Time-lapse 4D full-waveform inversion for ocean-bottom cable seismic data with seawater velocity changes
Bibliographic record
Abstract
ABSTRACT Time-lapse 4D full-waveform inversion (FWI) is a valuable technology for high-resolution imaging of reservoir changes caused by hydrocarbon production and CO2 storage. However, it still faces challenges in dealing with nonrepeatability issues due to changes in seawater or near-surface velocity between baseline and monitor surveys. Despite recent advances, the ability of 4D FWI to address this problem has rarely been demonstrated. We investigate the effectiveness of current 4D FWI strategies, such as the parallel, double-difference, sequential, and common-model strategies (CMS), in resolving nonrepeatability issues for 4D ocean-bottom cable (OBC) seismic data. In addition, a three-stage 4D FWI strategy is developed for 4D OBC seismic data, involving the estimation of seawater velocities in the baseline and monitor models, obtaining a good common starting model, and a final convergence to obtain subsurface 4D changes. The synthetic data tests conducted with varying levels of seawater velocity changes indicate that among the investigated strategies, the CMS performs the best. However, the proposed three-stage strategy surpasses it, emphasizing the importance of accurately estimating seawater velocities for both baseline and monitor inversions in 4D FWI of OBC data.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".