Accelerating Hessian-free Gauss-Newton full-waveform inversion via improved preconditioning strategies
Bibliographic record
Abstract
Gradient-based methods for full-waveform inversion (FWI) have the potential to converge globally but suffer from a slow convergence rate. Newton-type methods provide quadratic convergence, but they are computationally burdensome for large-scale inverse problems. The Hessian-free (HF) optimization method represents an attractive alternative to these above-mentioned optimization methods. At each iteration, the HF approach obtains the search direction by approximately solving the Newton linear system using a conjugate-gradient (CG) algorithm. One issue with HF optimization is that the CG algorithm requires many iterations. In this paper, we develop and compare different preconditioning schemes for the CG algorithm to accelerate the HF Gauss-Newton method. Traditionally, the preconditioners are designed as diagonal Hessian approximations or inverse Hessian approximations. In this research, we propose to construct the l-BFGS inverse Hessian preconditioner with the diagonal Hessian approximations as initial guess. It is shown that the quasi-Newton l-BFGS preconditioning scheme with the pseudo diagonal Gauss-Newton Hessian as initial guess shows the best performances in accelerating the HF Gauss-Newton FWI. Presentation Date: Wednesday, October 19, 2016 Start Time: 10:45:00 AM Location: 143/149 Presentation Type: ORAL
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".