High-resolution impedance estimation using multiparameter viscoelastic FWI in a Gulf of Mexico setting
Bibliographic record
Abstract
Abstract Traditional seismic imaging methods rely on assumptions (e.g., single scattering), which become less reliable in high-impedance-contrast scenarios. Such scenarios are more prevalent as oil production moves into increasingly complex geologic settings. Full-waveform inversion (FWI) offers an alternative by directly inverting for earth properties while simulating complex wave phenomena and handling uneven illumination through iterative processes. Recent advancements in computing power and data acquisition have enabled high-frequency FWI, including elastic FWI, which can produce high-resolution velocity models for structural interpretation. However, full quantitative interpretation requires multiparameter FWI, which remains challenging due to the crosstalk between different parameters. We present a multiparameter viscoelastic FWI workflow that attempts to mitigate crosstalk by separating the problem into smaller pieces, focusing on different properties with specific portions of the data as well as better-suited cost functions. The workflow first improves the P-wave background velocity and attenuation models, then it updates acoustic and shear impedance using reflection data amplitude. Application to a field data set from the Gulf of Mexico, characterized by complex salt geometry with variable thickness, shows enhanced subsalt structural imaging and sensible quantitative information, with a reasonable match to well logs and consistent amplitude variation with angle behavior compared to least-squares reverse time migration.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".