Amplitude variation with offset (AVO) inversion modeling with a local elastic solver
Bibliographic record
Abstract
Conventional amplitude variation with offset (AVO) inversion analysis uses the Zoeppritz equations which are based on a plane wave approximation. However, since real seismic data are created by point sources reflections are better modeled by spherical waves than planar waves. Spherical reflection methods such as full waveform inversion provide a solution to the limitations of the Zoeppritz approximation since they can handle near and post-critical reflections. Additional advantages of full waveform inversion are its applicability to heterogeneous models and its iterative multi-parameter inversion, however, this method is computationally expensive. In this study, we model AVO using a coupled elastic-acoustic local solver to model the wavefields and find that the amplitude of the reflection generated by the local solver can be used to invert material properties at a reflector of interest. Presentation Date: Tuesday, October 16, 2018 Start Time: 9:20:00 AM Location: Poster Station 13 Presentation Type: Poster
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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) | 0.000 | 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) | 0.002 | 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 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".