Elastic least-squares reverse time migration via linearized elastic full-waveform inversion with pseudo-Hessian preconditioning
Bibliographic record
Abstract
ABSTRACT Time-domain elastic least-squares reverse time migration (LSRTM) is formulated as a linearized elastic full-waveform inversion problem. The elastic Born approximation and elastic reverse time migration (RTM) operators are derived from the time-domain continuous adjoint-state method. Our approach defines P- and S-wave impedance perturbations as unknown elastic images. Our algorithm is obtained using continuous functional analysis in which the problem is discretized at the final stage (optimize-before-discretize approach). The discretized numerical versions of the elastic Born operator and its adjoint (elastic RTM operator) pass the dot-product test. The conjugate gradient least-squares method is used to solve the least-squares migration quadratic optimization problem. In other words, the Hessian operator for elastic LSRTM is implicitly inverted via a matrix-free algorithm that only requires the action of forward and adjoint operators on vectors. The diagonal of the pseudo-Hessian operator is used to design a preconditioning operator to accelerate the convergence of the elastic LSRTM. The elastic LSRTM provides higher resolution images with fewer artifacts and a superior balance of amplitudes when compared with elastic RTM. More important, elastic LSRTM can mitigate crosstalk between the P- and S-wave impedance perturbations given that the off-diagonal elements of the Hessian are attenuated via the inversion.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".