FWI using reflections for deep velocity model updates
Bibliographic record
Abstract
Full Waveform Inversion (FWI) utilizes refractions and reflections to improve the accuracy and resolution of the earth subsurface models. The use of refractions is limited by the maximum offset of the acquisition, up to their maximum penetration depth. In contrast, reflections can produce deeper updates with small offsets, but they demand robust and more sophisticated algorithms. FWI using reflections needs hard boundaries in the velocity/density models to simulate backscatter energy and generate the velocity sensitivity kernels. Alternatively, one can apply the wave-equation and first-order Born approximation to decompose the seismic wavefields into background and perturbations. Here, we utilize the acoustic wave-equation in terms of vector reflectivity to produce reflections in the modeling engine of FWI. The vector reflectivity wave-equation is derived by parametrizing the variable density acoustic wave-equation. The main advantages of its insertion in the FWI algorithm are the following: it does not require the construction of density/hard boundaries in the velocity model to generate reflections; it allows the use of reflected events without the need of solving two different wave-equations in the forward and backward propagation; it is more accurate than the method based on the first-order Born approximation and perturbation theory. We illustrate with synthetic and field data examples the use of deep reflections to produce FWI updates. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 1:50 PM Presentation Time: 2:40 PM Location: 361F 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.032 |
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".