Multisource Time-Lapse Elastic Full-Waveform Inversion Using a Target-Oriented Common-Model Strategy
Bibliographic record
Abstract
Full-waveform inversion (FWI) is a powerful tool for time-lapse seismic analysis, enabling high-resolution imaging of subsurface physical properties to monitor reservoir changes during injection, production, and long-term CO2 storage. However, conventional time-lapse FWI, which relies on a parallel inversion strategy, suffers from significant artifacts due to survey non-repeatability, disrupting convergence consistency between baseline and monitor inversions. Additionally, the high computational cost remains a major challenge. To address these limitations, we propose a novel time-lapse FWI strategy—the target-oriented (TO) common-model strategy (CMS)—which strategically integrates multiple approaches. Our method combines TO FWI, which enhances model convergence in the target region to improve time-lapse accuracy, with CMS, which reduces artifacts by using an optimized starting model to guide baseline and monitor inversions toward similar convergence paths. Additionally, we employ an amplitude-encoding multi-source strategy, significantly reducing computational costs without compromising inversion accuracy. Through extensive elastic tests, we validate the robustness and effectiveness of TO CMS, demonstrating superior performance over both the conventional parallel strategy and standard CMS across various challenging scenarios—including non-repeated source positions, random noise, seawater velocity variations, and biased initial models. Notably, strong noise and seawater velocity variations can significantly impact time-lapse FWI results, highlighting the need for further investigation. Ensuring consistent multi-source parameters in time-lapse FWI can help minimize artifacts.
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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 | 0.001 |
| 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) | 0.003 | 0.001 |
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".