Structurally Consistent Elastic Frequency- Controllable Envelope Inversion for P- and S-Wave Velocity Model Building
Bibliographic record
Abstract
Elastic full waveform inversion (EFWI) is able to simultaneously build multiple subsurface parameters. However, EFWI faces the ill-posed problem and the multi-parameter coupling effects. Building accurate initial P- and S-wave velocity models is crucial for mitigating these issues and ensuring the convergence of EFWI. Yet, limitations of acquisition systems and the different sensitivities of P- and S-waves to wavenumber components pose significant challenges in simultaneously building P- and S-wave velocity models with structural consistency. These issues may result in additional iterations, or in some cases, even non-convergence. Aiming to build accurate long-wavelength velocity models with high structural consistency, a novel structurally consistent elastic frequency controllable envelope inversion (SC-EFCEI) method is proposed in this paper. SC-EFCEI performs multi-scale inversion using elastic frequency-controllable envelope data to build long-wavelength models, and additionally introduces structural constraints between different velocity gradients via automatic differentiation (AD) to enforce consistency between the inverted P- and S-wave velocity models. Numerical experiments on the elastic Overthrust model, modified Marmousi2 model, and Chevron 2014 blind dataset verified the effectiveness of the proposed inversion method. The inverted P- and S-wave velocity models both successfully reveal the large-scale features of the velocity model while exhibiting good structural consistency, which can be used as good initial models for EFWI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".