Incorporating estimates of data covariance in elastic FWI to combat random and correlated noise
Bibliographic record
Abstract
As field data applications of FWI increase, dealing with both random and coherent noise in seismic data, and the artifacts they create in FWI models, becomes increasingly important; noise suppression or estimation is also increasingly important when we transition to elastic multiparameter inversion from acoustic approximations. In this study, we analyzed the influence of random and correlated noise on the estimation of model parameter V p, Vs, and density, and, to mitigate their influence, we adopted a two-stage inversion approach, whose second stage involves a modified FWI misfit. The data covariance matrix is calculated from data residuals obtained from an initial run of FWI, and this is incorporated into the misfit function for a second run. With the elastic FWI conducted in the frequency domain, and the data covariance matrix consequently calculated frequency by frequency, the approach, though not computationally inexpensive, places reasonable demands on memory and storage. Random and correlated noise were examined and estimated, and inversion results were compared with those of otherwise identical conventional FWI runs. The bootstrap approach to inclusion of data covariance estimates in FWI appears to be stable, and to have a strong positive impact especially for correlated data noise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".