Elastic least-squares reverse time migration via linearized elastic full-waveform inversion with pseudo-Hessian preconditioning
Bibliographic record
Abstract
Time domain elastic least-squares reverse time migration (LSRTM) is formulated as a linearized elastic full waveform inversion (FWI) 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 elastic images. Our algorithm is obtained using continuous functional analysis where the problem is discretized at the final stage (optimize-before-discretize approach). The discretized numerical versions of the elastic Born operator and elastic RTM operator pass the dot product test. The conjugate gradient least squares (CGLS) method is used to solve the least-squares optimization problem. The diagonal of the pseudo-Hessian is used for preconditioning the elastic LSRTM. Elastic LSRTM provides higher resolution images with fewer artifacts and a superior balance of amplitudes when compared to elastic RTM. Moreover, elastic LSRTM can remove cross-talk between P-and S-wave impedance perturbations. Presentation Date: Monday, September 25, 2017 Start Time: 2:40 PM Location: 361A 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.007 | 0.003 |
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".