reduces the computational burden. Poorly scaled and blurred gradient updates can be enhanced using exact or approximate versions of the inverse Hessian, which leads to a faster convergence rate. We developed a new chirp phase-encoding strategy for diagonal Hessian construction. Preconditioning the gradient using the diagonal phase-encoded approximate Hessian forms what we refer to as a pseudo-Gauss-Newton (PGN) step. To test the effectiveness of the τ-p-domain FWI, the strategies were enacted on a modified Marmousi model. We compared the computational cost of the PGN method with traditional methods, and we evaluated the quality of the inversion results. The PGN method can get a better inversion result with the same computational cost. We have also analyzed the effects of different ray parameter settings and the influence of source spacing, and we compared different preconditioning methods for τ-p-domain FWI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".